BEHAVIORAL MECHANISMS OF Δ9-THC IN NONHUMAN PRIMATES
BY
WILLIAM JOHN
A Dissertation Submitted to the Graduate Faculty of
WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES
In Partial Fulfillment of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
Integrative Physiology and Pharmacology
December, 2016
Winston-Salem, North Carolina
Approved By:
Michael A. Nader, Ph.D., Advisor
Thomas J. Martin, Ph.D., Chair
Paul W. Czoty, Ph.D.
Linda J. Porrino, Ph.D.
Jenny L. Wiley, Ph.D.
ii
ACKNOWLEDGEMENTS
First and foremost, I would like to extend my gratitude to my graduate advisor,
Dr. Nader. I could not have asked for a better graduate research experience within your
laboratory thanks to your constant support and guidance. You have taught me to
approach science from a perspective that will influence the rest of my career and has
changed the way I see the world. I am also grateful for my dissertation committee
members, Drs. Martin, Czoty, Porrino, and Wiley for all their time, support, and
constructive criticism. Thank you to Sue Nader and all other current and past members
of the Nader lab, each one of you has been instrumental in completing my graduate
research. I wish to thank my parents for their unconditioned love and support and for
always sacrificing so much of your time and energy to make sure I always had the best
opportunity to succeed. I would like to express my deepest love and appreciation to my
wife, Joanna, for her support during my graduate education. And finally, to my son,
Oliver- you are gift of happiness, love, and hope. Thank you for enriching our lives so
much during your six months on this earth and know that you are deeply cherished.
This research was supported by an Individual Predoctoral National Research
Service Award (F31 DA041825) and grants R37 DA10584, P50 DA06634 from the
National Institute on Drug Abuse and a Predoctoral Training Fellowship (T32 AA-
007565) from the National Institute on Alcohol Abuse and Alcoholism.
iii
TABLE OF CONTENTS
PAGE
LIST OF ABBREVIATIONS ................................................................................................ iv LIST OF TABLES................................................................................................................ vi LIST OF FIGURES ............................................................................................................ vii ABSTRACT ......................................................................................................................... ix CHAPTER I INTRODUCTION..........................................................................................1
II DETERMINANTS OF CANNABINOID SELF-ADMINISTRATION IN NONHUMAN PRIMATES ..........................................................................61
Submitted for publication to Neuropsychopharmacology, August, 2016
III CHRONIC Δ9-THC IN RHESUS MONKEYS: EFFECTS ON COGNITIVE
PERFORMANCE AND DOPAMINE D2/D3 AVAILABILITY.......................93 Submitted for publication to Biological Psychiatry, August 2016
IV DISCUSSION ...........................................................................................132 SCHOLASTIC VITAE.......................................................................................................202
iv
LIST OF ABBREVIATIONS
µg microgram ANOVA analysis of variance CANTAB Cambridge Neuropsychological Test Automated Battery CBR cannabinoid receptor Cd caudate CD compound discrimination CDC Center for Disease Control CDR reversal of compound discrimination cm centimeter CPP conditioned place preference CS conditioned stimulus d days D2 dopamine D2-like receptor superfamily D2 dopamine D2 receptor subtype D3 dopamine D3 receptor subtype DA dopamine DEA Drug Enforcement Agency DMS delayed-match-to-sample DSM Diagnostic and Statistical Manual of Mental Disorders DVR distribution volume ratio ED extradimensional shift ED50 interpolated effective dose that engenders 50% response FI fixed-interval FR fixed-ratio FT fixed-time g gram h hours HCl hydrochloride icv intracerebroventricular inf infusion ID intradimensional shift IRP Intramural Research Program i.v. intravenous k.g. kilogram LSD least significant difference m meters mm millimeter methA methamphetamine mg milligram min minute(s) ml milliliter MR magnetic resonance NIDA National Institute on Drug Abuse NHP nonhuman primate PCP phencycidine PET positron emission topography PFC prefrontal cortex
v
Pt putamen PT pretreatment R response ROI region of interest s second(s) S+ stimulus signaling reinforcement S- stimulus signaling trial termination, lack of reinforcement SC consequent stimulus SD discriminative stimulus SA self-administration SAMHSA Substance Abuse and Mental Health Services Administration s.c. subcutaneous SC consequence stimulus SD standard deviation SR reinforcer SEM standard error of the mean THC Δ9-tetrahydrocannabinol TO timeout US United States WCST Wisconsin Card Sort Test WM Working memory
vi
LIST OF TABLES
PAGE
CHAPTER I Table I. Cannabinoid self-administration studies in experimental animals ............13 CHAPTER II Table I. Ages, weights, and self-administration histories for individual rhesus
monkeys .....................................................................................................66 Table 2. Relationship between CP55,940 potency to decrease food-maintained
responding and CP55,940 self-administration ..........................................73 CHAPTER III Table I. Individual parameters used throughout study that produced delay-
dependent effects on DMS performance.................................................104 Table II. Baseline percent accuracy (± SD) at each delay for individual monkeys
responding under the DMS task ..............................................................111 Table III. Individual and mean (±SEM) [11C]-raclopride DVRs in control and THC
treated monkeys.......................................................................................116
vii
LIST OF FIGURES
PAGE
CHAPTER I Figure 1. Dose comparisons for preclinical THC self-administration studies in
nonhuman primates ...................................................................................19 CHAPTER II Figure 1. Effects of CP 55,940 and THC on food maintained responding and body
temperature in individual rhesus monkeys ................................................72 Figure 2. Individual self-administration dose-response curves for CP 55,940, THC,
and THC before after the development of tolerance to rate-decreasing effects on food-maintained responding under an FR 10 schedule of reinforcement .............................................................................................74
Figure 3. Acquisition of CP 55,940 self-administration.............................................75 Figure 4. Transient reinforcing effects of THC in individual rhesus monkeys ..........76 Figure 5. THC functioned as a reinforcer in three monkeys after daily THC
treatment ....................................................................................................78 Figure 6. THC self-administration under a [FI 10-min (FR 30:S)] second-order
schedule of reinforcement .........................................................................80 CHAPTER III Figure 1. Experimental timeline...............................................................................102 Figure 2. Effects of acute THC on food-maintained responding and body
temperature before and during chronic THC (1.0 mg/kg, s.c.) treatment (A) and mean (±SD) food-reinforced response rates for sessions before (BL, baseline) during, and after chronic THC treatment for individual monkeys (B) .............................................................................................109
Figure 3. Effects of acute THC on cognitive performance before and during chronic
THC 1.0 mg/kg, s.c.) treatment ...............................................................113 Figure 4. Residual effects (i.e., 22 hrs after administration) of chronic THC
treatment on delayed match-to-sample performance (A) and discrimination and reversal learning and attentional set-shifting performance (B) .......................................................................................115
viii
Figure 5. Effects of chronic THC treatment on dopamine D2/D3 receptor availability as measured with [11C]-raclopride ...........................................................117
CHAPTER IV Figure 1. Hypothetical dose-response curve under a concurrent THC-food schedule
of reinforcement .......................................................................................145 Figure 2. Effects of THC (A) and CP 55,940 (B) pretreatment on cocaine-drug
choice in rhesus monkeys .......................................................................161 Figure 3. Rate dependency of THC’s potency to decrease rates of food-maintained
responding ...............................................................................................178
ix
ABSTRACT
John, William S.
BEHAVIORAL MECHANISMS OF Δ9-THC IN NONHUMAN PRIMATES
Dissertation under the direction of Michael A. Nader, Ph.D., Professor of Physiology and
Pharmacology
A comprehensive understanding of the reinforcing effects and cognitive
consequences of Δ9-tetrahydrocannabinol (THC), the primary psychoactive constituent
in marijuana, is critical for developing treatments in cannabis dependent patients.
Despite the fact that marijuana is the most commonly abused illicit substance in the
U.S., self-administration (SA) of THC has only been established in one laboratory using
New World primates (squirrel monkeys). In Chapter 2, THC and the synthetic
cannabinoid agonist CP55,940 were made available for SA in Old World monkeys, a
species more closely related to humans, by using similar parameters deemed successful
in squirrel monkeys. CP55,940 functioned as a reinforcer in a subset (3 of 8) of rhesus
monkeys, with an inverse relationship between magnitude of SA and sensitivity to rate-
decreasing effects. On the other hand, THC did not function as a reinforcer in any
monkey. To test the hypothesis that attenuating the rate-decreasing effects of THC
would increase the likelihood THC would function as a reinforcer, monkeys were
administered THC daily until tolerance developed. When THC SA was reexamined, it
functioned as a reinforcer in a subset (3 of 8) of subjects. Further, the reinforcing effects
of THC were noted in two of four cynomolgus monkeys responding under a second-
order schedule of reinforcement in which behavior was maintained by conditioned
stimuli. These studies indicate that sensitivity to rate-decreasing effects and schedule of
reinforcement are important determinants for cannabinoid-maintained behavior.
x
In Chapter 3, monkeys were chronically treated with THC and the residual effects
(22 hrs after THC administration) were assessed on cognitive performance. Impairments
were found in working memory (WM) but not in other aspects of executive function.
These cognitive deficits were independent of changes in dopamine D2/3 receptor
availability as measured with PET and [11C]-raclopride. THC-induced impairments to WM
recovered during abstinence. Moreover, chronic THC treatment resulted in tolerance to
the acute impairments in compound discrimination learning but only to impairments in
working memory when the cognitive demand was low.
Overall, these studies provide insight on the behavioral mechanisms underlying
the reinforcing, unconditioned, and cognitive effects of THC, which is important for
understanding the abuse-related properties of cannabis. Such knowledge should aid in
the development of treatments for cannabis use disorder.
1
CHAPTER I
INTRODUCTION
Drug abuse is a major public health problem worldwide, with an estimated 21.6
million individuals that meet DSM IV criteria for substance dependence or abuse in the
United States alone (SAMSHA, 2013). Drug abuse is a chronic relapsing brain disease
characterized by compulsion to seek and take a drug, loss of control in limiting intake,
and the emergence of negative affect (e.g., dysphoria, anxiety) when access to the drug
is withheld. In addition to the deleterious effects on the individual, drug abuse poses an
enormous economic cost to society including costs from crime, loss in productivity,
health care, incarceration, and drug enforcement operation costs. Estimates indicate that
overall, these costs exceed $600 billion annually in the United States (National Drug
Intelligence Center, 2011; Rehm et al., 2009; CDC, 2014).
Marijuana (Cannabis sativa) is the most commonly abused illicit substance in the
United States with more than 4 million Americans that meet DSM IV criteria for
marijuana dependence (SAMSHA, 2013). In fact, this number is greater than
dependence on painkillers, cocaine, heroin, and stimulants combined (SAMSHA, 2013).
Furthermore, the second most substance abuse treatment admissions in the U.S. were
for cannabis-related disorders, with alcohol-related disorders being the first (SAMSHA,
2013). The number of marijuana users has been steadily increasing among 12th graders,
which is associated with a decrease in the perceived risks of regular use (Johnston et
al., 2014). Moreover, marijuana is now legal in 4 states, suggesting that use will
increase. The behavioral therapies that have been evaluated as treatments for
marijuana dependence are only modestly effective while there are currently no effective
pharmacotherapies (Nordstrom and Levin, 2007). As a result, there is a compelling need
for more research on the impact of marijuana on neurobiology and behavior in order to
develop more targeted therapeutic strategies for cannabis dependence.
2
Cannabis contains more than 400 different chemicals including
phytocannabinoids, terpenoids and essential oils (ElSohly and Slade, 2005); however,
the behavioral effects of cannabis (i.e. euphoria, relaxation, anxiety, confusion, memory
loss, and paranoia) are primarily the result of the psychoactive ingredient, Δ9-
tetrahydrocannabinol (THC) (Adams and Martin, 1996; Wachtel et al., 2002; Hall and
Degenhardt, 2009). The behavioral effects of THC are largely mediated by its action as a
partial agonist at the G-protein coupled cannabinoid-type 1 (CB1) receptor, which is the
most-abundantly expressed G-protein-coupled receptor in the brain. In particular, CB1
receptors have dense expression in brain regions involved in reward, addiction, and
cognitive function, including the amygdala, cingulate cortex, prefrontal cortex, ventral
pallidum, caudate-putamen, nucleus accumbens, ventral tegmental area, and lateral
hypothalamus (Glass et al., 1997; Wang et al., 2003). The endocannabinoid system also
contains cannabinoid type 2 (CB2) receptors, at which THC is a partial agonist as well.
CB2 receptors are known to mainly be expressed by immune cells; however, recent
evidence suggest CB2 receptors are also found centrally, albeit at much lower levels
than CB1 receptors, and may mediate some of THC’s actions as well (Atwood and
Mackie, 2010). CB1 and CB2 receptors are coupled to similar transduction systems,
primarily through Gi or Go proteins, which directly inhibit the release of GABA, glutamate,
and acetylcholine. Moreover, THC has been demonstrated to stimulate neuronal firing of
dopamine (DA) through the mesolimbic DA system in animals (Tanda et al., 1997) and
humans (Bossong et al., 2009; Voruganti et al., 2001; Bossong et al., 2015; Van de
Giessen et al., 2016), which has been proposed as a contributing factor to its reinforcing
effects.
Moreover, a fundamental principle governing the field of behavioral
pharmacology is the notion that the behavioral action of a drug cannot be confined to the
inherent pharmacological properties of the drug itself. Rather, the behavior-environment
3
contingencies must be considered in determining the effects of a drug in addition to its
pharmacological properties. These conditions include, but are not limited to, the
schedule of reinforcement, the stimulus context, the type and level of the establishing
operation, the type of consequent event, and the behavioral history of the subject. For
instance, the same dose range of nicotine has been shown to maintain or punish
responding that produces its injection or to maintain responding that prevents its
injection (Goldberg et al., 1983). Similarly, cocaine has been shown to simultaneously
maintain responding by scheduled injections of the drug and by termination of the
schedule of cocaine injection (Spealman, 1979). These pioneering behavioral
pharmacological studies demonstrate that drugs affect behavior in many different ways
depending on the prevailing environmental conditions and that these effects cannot be
predicted simply on the basis of their pharmacological nature.
The overarching goal of this dissertation was to apply this concept of behavioral
mechanisms and extend it to an examination of the factors that influence the reinforcing
effects of THC and the specificity and temporal nature of the cognitive consequences of
THC by using a within-subject study design in nonhuman primates. A better
understanding of these behavioral mechanisms of THC will be important for 1)
identifying variables that may render certain individuals more vulnerable to cannabis
abuse, 2) discovering parametric manipulations that can be used to advance the
cannabinoid self-administration methodology in preclinical animal models and 3) for
identifying specific attributes of cannabis dependent patients that may be targeted by
pharmacological and behavioral interventions to improve treatment outcome.
In the context of these aims, the following chapter will present an overview of
previous studies that have examined the effects of THC in 1) preclinical animal models
of abuse liability, 2) human laboratory models of reinforcement, and 3) studies assessing
the neurocognitive effects of cannabis/THC. This overview will serve to identify the
4
recent advances made in these areas of research as well as the current gaps in
knowledge and how that information has shaped the studies within this dissertation.
Drug discrimination
Much of the information we know regarding the abuse-related effects of
cannabinoids comes from preclinical studies using the drug discrimination procedure
(Balster and Prescott, 1992; Wiley, 1999). The drug discrimination procedure is based
on the ability of a drug to induce a specific set of interoceptive stimulus conditions
perceived by animals that might be predictive of the subjective reports of
perceptions/feelings induced by the same drug in humans. As a result, the drug
discrimination procedure does not measure reinforcing effects of drugs; however, there
is usually a good correlation between those drugs belonging to a particular
pharmacological class that serve as reinforcers and those that produce
subjective/discriminative effects characteristic of that pharmacological class (Schuster
and Johanson, 1988). Therefore, studies of the subjective effects of new drugs in both
humans and animals have been relatively good predictors of whether or not a drug will
be abused.
Animal drug discrimination procedures consist of an initial period of training that
consists of alternating the administration of either a specific drug dose or the drug's
vehicle before each session. Typically, sessions take place within an experimental
chamber, which has two different response manipulanda. Animals learn to make a
response on one manipulandum when sessions are preceded by injection of the training
drug and make a response on the other manipulandum during sessions preceded by a
vehicle injection. In order to maintain this behavior, responding is intermittently
reinforced by food pellets or by postponement or escape from electric shocks. Once
subjects reach a criterion level of correct performance (e.g. 80% drug appropriate
5
responding), test sessions are occasionally scheduled, during which they receive either
the training drug at a different dose or a different drug, to test whether or not such a
drug/dose will produce a discriminative stimulus effect similar to the training drug.
Generally, if the training drug is an agonist, only agonist drugs belonging to the same
pharmacological class will produce full substitution to the training stimulus and only
antagonists belonging to the same pharmacological class will selectively block the
training stimulus.
Cannabinoid drugs, indeed, show a pharmacological specificity in this behavioral
procedure. For example, in animals trained to discriminate injections of THC from
injections of its vehicle, only drugs that selectively activate CB1 receptors fully substitute
for the THC training stimulus (Wiley et al. 1993a, 1993b, 1995c; Barret et al. 1995).
Furthermore, CB1 receptor agonists have been shown to substitute for the THC training
stimulus with a potency that is consistent with their in vitro affinity for the CB1 receptors
(Balster and Prescott 1992; Gold et al. 1992; Wiley et al. 1995b).
In addition to THC, other cannabinoid CB1 receptor agonists have also been
used as the training stimulus (Wiley et al. 1995a, 1995b; Perio et al. 1996; Järbe et al.
2001). For example, Wiley et al. (1995b) trained rats to discriminate the synthetic CB1
receptor agonist CP 55,940 from saline and found that THC, the synthetic CB1 receptor
agonist WIN 55,212-2, and cannabinol all substituted for the CP 55,940 training stimulus
and did so at potencies similar to those that displace CP 55,940 in binding studies.
Similarly, other investigators have trained rats to discriminate WIN 55,212-2 from vehicle
(Perio et al. 1996), and found that WIN 55,212-2 completely generalized to THC and CP
55,940. Moreover, the discriminative stimulus effects of THC and other synthetic CB1
agonists have been shown to be antagonized by the selective CB1 receptor antagonist
SR 141716A, further demonstrating that the cannabinoid discrimination is mediated by
CB1 receptors (Mansbach et al. 1996; Perio et al. 1996). It has been demonstrated that
6
these discriminative stimulus effects are centrally mediated behaviors through studies
showing that a selective CB1 receptor antagonist that does not cross the blood-brain
barrier, SR 140098, does not antagonize the discriminative stimulus effects of
cannabinoids (Perio et al. 1996).
Conditioned place preference/aversion
Another animal model of abuse liability that has provided much information on
the behavioral effects of cannabinoids is the conditioned place preference (CPP)
procedure. This procedure is based on the principles of classical conditioning and
provides an indication of drug-related reward/aversion effects in animals. The
rewarding/aversive stimulus properties of a drug assessed under these procedures refer
to the appetitive nature of the stimulus as opposed to the ability of a drug to increase the
probability of a given behavior (i.e. reinforcing effects). Although methodological details
differ among laboratories, CPP procedures typically begin by allowing animals to freely
explore two distinct environmental contexts within a chamber, which differ in color, size,
or texture, and are connected by an open door. The time spent in each context is then
recorded. During conditioning sessions, a drug injection (unconditioned stimulus) is
repeatedly paired with one of the two environmental contexts while access to the other
context is prohibited. On alternate sessions, vehicle injections are paired with the other
environmental context. On the final test day, no injections are given and the relative time
spent in each environmental context is measured and the difference is taken in order to
provide a measure of preference. Using this dependent variable, the CPP model has
been shown to have high predictive validity in that most drugs abused by humans are
able to increase the time spent in the drug-paired context (i.e., produce a place
preference); however, many interpretations of results are limited due to the frequency of
false positives and the difficulties encountered in obtaining systematic dose-response
7
relationships over a limited range of doses (Bardo and Bevins 2000; Tzschentke 1998).
CPP studies with cannabinoids have been largely ineffective at producing
positive place preferences and the studies that have shown cannabinoid place
preference have done so at various doses and magnitudes depending on the training
procedure used across studies. Although very specific dosing parameters are required to
produce place preferences for many drugs in the CPP paradigm (Bardo and Bevins
2000; Tzschentke 1998, the ability of cannabinoids, such as THC and the synthetic CB1
receptor high-efficacy agonist CP 55,940, to produce a place preference in rats and mice
are particularly sensitive to the timing of injections as well as on the range of doses used
(Lepore et al. 1995; Valjent and 2000; Braida et al. 2001; Ghozland et al. 2002). For
instance, Lepore et al. (1995) demonstrated that when a standard schedule of daily
training injections was used (i.e., vehicle, drug, vehicle, drug, etc.), a relatively low dose
of THC, 1.0 mg/kg, produced a conditioned place aversion, however, higher doses of 2.0
and 4.0 mg/kg THC produced positive place preferences. When the schedule of daily
injections was changed and a day where no drug or vehicle injection was administered
was added in between test sessions (i.e., vehicle, day off, drug, day off, vehicle, day off,
drug, etc.), the results were opposite such that THC produced a conditioned place
preference at 1.0 mg/kg but place aversions were found at the 2.0 and 4.0 mg/kg doses
of THC. These findings indicated that the rewarding effects of THC as assessed under
the CPP paradigm might be qualitatively changed by increasing the interval between
drug injections due to what the authors referred to as a “postdrug dysphoric rebound.”
As a result, the pharmacological properties of THC that may be contributing to this effect
may help to explain why THC and synthetic cannabinoids often produce conditioned
place aversions, rather than place preferences, in rats and mice using similar dose
ranges and standard place-preference conditioning procedures (Lepore et al. 1995;
McGregor et al. 1996; Sanudo-Pena et al. 1997; Mallet and Beninger 1998; Chaperon et
8
al. 1998; Cheer et al. 2000; Zimmer et al. 2001).
A study by Valjent and Maldonado (2000) also found positive place preferences
with THC by using another variation in the procedure. These investigators found that
when using a standard place-conditioning protocol in mice, a relatively low dose of THC
(i.e., 1.0 mg/kg) produced neither place aversion nor preference while a high dose (i.e.,
5.0 mg/kg) produced place aversion. On the other hand, when mice received an
additional exposure to THC before the conditioning test such that a 1.0 mg/kg injection
was administered in their home cage 24 h before the start of the experiment and were
exposed to the drug-paired conditioning chamber for a longer period of time (45 min
instead of 15–30 min), the lower dose of 1.0 mg/kg THC produced a place preference
while the higher dose of 5.0 mg/kg produced neither place preference nor aversion. This
finding has also been replicated by a separate group of investigators (Ghozland et al.
2002). Overall, these results suggested that prior exposure to the drug may facilitate the
development of positive place preferences, which may be otherwise overridden by
certain aversive effects of THC that are apparent upon initial exposure. These findings
may be particularly informative for cannabinoid self-administration experiments because
they imply that certain parametric manipulations may need to be designed into studies in
order to account for the effects of THC may inhibit the expression of its reinforcing
effects.
Drug self-administration
The drug self-administration procedure is the primary methodology for
demonstrating the reinforcing effects of drugs that are abused by humans. The
procedure is built on principles of operant conditioning such that laboratory animals are
trained to respond on a manipulandum to receive an injection of a drug. Like any other
operant behavior, the drug self-administration procedure can be broken down into
9
specific components using Skinner’s 3-term contingency (Skinner, 1938), which is
described as follows:
SD R SC,
where SD designates the discriminative stimulus, R designates an operant response, and
SC designates a consequent stimulus. The arrows specify the contingency (i.e.
performance of the response during the SD) that will result in delivery of the consequent
stimulus SC. Thus, a typical drug self-administration experiment would involve the
illumination of a stimulus light inside an operant chamber (the SD), signaling to an animal
that depression of a response lever (R) will result in delivery of a drug injection via an
indwelling intravenous catheter (SC). Consequent injections of a drug that increase the
probability of responding leading to their delivery are operationally defined as
reinforcers, while the discriminative stimulus, responses, and consequent stimuli are
defined by the schedule of reinforcement.
The first demonstrations of drug self-administration came in the 1960s where rats
and monkeys previously made physically dependent on morphine were trained to press
a lever when those responses produced intravenous injections of morphine (Weeks,
1962; Thompson and Schuster 1964). Shortly following, the reinforcing effects of opioids
in subjects that had not previously been made dependent prior to the start of the self-
administration studies were demonstrated (Yanagita et al., 1965a, b; Deneau et al.,
1969). Since then, the reinforcing effects of practically every drug abused by humans
have been demonstrated in experimental animals by the self-administration procedure
under a variety of routes, schedules of reinforcement, and species. These studies have
been paramount in advancing our understanding of drug abuse on both a conceptual
and scientific level. For example, prior to the demonstrations that laboratory animals will
self-administer a variety of drugs, drug abuse was widely considered to be a result of an
“addictive personality” or weakness in self-control. Moreover, self-administration studies
10
have shed light on many of the pharmacological, environmental, and biological
determinants of drug-taking behavior as a model of addiction. However, the one drug
class that has remained a “false negative” in the self-administration procedure has been
the cannabinoids including THC. Successful efforts have been limited to one laboratory
using squirrel monkeys and a unique set of parameters that have not been previously
employed (Tanda et al., 2000; Justinova et al., 2003). Every other THC self-
administration attempt to date using Old World nonhuman primate species (rhesus
monkeys) or rodents has been unsuccessful (Kaymakcalan 1972, 1973; Pickens et al.,
1973; Harris et al., 1974; Leite and Carlini 1974; Carney et al., 1977; Van Ree et al.,
1978; Mansbach et al., 1994; Li et al., 2012; Lefever et al., 2014). Therefore, the lack of
a widespread, suitable method to establish robust levels of THC SA in animals remains
an impediment for directly assessing the abuse-liability of novel cannabinoid compounds
and for developing treatments for human marijuana abuse. Thus, the first aim of this
dissertation was to establish the experimental conditions necessary for THC SA to be
maintained by Old World monkeys, a species more closely related to humans than the
New World squirrel monkeys. Previous studies on the reinforcing effects of cannabinoids
in human subjects and preclinical animals will be presented. This will be followed by a
discussion on potential factors that may be limiting the expression of cannabinoid
reinforcement in animal models and how the studies within this dissertation sought to
address those factors in order to demonstrate the reinforcing effects of THC.
THC self-administration in humans
Several studies in humans have provided experimental data supporting THC's
integral role in the reinforcing effects of smoked marijuana. For instance, when subjects
are given a choice between THC-containing cigarettes and a non-drug reinforcer (e.g.,
snack food) or between marijuana cigarettes with different THC contents, the THC
11
cigarettes are consistently chosen over non-drug reinforcers and cigarettes with a
greater THC content have been shown to be consistently preferred to cigarettes with a
lower THC content, even when the choice between the two was not mutually exclusive
(Haney et al., 1997, Kelly et al., 1997 and Ward et al., 1997). Other studies in humans
have also attempted to better define THC's role as the primary psychoactive ingredient
in marijuana by investigating whether the its reinforcing effects are dose-dependent. In
order to do this, the THC content in a cigarette is systematically manipulated during self-
administration sessions and the behavioral endpoints associated with reinforcement are
compared to placebo self-administration or self-administration of a cigarette containing a
low amount of THC. For example, Herning et al. (1986) investigated whether marijuana
smoking behavior was dose-dependent in experienced users by examining whether the
subjects would titrate their intake according to differing THC concentrations in order to
achieve a defined subjective state. It was observed that when cigarettes containing a
high concentration of THC (3.9% THC) was available, the subjects took significantly
more puffs with longer intervals between puffs compared to when cigarettes containing
low amounts of THC (1.2% THC) were available. It was also found that the subjects
inhaled substantially larger (46%) volumes of air when self-administering the high THC-
content cigarettes. Thus, it was concluded that perhaps the subjects were demonstrating
different smoking-related behavior as a mechanism to titrate the THC dose, thus
indicating a dose-related effect. Another study by Nemeth-Coslett et al. (1986) also
demonstrated compensatory changes in marijuana smoking as a function of THC dose
where expired air carbon monoxide (CO) levels following smoking were inversely related
to THC content of the marijuana (1.29%, 2.84% or 4.0% THC). Thus, it was suggested
that subjects reduced their smoke intake as THC content increased thereby providing
evidence for dose-dependency in THC’s reinforcing effects.
12
Taken together these studies demonstrate that, aside from epidemiological
evidence, THC produces clear and mostly dose-related reinforcing effects in controlled
experimental studies using human subjects. What is not clear, however, is that despite
these demonstrations in humans, every attempt in experimental animals with the
exception of one laboratory has failed to demonstrate the ability of THC to function as a
reinforcer. Going forward, it will be important to “reverse translate” these findings from
humans to animals in order to optimize the cannabinoid self-administration model. For
instance, given the limited dose range that smoking titration occurs in humans, careful
consideration of the unit dose made available for self-administration as well as the
pharmacokinetic factors of cannabinoids will be vital for optimizing the cannabinoid self-
administration model in experimental animals.
Cannabinoid self-administration in experimental animals
Numerous attempts have been made over the past 3 decades to demonstrate
persistent, dose-related reinforcing effects of THC in experimental animals; however,
successful efforts have been limited to one laboratory using squirrel monkeys and a set
of unique experimental parameters (see Table 1). Therefore, the majority THC self-
administration studies in animal models are incongruent to the numerous laboratory
demonstrations of the reinforcing effects of THC in human subjects. In early preclinical
studies, many procedural factors were taken into consideration including route of
administration (e.g., inhalation, oral, intravenous), previous drug history, and schedule of
reinforcement, however, none of these studies showed that THC could maintain rates of
responding above vehicle levels. For example, in one early study, Harris et al. (1974)
failed to demonstrate reinforcing effects of THC (0.025-0.3 mg/kg) under an FR1
schedule (12 h access per day, 20 days per dose) in drug-naïve rhesus monkeys or
monkeys that had histories of cocaine, phenobarbital, PCP, ethanol, or amphetamine
13
self-administration. THC also failed to function as a reinforcer in this study following 30
days of programmed infusions of THC (2.0 mg/kg/day) or in monkeys that had been
trained to self-administer a cocaine-THC mixture. In another early study using rhesus
monkeys trained to self-administer PCP, THC was substituted and maintained low rates
of behavior but did not persist above vehicle levels over repeated sessions (Pickens,
1973). Kaymakcalan (1973) provided the most positive demonstration of THC
reinforcement in rhesus monkeys where two of six monkeys acquired THC self-
administration under an FR1 schedule of reinforcement, but only during withdrawal
following the development of physical dependence via 36 days of forced automatic THC
Table 1. Cannabinoid self-administration studies in experimental animals.
Reference Dose and vehicle of drug
Species Procedure/schedule Main outcome
THC
Deneau and Kaymakcalan
(1971), Harris et al., (1974)
100–400 μg/kg Tween 20/saline
or 25–300 μg/kg PVP/saline
Rhesus monkeys
IVSA, FR1, drug naïve
Negative
Pickens et al. (1973)
0.025-0.1 mg/kg PVP/saline
Rhesus monkeys
IVSA, FR1, substitution for
phencyclidine
Negative
Pickens et al. (1973)
0.025-0.1 mg/kg and 25 mg burned hashish
Rhesus monkeys
Inhalation and food presentation/FR3 and 4 s mouth contact to
smoke tube, 24 h per day, 4 days each week
Negative
Carney et al.
(1977), Harris et al. (1974), Kaymakcalan
(1973)
3–300 μg/kg
EL620; 25–200 μg/kg PVP/saline; 100–
400 μg/kg Tween 20/saline
IVSA, substitution for
cocaine
Negative
Kaymakcalan (1973)
100–400 μg/kg Tween 20/saline
Rhesus monkeys
IVSA, monkeys made dependent on THC
33% positive
Mansbach et al.
(1994)
17–100 μg/kg in
EL620/EtOH/saline
Rhesus
monkeys
IVSA, FI 10 min (2 hr
TO), substitution for phencyclidine
Negative
Li et al. (2012) 3.2–32 μg/kg EL620/EtOH/sali
ne
Rhesus monkeys
IVSA, history of heroin SA, or naive
Negative
Corcoran and Amit 0.25 mg Rats Forced drinking Negative
14
(1974) (hashish)/ml
Van Ree et al. (1978)
7.5–300 μg/kg Tween 20/saline
Rats IVSA, naïve 40 % positive
Takahashi and Singer (1979, 1980)
6.25–50 μg/kg Tween 80/saline
Rats IVSA, naïve, food deprived, with
automatic food pellet delivery
Positive only with
deprivation conditions
Braida et al. (2004) 0.01–1 μg/inf cerebrospinal
fluid, EtOH, cremophor
Rats ICVSA, trained with water
Positive, max responding at
0.02 μg/inf
Lefever et al., (2014)
3-10 μg/kg in polysorbate
80/saline
Long0Evans Rats
IVSA, FR 3, substitution for WIN
55,212-2
Negative
Tanda et al. (2000), Justinova et al. (2008), Justinova et
al. (2003)
1–16 μg/kg EtOH/Tween 80/saline
Squirrel monkeys
IVSA, naïve, or cocaine trained with saline extinction/FR
or second order
Positive, max responding at 4 μg/kg
2-AG
De Luca et al. (2014)
12.5–50 μg/kg Tween 80/saline/eth
Sprague-Dawley rats
IVSA, naïve 90 % positive, max responding at
25 μg/kg
Justinova et al. (2011)
0.1–100 μg/kg EtOH/Tween 80/saline
Squirrel monkeys
IVSA, nicotine substitution or history IVSA of anandamide,
anandamide reuptake inhibitors or nicotine
100 % Positive, max responding at
3 μg/kg
CP 55,940
Mansbach et al. (1994)
0.3–3 μg/kg emulphor/EtOH/s
aline
Rhesus monkey
IVSA, FI 10 min (2 hr TO), PCP substitution
Negative
Braida et al. (2001a, b)
0.1–1.6 μg cerebrospinal fluid, EtOH,
cremophor
Wistar rats IVSA Positive, max responding at 0.4 μg/kg
WIN 55,212-2
Martellotta et al. (1998)
10–500 μg/kg CD1 mice IVSA, naïve Positive, max responding at 100 μg/kg
Fattore et al. (2001) 6.25–50 μg/kg
Tween 80/heparin/saline
Wistar rats IVSA, naïve 87 %
positive, max responding at 12.5 μg/kg
Solinas et al.
(2007a, b)
12.5–25 μg/kg
Tween 80/saline
Long-Evans
rats
Positive, max
responding at 12.5 μg/kg
Justinova et al. (2013)
12.5 μg/kg Tween
80/heparin/saline
Long-Evans rats/Sprague-
Dawley
IVSA, naïve Positive
Lecca et al. (2006) 12.5 μg/kg Tween 80/saline
Sprague-Dawley rats
IVSA, naïve Positive
Lefever et al., (2014)
5.6-30 μg/kg in polysorbate
80/saline
Rats IVSA, FR 3, naïve Positive
15
JWH 018
De Luca et al. (2015)
10–20 μg/kg EtOH/Tween 80/saline
Sprague-Dawley rats
IVSA, naïve 90 % positive, max responding at
20 μg/kg
De Luca et al. (2015)
15–30 μg/kg EtOH/Tween 80/saline
C57BL/6 mice
IVSA, naïve 90 % positive, at 30 μg/kg
Injections (0.4 mg/kg/injection, i.v.). Surprisingly, only two other studies in rhesus
monkeys involving cannabinoid self-administration have been published since the
1970s. One of these by Mansbach et al. (1994) used a schedule of reinforcement
designed to compensate for the biodispositional factors of THC (i.e. slow-onset of
behavioral effects and extended elimination phase) that may limit the expression of
reinforcing effects. Here, individual drug injections were spaced by 2-hour intervals and
paired with a distinct conditioned stimulus; however, THC still did not maintain rates of
responding above vehicle levels although this factor of temporal contiguity was taken
into consideration. The other study, Li et al., 2012, substituted THC for heroin SA under
an FR 50, 180-s timeout schedule of reinforcement in male rhesus monkeys. Although
some evidence indicates a shared mechanism between cannabinoid and opioid receptor
compounds, these conditions were also unsuccessful in demonstrating the reinforcing
effects of THC.
Studies in rodents have also been largely unsuccessful in demonstrating the
reinforcing effects of THC by the self-administration paradigm. One exception was a
study by Takahashi and Singer (1979) where 6.25 and 12.5 μg/kg/injection THC
maintained responding above vehicle in food-restricted rats at 80% of normal body
weight and by using a concurrent fixed-time (FT) 1-min schedule of food-delivery and
FR1 schedule THC self-administration. THC self-administration above vehicle levels was
not obtained in rats that were only food-restricted (without the FT 1-min schedule of food
16
delivery) or in rats with the FT1-min schedule at free-feeding body weight. Therefore,
these results indicated a role of adjunctive behavior, that is the environmental
contingencies introduced by a combination of the FT 1-min schedule and food
deprivation were the critical variables in demonstrating THC reinforcing effects in this
study. On the other hand, several studies in rodents have demonstrated reinforcing
effects of synthetic CB receptor agonists by intravenous self-administration. The first
report by Martellotta et al. (1998) showed reinforcing effects of the synthetic CB1 receptor
agonist WIN 55,212-2 in drug-naïve mice. In that study, self-administration was only
studied during one experimental session in which WIN 55,212-2 was made available
under an FR 1 schedule for one mouse while another served as a yoked control. The
ratio between the number of responses by the active-lever mice and yoked controls
indicated that WIN 55,212-2 reinforcing effects were obtained in a dose-related manner.
Despite the fact that “reinforcing effects” were determined by using a grouped
experimental design, these findings were replicated by another laboratory using the
same procedure (Ledent et al., 1999).
The early studies of synthetic cannabinoid self-administration in rodents by
Martellotta et al. (1998) and Ledent et al. (1999) were important advancements in
cannabinoid self-administration methodology; however, these studies also contained
several caveats that limited the translational nature of the procedure, such as studying
each dose during a single self-administration session and the use of a grouped design to
measure reinforcing effects. Since then, other studies have addressed these issues by
showing that synthetic cannabinoids can function as reinforcers within-subject and over
consecutive experimental sessions. For example, Fattore et al. (2001) showed that
stable and robust rates of i.v. WIN 55,212-2 self-administration was maintained in rats
responding under an FR 1 schedule (10-s timeout) in 3-h sessions. Stable responding
was acquired in approximately 16 sessions at a dose of 12.5 μg/kg/injection where
17
maximal rates of responding occurred at approximately 25 injections per session. It was
also shown that WIN 55,212-2 self-administration was subject to saline extinction and
antagonism by the CB1 antagonist/inverse agonist SR 141716A. It is also important to
note that rats were maintained at 80% of free-feeding weights in these studies.
Furthermore, reinforcing effects of the bicyclic cannabinoid analog, CP 55,940 have
been demonstrated in rodents via intracerebroventricular (ICV) self-administration
(Braida et al., 2001). This study also used a form of diet restriction as an antecedent
variable in which the animals only had access to water during the experimental session.
During each session, two levers were made available and water delivery was contingent
on responding on either lever. In addition to response contingent water delivery,
responding on one lever also delivered a paired ICV injection of CP 55,940 while
responding on the other delivered an ICV injection of vehicle paired with water delivery.
The lever producing water delivery paired with ICV CP 55,940 dose-dependently
maintained greater rates of responding than the lever paired with vehicle. One limitation
to these studies, however, is that diet restriction was required for the acquisition and
maintenance of self-administration. Food restriction is a well-established procedural
factor in drug self-administration studies that has been shown to enhance both the
acquisition (Carroll et al., 1979; de la Garza and Johanson, 1987; Cabeza de Vaca and
Carr, 1998) and maintenance (Carroll and Meisch, 1984) of drug-maintained behavior.
More recently, Lefever et al. (2014) also demonstrated the reinforcing effects of WIN
55,212-2 in rodents responding under an FR 3 schedule of reinforcement and
maintained at 90% free-feeding weight, as opposed to 80% in the initial successful
demonstration of WIN 55,212-2 self-administration by Fattore et al. (2001). However,
self-administration was not consistent among all subjects in this study with considerable
within-group variability in the number of infusions earned per session.
18
The initial demonstration of robust THC-maintained responding in experimental
animals by Tanda et al. (2000) was achieved by using a set of experimental parameters
not previously employed. First, squirrel monkeys (Saimiri sciureus), a species of New-
World primates, served as subjects. In contrast, previous unsuccessful attempts of THC
self-administration in nonhuman primates exclusively used Old-World primates (i.e.
rhesus monkeys). Furthermore, the squirrel monkeys used were not food restricted in
contrast to studies in rodents that demonstrated reinforcing effects of synthetic
cannabinoids. The initial demonstration of THC self-administration in squirrel monkeys
was performed in monkeys that had a prior history of cocaine self-administration and at
the time, was considered by others to be an antecedent variable that contributed to its
effectiveness as a reinforcer in these studies (Maldonado, 2002). However, subsequent
work showed that drug-naïve squirrel monkeys readily acquired and maintained robust
rates of THC self-administration (Justinova et al., 2003) thereby demonstrating that a
prior cocaine self-administration history was not a necessary condition among the other
procedural variations.
The successful demonstrations of THC reinforcement in squirrel monkeys
employed an FR 10 schedule of drug injection with a 60-s timeout after each injection. In
the initial study, saline was substituted for cocaine until behavior was reliably
extinguished. Subsequently, THC was made available for self-administration by
substituting the drug for saline with periods of vehicle substitution between different
doses of THC. This substitution procedure is in contrast to earlier studies in rhesus
monkeys where THC was substituted directly for cocaine or other drugs like PCP or
heroin (Mansbach et al., 1994, Pickens et al., 1973; Carney et al., 1977; Harris et al.,
1974; Kaymakcalan, 1973; Li et al., 2012). In fact, this substitution procedure for
studying self-administration is in contrast to what has been used historically to assess
the reinforcing effects of a stimulus where a test drug is substituted for a stimulus (e.g.
19
food pellets, another drug) that previously maintained responding. The significance of
substituting THC for saline vs. a stimulus with reinforcing effects in order facilitate the
acquisition of THC-maintained behavior remains to be determined. This procedural
variation could suggest that THC is more likely to maintain high levels of behavior only
when it is substituted for a stimulus that is either not reinforcing or is of lower reinforcing
efficacy than THC.
Perhaps the most critical procedural factor other than species differences for
initially demonstrating the reinforcing effects of THC in squirrel monkeys was the dose
range used. Specifically, the dose range of THC (2.0 – 8.0 μg/kg/injection) that
maintained behavior above vehicle levels in the squirrel monkeys used by Tanda et al.
(2000) was relatively lower than what was used in previously unsuccessful attempts in
rhesus monkeys (Fig. 1).
Fig. 1. Dose comparisons for preclinical monkey
THC self-administration studies. Note: there is no ordinate.
10-15puffs=2.9-4.3μg/kgTHC
0 100 200 300 400
THC (µg/kg/injection)
Tanda et al. (2000)
Justinova et al. (2003)
Li et al. (2012)
Mansbach et al. (1994)
Harris et al. (1974)
Carney et al. (1977)
Kaymakcalan (1973)
Pickens et al. (1973)
20
Peak rates of responding and maximal numbers of injections were maintained by
2.0 – 4.0 μg/kg/injection doses of THC in squirrel monkeys whereas many of the early
unsuccessful studies with rhesus monkeys did not test doses lower than 100
μg/kg/injection. It is interesting to note that squirrel monkeys have been reported to show
no visible signs of intoxication after reinforcing doses of THC are made available for self-
administration. In contrast, studies in rhesus monkeys have reported that monkeys did
appear intoxicated after the session when relatively higher doses of THC were used,
although few injections were received. These observations suggest that the dose range
necessary for engendering THC-maintained behavior might be significantly lower what
appears to be behaviorally active by visual inspection. Furthermore, the dose range of
THC that maintains peak levels of behavior in squirrel monkeys has been reported to be
analogous to the dose range self-administered by humans smoking a single marijuana
cigarette. For instance, the average marijuana cigarette contains approximately 15 mg of
THC but the actual THC intake is only 3 mg considering the bioavailability of THC from
smoking is about 20% (Agurell et al., 1986). Distributed over 10 to 15 puffs per cigarette,
this equates to approximately 2.9 – 4.3 μg/kg of THC delivered per puff, which
corresponds to the unit doses used to maintain responding in squirrel monkeys. The
total intake of THC obtained in squirrel monkeys when 2 - 4 μg/kg/injection of THC was
available for self-administration is 50 and 120 μg/kg, respectively, over a 1-hour session.
This is also analogous to a report in humans that showed an i.v. injection of 5 mg of THC
(approximately 70 μg/kg) produced the same degree of euphoria compared to smoking a
marijuana cigarette (Ohlsson et al., 1980).
Another methodological difference between successful THC self-administration
studies in squirrel monkeys compared to others involves the vehicle used to dissolve
THC in solution for intravenous injection. Many early studies that assessed i.v. THC self-
administration were conducted with solutions of THC in suspension due to its lipophilic
21
nature, low solubility in water, and high doses used. In contrast, THC was dissolved in a
Tween 80/ethanol/saline vehicle to produce a clear solution for the studies in squirrel
monkeys. This vehicle has been shown to provide rapid distribution of THC to the brain
compared to when the drug is in suspension (Mantilla-Plata and Harbison, 1975) and
significantly elevates dopamine levels in the nucleus accumbens shell of rodents (Tanda
et al., 1997). The bioavailability of a drug is certainly a factor that affects its behavioral
effects. Therefore, vehicle preparation as a means for increasing the bioavailability of
THC likely represents an important methodological factor that should be taken into
consideration for enhancing the likelihood of THC to function as a reinforcer in
experimental animals.
Potential factors limiting the reinforcing effects of cannabinoids in experimental
animals
It is currently unknown whether previous unsuccessful attempts at establishing
the reinforcing effects of cannabinoids in rhesus monkeys were due to methodology or a
direct species difference between squirrel (New World) and rhesus (Old World)
monkeys. To address this, in Chapter II, THC was made available to rhesus monkeys
under similar SA methodological conditions as used in the successful demonstrations in
squirrel monkeys, including schedule of reinforcement, dose range, and vehicle
preparation. It is important that the THC SA procedure be extended to include rhesus
monkeys because this species is phylogenetically closer to humans than any other
currently available preclinical laboratory species including squirrel monkeys, which would
enhance the ability to accurately generalize results to human drug abuse. The
reinforcing effects of the synthetic bicyclic cannabinoid analog, CP 55,940, a potent
CB1/CB2 agonist, were also evaluated in Chapter II using the same monkeys, which
allowed for a better understanding of the role of pharmacological efficacy at the CB
22
receptor in SA, such as differences in potency and magnitude of response. It was
hypothesized that by using conditions that promoted THC self-administration in squirrel
monkeys, successful self-administration in rhesus monkeys would be obtained.
Besides methodological factors, one hypothesis for the failure of THC to function
as a reinforcer in most studies using experimental animals is that the initial aversive
(e.g., rate-decreasing) effects of THC counteract the reinforcing effects. Multiple stimulus
effects of drugs of abuse have been increasingly recognized, not all of which are
rewarding. Thus, drug-taking behavior has been conceptualized as a balance between a
drug’s reinforcing effects and the rate-suppressing effects (e.g., aversion) that limit
responding leading to a drug’s presentation (Verendeev and Riley, 2013). The impact of
these two opposing systems is demonstrated through the inverted U-shaped nature of a
drug self-administration dose-response curve. For example, on the ascending limb, the
reinforcing effect of a drug increases in proportion to the size of the drug’s unit dose and
thus leads to an increase in rate of responding. However, on the descending limb,
response rate and reinforcer magnitude are inversely proportional due to the direct rate-
suppressing effects of high drug doses (e.g., sedation, induction of stereotypies,
aversion, etc.) that serve to counteract the drug’s reinforcing (i.e., rate-increasing
effects). This concept has been experimentally validated by cocaine self-administration
studies showing that the development of tolerance to the rate-decreasing effects leads to
a vertical shift of the dose-response curve, which primarily occurs on the descending
limb (Ahmed and Koob, 1998; Deroch et al., 1999; for review see Zernig et al., 2004).
The notion of THC-maintained behavior being limited by the drug’s initial rate-
decreasing effects is an empirical question and one not previously tested in animal
models of self-administration. Supporting evidence for this hypothesis, however, has
been demonstrated in parallel studies involving conditioned place preference in rodents
as well as epidemiological evidence in humans. For instance, as described previously,
23
cannabinoids have been shown to generally induce conditioned place aversion rather
than place preference in rodents (e.g. Lepore et al., 1995; McGregor et al., 1996;
Sanudo-Pena et al., 1997; Mallet and Beninger, 1998; Chaperon et al., 1998; Cheer et
al., 2000; Zimmer et al., 2001) suggesting that there may be an initial
dysphoric/anxiogenic effect of THC upon first exposure which may correspond to
negative results in self-administration studies. Moreover, retrospective studies in
humans show that individuals who are more sensitive to the aversive effects of cannabis
were less likely to be current users (Haertzen et al., 1983; Thomas, 1996).
In order to test the hypothesis that sensitivity to the rate-decreasing effects would
influence measures of cannabinoid reinforcement, the potency of both THC and CP
55,940 to decrease food-maintained responding was correlated with rates of SA in each
subject. It was hypothesized that individual subjects less sensitive to the rate-decreasing
effects of CP 55,940 and THC will have greater rates of SA of both drugs. Furthermore,
the rate-decreasing effects of cannabinoids that may be counteracting initial reinforcing
effects may also represent a variable that could be modified to facilitate acquisition of
SA. Preclinical data show that tolerance to the effects of repeated THC administration
can differentially develop depending on the behaviors studied (Tanda and Goldberg,
2003). For instance, tolerance to repeated THC administration has been demonstrated
for discriminative stimulus effects, decreases in operant responding, hypothermia,
locomotor activity, nociception, and ingestion (McMillan et al., 1970; Miczek, 1979; Wiley
et al., 1993a; Fan et al., 1994; MicKinney et al., 2008; McMahon, 2011). However, it has
been reported that tolerance does not develop to the subjective “high” of marijuana after
chronic consumption in humans (Lindgren et al., 1981; Perez-Reyes et al., 1991; Kirk
and de Wit, 1999). These effects are consistent with the lack of tolerance development
to THC-induced increases in ventral tegmental area dopamine neuron firing rates both in
vivo (Wu and French, 2000) and in vitro (Cheer et al., 2000). Overall, these data suggest
24
that tolerance may preferentially develop to the rate-decreasing effects and not the
reinforcing effects of THC. Experiments within Chapter II extended the assessment of
tolerance to the behavioral effects of CB agonists to include THC-induced decreases in
operant responding that may impact measures of THC reinforcement using a drug SA
paradigm. We hypothesized that tolerance would develop to the rate-decreasing effects
of THC and this would result in increases in rates of THC SA compared to the initial
assessment.
The pharmacokinetic profiles of drugs are crucial for their reinforcing effects.
Thus, another reason for the relativeness ineffectiveness of cannabinoids to function as
reinforcers in preclinical animal models is because the appropriate conditions have not
been implemented to control for the pharmacokinetic and metabolic factors that may limit
drug maintained responding. Cannabinoids have a relatively delayed onset of their
behavioral effects and an extended elimination phase (Ohlson et al., 1980; Hollister et
al., 1981). For instance, experiments in humans have shown that maximal intoxication
produced by THC can occur 30 min or more after intravenous injection. In drug
discrimination studies in rodents, maximal THC-appropriate responding was not
observed until 60 min after i.m. injection and substantial THC-appropriate responding
was observed for as long as 6 h after the initial administration (Prescott et al., 1992). In
rhesus monkeys, it has been reported that the discriminative stimulus effects of THC can
be detected within 20 min but remain present for up to 4 h after intravenous
administration (McMahon, 2006). Therefore, under schedules of self-administration
where response rates and reinforcement is directly related (i.e., fixed ratio schedules),
these pharmacokinetic variables likely limit subsequent self-administration either by
reducing the temporal contiguity between the drug effect and the response that produces
it or through rate-decreasing effects (e.g., satiation, decreased discriminability of
additional drug injections) resulting from slow offset of drug effects. In Chapter II, these
25
possibilities were examined by studying THC self-administration under a second-order
schedule where behavior was maintained across long intervals between drug injections
by conditioned stimuli. Mansbach et al. (1994) attempted to also take these factors into
consideration in order to demonstrate the reinforcing effects of THC in rhesus monkeys.
That study used a procedure where individual THC injections were spaced by 2 h
intervals and paired with a distinct visual stimulus; however, THC-maintained responding
above vehicle levels was not obtained. Notably, the dose range tested in this study was
0.017 – 0.1 mg/kg/injection, which was considerably higher than the dose range that was
eventually found to maintain the highest amount of behavior leading to THC injections in
squirrel monkeys (0.001 – 0.004 mg/kg/injection). Therefore, we hypothesized that by
using a lower range of doses coupled with second-order contingencies that would help
to mitigate the biodispositional factors of THC that may inhibit the expression of its
reinforcing effects, THC-maintained behavior would be obtained.
The role of cognitive function in substance use disorders
Despite advances in pharmacotherapies over the last few decades, behavioral
treatments are often the most efficacious option for the treatment of various substance
use disorders. Among those with the strongest level of empirical support from
randomized clinical trials are 1) contingency management, where abstinence or other
targeted outcomes are reinforced with incentives (Higgins et al., 1991; Petry, 2006), 2)
cognitive behavioral therapy, which teaches specific strategies and skills to reduce
substance use (Carroll et al., 1994), and 3) motivational interviewing, where a specific,
nonjudgmental interviewing style is used to enhance motivation and harness the
patient’s capacity for change (Hettema et al., 2005; Miller, 1985). However, despite
empirical support, the established efficacy of many of these approaches remain at low
levels. For instance, a meta-analysis of randomized controlled trials for cognitive-
26
behavioral therapy approaches to treat adult alcohol and illicit drug users reported only a
small, but statistically significant treatment effect (g = 0.154), with effect sizes depending
on the substance of abuse (Magill and Ray, 2009). Moreover, the successful
implementation and broad dissemination of behavioral treatments are hindered by
considerable costs and resources needed. Thus, many areas for improvement are
present and more efficient ways to implement behavioral treatments are needed. In an
effort to do so, it is important to better understand how and when the treatments work.
This information can then be used to maximize the effects and develop a more targeted
approach.
One strategy is through understanding the cognitive mechanisms underlying the
effectiveness of behavioral treatments, which can ultimately be used to develop
cognitive enhancement-based interventions to supplement standard behavioral
therapies. Indeed, the idea of cognitive control is central to many contemporary models
of addiction (Jentch and Pennington, 2014) and is a factor that can affect treatment
outcome (Sofuoglu et al., 2010). For example, cognitive impairments to executive control
in drug-dependent populations have been proposed as one mechanism to explain the
maintenance of drug use and the difficulty many individuals have in resisting habitual
drug use once established (Everitt et al., 2007; Goldstein and Volkow, 2011; Li and
Sinha, 2008; Porrino et al., 2007). In particular, aspects of executive function such as
decision-making, response inhibition (failure to abstain from substance use), error
processing (failure to adapt/learn with respect to prior harmful behavior), planning,
working memory, set-shifting, and attention appear to be associated with substance use
and substance use disorders when studied in adults (Sofuoglu et al., 2013). Further,
cognitive impairments in substance abusers at treatment initiation are generally
associated with poorer treatment retention and treatment outcomes. For instance,
research with polydrug users in a residential treatment program found that participants
27
who scored low on a neuropsychological test battery had had shorter lengths of stay in
treatment and were viewed less favorably by the treatment staff (Fals-Stewart, 1993).
Therefore, evidence indicates that attenuating cognitive impairments may serve as
potential treatment targets for substance use disorders.
The effects of THC and marijuana use on cognitive function have been studied
extensively in humans and animal models given the increasing prevalence of cannabis
use and the number of treatment-seeking patients with cannabis use disorder. Although
much progress has been made in understanding the acute (i.e., in the presence of
intoxication) cognitive consequences of cannabis use (Crean et al., 2011; Crane et al.,
2013), the residual effects of cannabis on cognitive function (i.e., after the intoxicating
effects have subsided) and cannabis-induced impairments to cognitive function during
abstinence are much more equivocal. It is particularly important to better characterize
these cognitive effects of THC because growing evidence suggests that cannabis-
induced cognitive deficits are not only apparent after smoking, but persist for a period of
time after the drug has been used (Bolla et al., 2005), which may hinder an individual’s
ability to make the best use of behavioral therapies. This issue, however, lies in better
characterizing the time course of effects and determining which specific cognitive
domains affected are most strongly related to treatment outcome. The remainder of this
chapter will present an overview of the effects of cannabis on neurocognitive function
and how these studies will inform the research within this dissertation focused on
elucidating the exact nature, and persistence of THC-induced cognitive impairment that
can ultimately be used to develop cognitive enhancement approaches to treat cannabis
use disorder.
Residual effects of cannabis on cognition in humans
For the following studies reviewed in this section, the residual effects of cannabis
28
on cognition refer to a period of time after which the direct intoxicating effects of
cannabis have subsided. Pharmacokinetic studies in humans showed that the peak level
of subjective “high” of smoked cannabis was observed after 20-30 minutes and
decreased to baseline levels by 4 hours after smoking. Thus, the current operational
definition of residual effects only includes assessments occurring after this time frame
but within 24 hours of last use. Furthermore, the cognitive domains discussed only
pertain to the domains studied in the present dissertation. These cognitive domains have
been selected from a therapeutic standpoint because they have been shown to have
prognostic significance or relevance in mediating the skills necessary for successful
behavioral modification for cannabis or other substance use disorders.
One of the domains of cognitive function most extensively studied as it relates to
the residual effects of cannabis in humans is inhibition. Inhibition is a significant feature
of many drug dependent individuals, which is defined by an inability to override a
prepotent response (i.e. drug taking) in response to drug cues. As a result, improving
behavioral inhibition by shifting focus away from drug use and toward non-drug
reinforcement (e.g., improved health, stronger interpersonal relationships, stable
employment, etc.) represents a viable cognitive domain to be targeted by cognitive
rehabilitation strategies. One of the first groups (Pope et al., 1996) to study inhibition in
cannabis users examined heavy and light users after a minimum of 19 hours of
abstinence. Heavy cannabis users demonstrated significantly more errors of inhibition
and perseveration compared with light users. Solowij et al. (2002) replicated these
findings in cannabis users after at least 12 hours of abstinence. The severity of these
deficits was correlated with years of use. Several other researchers have found a similar
pattern of impairment within inhibitory functioning (Aharonovich et al., 2008; Cunha et al,
2010). In contrast, a number of researchers found no residual effects of cannabis use on
inhibition (Whitlow et al., 2004; Gruber et al., 2005; Hermann et al., 2007); however,
29
these studies had small sample sizes (e.g. N=10) and the length of abstinence was
unspecified or was highly variable, ranging from 12 hours to 18 years. Thus, the residual
effects of cannabis use within this cognitive domain are equivocal, although clear
indication exists of impairment after acute cannabis intoxication (McDonald et al., 2003;
Ramaekers et al., 2006). As a result, studies are needed that more strictly control for
amount of time of THC intake.
Another cognitive domain with strong implications in substance use disorders
and treatment response is attentional control. Measures of attention have been closely
linked with inhibitory control and are also largely mediated by the prefrontal cortex.
Several studies have examined the residual effects chronic cannabis use on measures
of attention and as with inhibition, the findings have been inconsistent. In one study for
example, Pope et al. (2002) tested current, heavy cannabis users (i.e., users that had
smoked cannabis at least 5000 times and were smoking daily at the time of study entry),
former heavy cannabis users (i.e., users that had also smoked at least 5000 times but
had smoked fewer than 12 times in the past 3 months), and control subjects (who had
only tried cannabis < 50 times during their lifetime) on various measures of selective and
divided attention during experimentally controlled days of abstinence (i.e. 0, 1, 7, and 28
days since last drug use). At all four time points, no significant differences were found on
attentional abilities. Similarly, Jager et al., (2006) also found no differences in attentional
abilities as a result of the residual effects of long-term cannabis use. However, in
contrast, Solowij et al. (1995, 2002) found significantly impaired attentional abilities in
short- and long-term cannabis users compared to control subjects when assessed 24
hours since last use. It was also found that cannabis users demonstrated longer reaction
times to complete the tasks and impairments involved in information processing abilities
associated with the task. Furthermore, Wadsworth et al. (2006) examined attentional
capacities under a choice reaction time task within the context of a “real world” setting,
30
such that performance was assessed before and immediately after work, at both the
beginning and end of the work week. They found that, cannabis use was associated with
significantly impaired performance on the attention task both at the beginning of the work
week and at the end, which was significantly correlated with duration of cannabis use.
However, cannabis use was not found to be associated with an increase in workplace
errors.
Working memory, the ability to hold and manipulate information and remember it
after a short delay, is a major component of executive function and is a cognitive domain
associated with well documents in chronic drug abusers. Working memory has
consistently been shown to be impaired by cannabis shortly after use (e.g. Hart et al.,
2001, 2010); however, the residual effects of cannabis use on measures of working are
a lot more inconsistent. For example, some studies show residual impairments in heavy
cannabis using young adults on immediate recall (Fried et al., 2005), verbal reasoning
(Wadsworth et al., 2006), and verbal n-back tasks (Herzig et al., 2014). In contrast,
several studies have found no residual impairments in working memory abilities between
heavy and light cannabis users compared with control subjects (Pope et al., 1996;
Kanayama et al., 2004; Jager et al., 2006; Solowij et al., 2002; Whitlow et al., 2004; Fisk
and Montgomery., 2008); however, it is unclear whether these results are due to the
duration and quantity of cannabis use or a lack of temporal specificity to detect deficits or
small sample size.
Another cognitive domain associated with substance abuse and treatment
outcome is associative learning. For instance, impairments in associative learning have
been hypothesized to play a role in drug craving and relapse through a process of
“overlearning” drug-stimulus reinforcement-outcome pairings at the expense of nondrug
reinforcers (Gould, 2010). However, intact associative learning systems are crucial for
acquiring new skills and coping strategies during behavioral therapy sessions and
31
applying those skills outside of the treatment setting to facilitate abstinence (Rezapour et
al., 2016). In humans, associative learning is typically measured by word-list learning
tasks and much like the previously discussed cognitive domains, findings for the residual
effects of chronic cannabis use are equivocal. For instance, residual impairments (i.e. >
12 hours since last use) have been reported in adolescents (Harvey et al., 2007; Solowij
et al., 2011; Dougherty et al., 2013) and young adults (Cuttler et al., 2012; Gonzalez et
al., 2012; Becker et al., 2014) as well as in occasional users (Hanson et al., 2010).
Several of these studies report significant associations between impairment and
frequency, quantity, and duration of use and age of onset (e.g., Solowij et al., 2011;
Becker et al., 2014). On the other hand, several studies report no difference in
associative learning performance in recently abstinent cannabis users compared to
nonusers (Fisk and Montgomery, 2008; Gruber et al., 2012).
Long-term effects of cannabis on cognition in humans
While the residual effects of cannabis use on cognition have been
operationalized to refer to the period after the intoxicating effects have subsided but
within 24 hours of last use, evidence indicates that certain cognitive impairments may
persist for several weeks into abstinence. As a result, the human literature has
designated the term “long-term effects” to indicate a period of 3 weeks or longer since
last use (Crean et al., 2011). This cutoff ensures that the acute and residual effects of
cannabis in the brain were eliminated by the time of assessment and thus provides a
reflection of any long-lasting impairments in basal functioning. The long-term effects of
cannabis use on cognition have received considerable research attention; however,
much like the literature on the residual effects, the findings for most cognitive domains
are equivocal. The one exception is episodic memory, which has been shown by a
number of investigators to be impaired for up to 28 days following abstinence (Gonzalez
32
2007; Grant et al. 2003; Solowij and Battisti, 2008).
For the long-term effects of cannabis on behavioral inhibition, studies using the
Stroop Test have consistently found no significant differences between chronic cannabis
users and control groups at 28 days of abstinence (Pope et al., 2001; 2002; 2003) or up
to a year of abstinence (Lyons et al., 2004; Verdejo-Garcia et al., 2005). In contrast,
studies using the Wisconsin Card Sort Test have all found significant differences in
performance up to 28 days of abstinence (Bolla et al., 2002, Pope et al., 2001; 2002;
2003), with the exception of Lyons et al. (2004), who compared cannabis use in heavy
users (>1 time per week for a minimum of 1 year) vs. non using (< 5 times in their life
time) male monozygotic twins. These findings may indicate that certain aspects of
inhibition as assessed by different tasks are more vulnerable to persistent impairment
during abstinence. Other measures of inhibition in abstinent cannabis users derived from
tasks such as the go/no-go or stop-signal reaction time tasks have not been assessed.
Much like the residual effects, the long-term effects of cannabis on attention are
also mixed. In adolescent cannabis users, measures of attentional abilities have been
found to be impaired after at least 21 days of abstinence (Hanson et al. 2010; Jacobsen
et al. 2004; Medina et al. 2007; Tapert et al. 2007), with evidence of a dose-dependent
relationship with amount of lifetime use (Medina et al. 2007). Conversely, others studies
in adolescent cannabis users show that performance on measures of selective and
divided attention were not different compared to controls after 45 days of abstinence
(Jacobsen et al. 2004). In samples of heavy, chronic adult cannabis users, attentional
deficits have been reported after 28 days of abstinence (Bolla et al., 2002) as well as
after more variable periods of abstinence (i.e., 6 weeks to 2 years) (Solowij, 1995).
Moreover, several studies have shown no attentional deficits in heavy cannabis users
after periods of abstinence ranging from 28 days to one year (Lyons et al., 2004; Pope et
al., 2001; 2002; 2003; Verdejo-Garcia et al., 2005), with some indication that disparate
33
findings are the result of task complexity.
In studies examining working performance during long-term abstinence from
chronic cannabis use, there appears to be a relationship between the age of the user
and the cognitive load. For instance, adolescent regular cannabis users that met DSM
criteria for cannabis abuse or dependence demonstrated poorer working memory
compared to controls after 3 and 13 days of abstinence during the Letter-Number
Sequencing task, which required the active manipulation of multidimensional items
(Hanson et al. 2010). However, working memory performance in adolescent users with
similar use characteristics was not different from controls after 8 days of abstinence
during the simpler Sternberg’s item recognition matching task (Jager et al. 2010). After
longer periods of abstinence, the evidence is mixed regarding the persistence of working
memory deficits among adolescents. For instance, some studies reported deficits after
28 days of monitored abstinence when performance was assessed with the n-back task
(Jacobsen et al. 2004; Jacobsen et al. 2007), whereas others reported no deficits
compared to control subjects after similar lengths of abstinence and on similar tasks
(Hanson et al. 2010; Padula et al. 2007; Schweinsburg et al. 2010). In contrast to studies
with adolescent samples, most studies using adult subjects have reported no differences
in working memory performance compared to controls after long-term abstinence
(Becker et al. 2010; Chang et al. 2006; Fisk and Montgomery 2008; J. Grant et al. 2011;
Jager et al. 2006) indicating a potential interaction between cannabis use and
neurodevelopmental processes that mediate working memory. On the other hand,
working memory deficits may also be related to amount of cannabis use, as Harvey and
colleagues (2007) found that the amount of cannabis used was one of the strongest
predictors of poorer spatial working memory performance in regular adolescent cannabis
compared to occasional users.
The long-term effects of chronic cannabis use on associative learning in humans
34
have also been mixed with some studies showing improvement during prolonged
periods of abstinence while others do not. For instance, in one study where cognitive
performance was studied during closely monitored abstinence periods, chronic
marijuana users showed impaired performance on a verbal list learning task at 3 days
since last use compared to non-using controls, although performance improved to
control levels by two weeks of abstinence (Hanson et al., 2010). In a study with lower
temporal resolution, it was also found that performance on a verbal list learning task in
former heavy cannabis users (> 12 months since last use) had improved to similar
performance levels of non-users (Tait et al., 2011). In contrast, Medina et al. (2007)
found impaired associative learning performance in adolescent marijuana users
compared to controls after > 23 days of monitored abstinence suggesting persistent
impairment in some populations of users.
Overall, the non-acute studies of cannabis on cognition in humans seem to raise
more questions than answers with regard to the specific cognitive domain that is
affected. In particular, some of the inconsistencies among these studies arise from the
heterogeneity of the participants. For instance, factors such as length of abstinence, age
at which cannabis users discontinue their use, amount and duration of cannabis use,
concurrent use of other substances, and whether prospective study designs were used
all complicate the interpretation of many of these studies. In addition, another factor that
seems to be overlooked when considering the effects of cannabis on cognition is the
cognitive load for each task. For example, it is unknown for many studies whether a lack
of effect is merely due to the task not containing a high enough complexity in order to
detect more subtle brain abnormalities among cannabis users. Moreover, studies in
humans cannot distinguish whether marijuana abusers exhibit innate cognitive
differences prior to initiating drug taking. Therefore, it is difficult to clearly establish a
causal role of long-term THC exposure in producing cognitive deficits.
35
Cognitive effects of THC in preclinical animal models
The utilization of preclinical animal models to study the long-term effects of THC
on cognition is crucial because many of the variables that contribute to the equivocal
findings in studies with human subjects can be controlled for in a systematic manner.
The acute effects of THC and synthetic cannabinoids on learning and memory
processes have been extensively studied in rodents and nonhuman primates and have
been shown to produce impairments in a variety of tasks including the eight-arm radial
maze (Han and Robinson, 2001; Mallet and Beninger, 1998; Winsauer et al. 1999), two-
component instrumental discrimination task (Han & Robinson, 2001; Mallet and
Beninger, 1998; Nava et al., 2000; Winsauer et al., 1999); conditional discriminations
(Han & Robinson, 2001; Mallet & Beninger, 1998; Nava et al., 2000; Winsauer et al.,
1999; Zimmerberg et al., 1971), recognition memory tasks (Aigner, 1988; Schulze et al.,
1989; Kangas et al., 2016), spatial delayed response tasks (Rupniak et al., 1991; Taffe,
2012), and time estimation (Peiper, 1976; Schulze et al., 1988, 1989).
In addition to examining the acute effects of THC on cognition, it is critical to
employ longitudinal studies in preclinical animal models in order to disentangle the
temporal ordering of cannabis-related cognitive deficits involving the exact nature,
persistence, and reversibility of these deficits. The use of nonhuman primates is
especially well-suited for these types of studies due to their long lifespan, capability to
learn complex cognitive tasks, and similarity to humans in regards to neuroanatomy and
neurochemistry (Berger et al., 1991; Joel and Weiner, 2000). However, to date, only one
preclinical study in nonhuman primates has examined the residual effects of chronic
THC exposure (Verrico et al., 2014). In that study, adolescent rhesus monkeys were
treated with THC (120 or 240 µg/kg, i.v.), 5 days per week for approximately 6 months
during which working memory performance was assessed 23 hours after drug
36
administration throughout the study. It was found that chronic THC exposure impaired
spatial-, but not object-working memory; tolerance or sensitization did not develop to
these effects.
Studies in Chapter 3 expanded on these findings by examining both the residual
effects of THC administration (i.e., 22 hours after administration) and the effects of
several weeks of abstinence (i.e., 4 -6 weeks since discontinuation of chronic THC
administration) on four cognitive domains that assessed 1) associative learning through
simple and multidimensional discrimination learning, 2) attention and working memory
through delayed-match to sample performance, 3) inhibition though reversal learning
requiring the ability to inhibit a previously established response based on an acquired
stimulus-reinforcement association, and 4) behavioral flexibility through set-shifting
performance requiring the formation of an attentional set based on relevant stimuli for
reinforcement and the disregard to irrelevant stimuli, all by using a within-subjects design
in monkeys. The development of tolerance to the acute effects of THC during chronic
exposure was also examined for each of these cognitive domains. These particular
domains of cognition were assessed because of their relevance in a cognitive
rehabilitation context, in that they underlie the skills necessary to assimilate and
integrate new information into a plan for behavior change that can lead to sobriety and
the ability to maintain focus on long-term goals (Rezapour et al., 2016). Moreover, as
described earlier, the residual effects of THC within these domains are equivocal in
studies with human subjects, most likely due to the numerous variables that are difficult
to control for in human research including differences in time since last use, amount and
duration of cannabis use, previous drug histories, social variables, and concurrent use of
other substances. In addition, studies in humans cannot distinguish whether marijuana
abusers exhibit innate cognitive differences prior to initiating drug taking so it is difficult
to clearly establish a causal role of long-term THC exposure in producing cognitive
37
deficits. By controlling for these factors in a systematic manner, the results of the studies
within this dissertation can be used to improve the efficacy of behavioral treatments for
cannabis use disorder by identifying the specific domains of cognitive functioning and a
temporal window that can be targeted for successful treatment outcome.
38
REFERENCES
Adams IB, Martin BR (1996) Cannabis: Pharmacology and toxicology in animals and
humans. Addiction 91: 1585– 1614.
Agurell S, Halldin M, Lindgren JE, Ohlsson A, Widman M, Gillespie H, et al. (1986)
Pharmacokinetics and metabolism of delta 1-tetrahydrocannabinol and other
cannabinoids with emphasis on man. Pharmacol Rev. 38: 21–43.
Aharonovich E, Brooks AC, Nunes EV, Hasin DS (2008) Cognitive deficits in marijuana
users: Effects on motivational enhancement therapy plus cognitive behavioral
therapy treatment outcome. Drug Alcohol Depend 95: 279-283.
Ahmed SH, Koob GF (1998) Transition from moderate to excessive drug intake: change
in hedonic set point. Science 282: 298–300.
Aigner TG (1988) Delta-9-tetrahydrocannabinol impairs visual recognition memory but
not discrimination learning in rhesus monkeys. Psychopharmacology (Berl). 95:
507-511.
Atwood, B. K. and Mackie, K (2010) CB2: a cannabinoid receptor with an identity crisis.
Br. J. Pharmacol. 160: 467–479.
Balster RL, Prescott WR (1992) Delta 9-tetrahydrocannabinol discrimination in rats as a
model for cannabis intoxication. Neurosci Biobehav Rev. 16: 55–62.
Balster RL, Schuster CR (1973) Fixed-interval schedule of cocaine reinforcement: effect
of dose and infusion duration. J Exp Anal Behav. 20: 119–29.
Bardo MT, Bevins RA (2000) Conditioned place preference: what does it add to our
preclinical understanding of drug reward? Psychopharmacology. 153: 31–43.
Barrett RL, Wiley JL, Balster RL, Martin BR (1995) Pharmacological specificity of Δ9-
tetrahydrocannabinol discrimination in rats. Psychopharmacology. 118: 419–
424.
39
Becker B, Wagner D, Gouzoulis-Mayfrank E, Spuentrup E, and Daumann J (2010) The
impact of early-onset cannabis use on functional brain correlates of working
memory. Progress in Neuro-Psychopharmacology & Biological Psychiatr. 34:
837–845.
Becker MP, Collins PF, Luciana M (2014) Neurocognition in college-aged daily
marijuana users. J Clin Exp Neuropsychol. 36:379-398.
Berger B, Gaspar P, Verney C (1991) Dopaminergic innervation of the cerebral cortex:
unexpected differences between rodents and primates. Trends Neurosci. 14: 21–
27.
Bolla KI, Brown K, Eldreth D, Tate K, Cadet JL (2002) Dose-related neurocognitive
effects of marijuana use. Neurology. 59: 1337-1343.
Bolla KI, Eldreth DA, Matochik JA, Cadet JL (2005) Neural substrates of faulty decision-
making in abstinent marijuana users. Neuroimage 26: 480–492.
Bossong MG, van Berckel BN, Boellaard R, Zuurman L, Schuit RC, Windhorst AD et al.
(2009) Delta 9-tetrahydrocannabinol induces dopamine release in the human
striatum. Neuropsychopharmacology. 34: 759–766.
Bossong MG, Mehta MA, van Berckel BN, Howes OD, Kahn RS, Stokes PR (2015)
Further human evidence for striatal dopamine release induced by administration
of 9-tetrahydrocannabinol (THC): selectivity to limbic striatum.
Psychopharmacology. 232: 2723–2729.
Braida D, Pozzi M, Cavallini R, Sala M (2001a) Conditioned place preference induced by
the cannabinoid agonist CP 55,940: interaction with the opioid system.
Neuroscience 104:923–926.
Braida D, Pozzi M, Parolaro D, Sala M (2001b) Intracerebral self-administration of the
cannabinoid receptor agonist CP 55,940 in the rat: interaction with the opioid
system. Eur J Pharmacol 413:227–234.
40
Braida D, Iosue S, Pegorini S, Sala M (2004) Delta9- tetrahydrocannabinol-induced
conditioned place preference and in- tracerebroventricular self-administration in
rats. Eur J Pharmacol 506: 63–69.
Cabeza de Vaca S, Carr KD (1998) Food restriction enhances the central rewarding
effect of abused drugs. J Neurosci. 18: 7502–7510.
Cappell H, Kuchar E, Webster CD (1973) Some correlates of marihuana self-
administration in man: a study of titration of intake as a function of drug potency.
Psychopharmacologia. 29: 177–184.
Carney JM, Uwaydah IM, Balster RL (1977) Evaluation of a suspension system for
intravenous self-administration of water insoluble substances in the rhesus
monkey. Pharmacol Biochem Behav. 7: 357–364.
Carroll ME, Meisch RA. Increased drug-reinforced behavior due to food deprivation. In:
Thompson T, Dews PB, Barrett JE, editors. Advances in Behavioral
Pharmacology, vol. 4. New York’ Academic Press; 1984. 47– 88.
Carroll ME, France CP, Meisch RA (1979) Food deprivation increases oral and
intravenous drug intake in rats. Science. 205: 319– 321.
Carroll KM, Rounsaville BJ, Gordon LT, Nich C, Jatlow P, Bisighini RM, Gawin FH
(1994) Psychotherapy and pharmacotherapy for ambulatory cocaine abusers.
Arch. Gen. Psychiatry 51: 177-187.
Centers for Disease Control and Prevention. Best Practices for Comprehensive Tobacco
Control Programs — 2014. Atlanta: U.S. Department of Health and Human
Services, Centers for Disease Control and Prevention, National Center for
Chronic Disease Prevention and Health Promotion, Office on Smoking and
Health, 2014.
Chait LD (1989) Delta-9-tetrahydrocannabinol content and human marijuana self-
administration. Psychopharmacology (Berl). 98: 51–55.
41
Chang L, Yakupov R, Cloak C, Ernst T (2006) Marijuana use is associated with a
reorganized visual-attention network and cerebellar hypoactivation. Brain 129:
1096–1112.
Chaperon F, Soubrie P, Puech AJ, Thiebot MH (1998) Involvement of central
cannabinoid (CB1) receptors in the establishment of place conditioning in rats.
Psychopharmacology. 135: 324–332
Cheer JF, Marsden CA, Kendall DA, Mason R (2000) Lack of response suppression
follows repeated ventral tegmental CB administration: an in vitro
electrophysiological study. Neuroscience. 99: 661–667.
Corcoran ME, Amit Z (1974) Reluctance of rats to drink hashish suspensions: free-
choice and forced consumption, and the effects of hypo- thalamic stimulation.
Psychopharmacologia 35: 129–147
Crane NA, Schuster RM, Fusar-Poli P, et al. (2013) Effects of cannabis on
neurocognitive functioning: recent advances, neurodevelopmental influences,
and sex differences. Neuropsychol Rev. 23: 117–37.
Crean, RD., Crane, NA, and Mason, BJ (2011) An evidence based review of acute and
long-term effects of cannabis Use on executive cognitive functions. Journal of
Addiction Medicine. 5: 1–8.
Cunha, P. J., Nicastri, S., de Andrade, A. G., & Bolla, K. I. (2010) The frontal
assessment battery (FAB) reveals neurocognitive dysfunc- tion in substance-
dependent individuals in distinct executive domains: abstract reasoning, motor
programming, and cognitive flexibility. Addictive Behaviors. 35: 875–881.
Cuttler C, McLaughlin RJ, Graf P (2012) Mechanisms underlying the link between
cannabis use and prospective memory. PLoS One. 7:e36820.
de la Garza R, Johanson CE (1987) The effects of food deprivation on the self-
administration of psychoactive drugs. Drug Alcohol Depend. 19: 17– 27.
42
De Luca MA, Valentini V, Bimpisidis Z, Cacciapaglia F, Caboni P, Di Chiara G (2014)
Endocannabinoid 2-arachidonoylglycerol self-administration by Sprague-Dawley
rats and stimulation of in vivo dopamine transmission in the nucleus accumbens
shell. Front Psychiatr. 5: 140.
De Luca MA, Bimpisidis Z, Melis M, Marti M, Caboni P, Valentini V, Margiani G, Pintori
N, Polis I, Marsicano G, Parsons LH, Di Chiara G (2015) Stimulation of in vivo
dopamine transmission and intravenous self-administration in rats and mice by
JWH-018, a Spice cannabinoid. Neuropharmacology. 99: 705-14.
Deneau G, Yanagita T, Seevers MH (1969) Self-administration of psychoactive
substances by the monkey-a measure of psychological dependence.
Psychopharmacologia. 16: 30–48.
Deneau GA, Kaymakcalan S (1971) Physiological and psychological dependence to
synthetic delta-9-tetrahydrocannabinol (THC) in rhesus monkeys.
Pharmacologist. 246.
Deroche V, Le Moal M, Piazza PV (1999) Cocaine self-administration increases the
incentive motivational properties of the drug in rats. Eur J Neurosci 11: 2731–
2736.
Elsohly MA, Slade D (2005) Chemical constituents of marijuana: the complex mixture of
natural cannabinoids. Life Sci. 78: 539-548.
Everitt BJ, Hutcheson DM, Ersche KD, Pelloux Y, Dalley JW, and Robbins TW (2007)
The orbital prefrontal cortex and drug addiction in laboratory animals and
humans. Annals of the New York Academy of Sciences. 112: 576–597.
Fals-Stewart W (1993) Neurocognitive defects and their impact on substance abuse
treatment. Journal of Addictions & Offender Counseling. 13: 46–57.
Fan F, Compton DR, Ward S, Melvin L, Martin BR (1994) Development of cross-
43
tolerance between delta 9-tetrahydrocannabinol, CP 55,940 and WIN 55,212. J
Pharmacol Exp Ther. 271: 1383–1390.
Fattore L, Cossu G, Martellotta CM, Fratta W (2001) Intravenous self- administration of
the cannabinoid CB1 receptor agonist WIN 55, 212-2 in rats. Psychopharmacol
(Berl). 156: 410–416 .
Ferraro DP and Grilly DM (1974) Effects of chronic exposure to delta-9-
tetrahydrocannabinol on delayed matching-to-sample in chimpanzees.
Psychopharmacologia. 37: 127–138.
Fisk JE and Montgomery C (2008) Real-world memory and executive processes in
cannabis users and non-users. Journal of Psychopharmacology 22: 727-736.
Fried PA, Watkinson B, Gray R (2005) Neurocognitive consequences of marihuana-a
comparison with pre-drug performance. Neurotoxicol Teratol. 27:231-239.
Ghozland S, Mathews H, Simonin F, Filliol D, Kieffer BL, Maldonado R (2002)
Motivational effects of cannabinoids are mediated by µ- and κ-opioid receptors. J
Neurosci 22: 1146–1154.
Glass M, Dragunow M, and Faull RL (1997) Cannabinoid receptors in the human brain:
a detailed anatomical and quantitative autoradiographic study in the fetal,
neonatal and adult human brain. Neuroscience. 77: 299–318.
Gold LH, Balster RL, Barrett RL, Britt DT, Martin BR (1992) A comparison of the
discriminative stimulus properties of delta 9-tetrahydrocannabinol and CP 55,940
in rats and rhesus monkeys. J Pharmacol Exp Ther. 262: 479-86.
Goldberg SR, Spealman RD, Risner ME, Henningfield JE (1983) Control of behavior by
intravenous nicotine injections in laboratory animals. Pharmacol Biochem
Behav.. 19: 1011-20.
44
Goldstein RZ, Volkow ND (2011) Dysfunction of the prefrontal cortex in addiction:
neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12: 652-669.
Gonzalez, R (2007) Acute and non-acute effects of cannabis on brain functioning and
neuropsychological performance. Neuropsychology Review. 17: 347–361.
Gonzalez R, Schuster RM, Mermelstein RJ, Vassileva J, Martin EM, Diviak KR (2012)
Performance of young adult cannabis users on neurocognitive measures of
impulsive behavior and their relationship to symptoms of cannabis use disorders.
J Clin Exp Neuropsychol. 34:962-76.
Gould TJ (2010) Addiction and cognition. Addict. Sci. Clin. Pract. 5: 4–14.
Grant I, Gonzalez R, Carey CL, et al. (2003) Non-acute (residual) neurocognitive effects
of cannabis use: A meta-analytic study. J Int Neuropsychol Soc. 9:679–689.
Grant JE, Chamberlain SR, Schreiber L, and Odlaug BL (2011). Neuropsychological
deficits associated with cannabis use in young adults. Drug and Alcohol
Dependence. 121: 159–162.
Griffiths RR, Roache JD, Ator NA, Lamb RJ, Lukas SE (1985) Similarities in reinforcing
and discriminative stimulus effects of diazepam, triazolam, and pentobarbital in
animals and humans. In: Seiden LS, Balster RL (eds) Behavioral pharmacology:
the current status. Liss, New York. 419–432.
Gruber SA and Yurgelun-Todd DA (2005) Neuroimaging of marijuana smokers during
inhibitory processing: a pilot investigation. Brain Res Cogn Brain Res. 23: 107–
118.
Gruber SA, Sagar KA, Dahlgren MK, Racine M, Lukas SE (2012) Age of onset of
marijuana use and executive function. Psychol Addict Behav. 26:496-506.
Haertzen CA, Kocher TR, Miyasato K (1983) Reinforcements from the first drug
experience can predict later drug habits and/or addiction: results with coffee,
cigarettes, alcohol, barbiturates, minor and major tranquilizers, stimulants,
45
marijuana, hallucinogens, heroin, opiates and cocaine. Drug Alcohol Depend 11:
147–165.
Hall W, Degenhardt L (2009) Adverse health effects of non- medical cannabis use.
Lancet 374: 1383–1391.
Han CJ, and Robinson JK (2001) Cannabinoid modulation of time estimation in the rat.
Behavioral Neuroscience. 115: 243– 246.
Haney M, Comer SD, Ward AS, Foltin RW, Fischman MW (1997) Factors influencing
marijuana self-administration by humans. Behav Pharmacol 8: 101–112.
Haney M, Spealman R (2008) Controversies in translational research: drug self-
administration. Psychopharmacology. 199: 403-419.
Hanson KL, Winward JL, Schweinsburg AD, Medina KL, Brown SA, Tapert SF (2010)
Longitudinal study of cognition among adolescent marijuana users over three
weeks of abstinence. Addict Behav. 35:970-976.
Harris RT, Waters W, McLendon D (1974) Evaluation of reinforcing capability of D9-
tetrahydrocannabinol in monkeys. Psychopharmacologia. 37: 23–29.
Hart CL, van Gorp W, Haney M, et al. (2001) Effects of acute smoked marijuana on
complex cognitive performance. Neuropsychopharmacology. 25: 757–765.
Hart CL, Ilan AB, Gevins A, Gunderson EW, Role K, Colley J, et al. (2010)
Neurophysiological and cognitive effects of smoked marijuana in frequent users.
Pharmacology, Biochemistry, and Behavior. 96: 333–341.
Harvey MA, Sellman JD, Porter RJ, Frampton CM (2007) The relationship between non-
acute adolescent cannabis use and cognition. Drug Alcohol Rev. 26:309-319.
Heishman SJ, Stitzer ML, Yingling JE (1989) Effects of tetrahydrocannabinol content on
marijuana smoking behavior, subjective reports, and performance. Pharmacol
Biochem Behav. 34:1 73–179.
46
Herkenham M, Lynn AB, Little MD, Johnson MR, Melvin LS, de Costa BR et al. (1990)
Cannabinoid receptor localization in brain. Proc Natl Acad Sci USA. 87: 1932–
1936.
Hermann D, Sartorius A, Welzel H, Walter S, Skopp G, Ende G, et al. (2007)
Dorsolateral prefrontal cortex N acetylaspartate/total creatine (NAA/tCr) loss in
male recreational cannabis users. Biol Psychiatry 61: 1281-1290.
Herning RI, Hooker WD, Jones RT (1986) Tetrahydrocannabinol content and differences
in marijuana smoking behavior. Psychopharmacology (Berl). 90: 160–162.
Herzig DA, Nutt DJ, Mohr C (2014) Alcohol and relatively pure cannabis use, but not
schizotypy, are associated with cognitive attenuations. Front Psychiatry. 5: 133.
Hettema J, Steele J, Miller WR (2005) Motivational interviewing. Annu. Rev. Clin.
Psychol. 1. 91-111.
Higgins ST, Delaney DD, Budney AJ, Bickel WK, Hughes JR, Foerg F, Fenwick JW
(1991) A behavioral approach to achieving initial cocaine abstinence. Am. J.
Psychiatry. 148. 1218-1224.
Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S (1981) Do
plasma concentrations of delta 9-tetrahydrocannabinol reflect the degree of
intoxication? J Clin Pharmacol. 21: 171S-177S.
Jacobsen, LK, Mencl, WE, Westerveld, M, and Pugh, KR (2004) Impact of cannabis use
on brain function in adolescents. Annals of the New York Academy of Sciences.
1021: 384–390.
Jacobsen LK, Pugh KR, Constable RT, Westerveld M, and Mencl WE (2007) Functional
correlates of verbal memory deficits emerging during nicotine withdrawal in
abstinent adolescent cannabis users. Biological Psychiatry. 61: 31–40.
47
Jager G, Kahn RS, Van Den Brink W, Van Ree JM, and Ramsey NF (2006) Long-term
effects of frequent cannabis use on working memory and attention: an fMRI
study. Psychopharmacology. 185: 358–368.
Jager G, Block RI, Luijten M, and Ramsey NF (2010) Cannabis use and memory brain
function in adolescent boys: a cross- sectional multicenter functional magnetic
resonance imaging study. Journal of the American Academy of Child and
Adolescent Psychiatry. 49: 561–572.
Järbe TU, Lamb RJ, Lin S, Makriyannis A (2001) (R)-methanan- damide and Delta 9-
THC as discriminative stimuli in rats: tests with the cannabinoid antagonist SR-
141716 and the endoge- nous ligand anandamide. Psychopharmacology 156:
369–380.
Jentsch JD, Pennington ZT (2014) Reward, interrupted: Inhibitory control and its
relevance to addictions. Neuropharmacology 76: 479-486.
Joel D, Weiner I (2000) The connections of dopaminergic system with the striatum in
rats and primates: an analysis with respect to the functional and compartmental
organization of the striatum. Neuroscience. 96: 451–474.
Johnston LD, et al. (2014) Monitoring the Future: National Survey Results on Drug Use,
1975 2014-Overview, Key Findings on Adolescent Drug Use. (Institute for Social
Research, University of Michigan, Ann Arbor) Available at
http://monitoringthefuture.org//pubs/monographs/mtf-vol1_2014.pdf.
Justinova Z, Tanda G, Redhi GH, Goldberg SR (2003) Self-administration of delta(9)
tetrahydrocannabinol (THC) by drug naive squirrel monkeys.
Psychopharmacology (Berl). 169: 135–140.
Justinova Z, Munzar P, Panlilio LV, Yasar S, Redhi GH, Tanda G, Goldberg SR (2008)
Blockade of THC-seeking behavior and relapse in monkeys by the cannabinoid
48
CB(1)-receptor antagonist rimonabant. Neuropsychopharmacology. 33: 2870–
2877.
Justinova Z, Yasar S, Redhi GH, Goldberg SR (2011) The endogenous cannabinoid 2-
arachidonoylglycerol is intravenously self- administered by squirrel monkeys. J
Neurosci: Off J Soc Neurosci. 31: 7043–7048.
Justinova Z, Mascia P, Wu HQ, Secci ME, Redhi GH, Panlilio LV, Scherma M, Barnes
C, Parashos A, Zara T, Fratta W, Solinas M, Pistis M, Bergman J, Kangas BD,
Ferre S, Tanda G, Schwarcz R, Goldberg SR (2013) Reducing cannabinoid
abuse and preventing relapse by enhancing endogenous brain levels of
kynurenic acid. Nat Neurosci. 16:1652–1661.
Kanayama G, Rogowska J, Pope HG, Gruber SA, Yurgelun-Todd DA (2004) Spatial
working memory in heavy cannabis users: a functional magnetic resonance
imaging study. Psychopharmacology. 176: 239-247.
Kangas BD, Leonard MZ, Shukla VG, Alapafuja SO, Nikas SP, Makriyannis A, Bergman
J (2016) Comparisons of Δ9-Tetrahydrocannabinol and Anandamide on a Battery
of Cognition-Related Behavior in Nonhuman Primates. J Pharmacol Exp Ther.
357: 125-133.
Kaymakcalan S (1972) Physiology and psychological dependence on THC in rhesus
monkeys. In: Paton WDM, Crown J (eds) Cannabis and its derivatives. Oxford
University Press, London. 142–149.
Kaymakcalan S (1973) Tolerance to and dependence on cannabis. Bull Narc 25:39–47.
Kelly TH, Foltin RW, Emurian CS, Fischman MW (1994) Effects of delta 9-THC on
marijuana smoking, dose choice, and verbal report of drug liking. J Exp Anal
Behav.. 61: 203–11.
49
Kelly TH, Foltin RW, Emurian CS, Fischman MW (1997) Are choice and self-
administration of marijuana related to delta 9-THC content? Exp Clin
Psychopharmacol. 5: 74–82.
Kirk JM, De Wit H (1999) Responses to oral delta9-tetrahydro- cannabinol in frequent
and infrequent marijuana users. Phar- macol Biochem Behav. 63: 137–142.
Lecca D, Cacciapaglia F, Valentini V, Di Chiara G (2006) Monitoring extracellular
dopamine in the rat nucleus accumbens shell and core during acquisition and
maintenance of intravenous WIN 55,212-2 self-administration. Psychopharmacol
(Berl). 188:63–74.
Ledent C, Valverde O, Cossu G, Petitet F, Aubert JF, Beslot F, Bohme GA, Imperato A,
Pedrazzini T, Roques BP, Vassart G, Fratta W, Parmentier M (1999)
Unresponsiveness to cannabinoids and re- duced addictive effects of opiates in
CB1 receptor knockout mice. Science. 283: 401–404.
Lefever, TW, Marusich, JA, Antonazzo, KR, and Wiley, JL (2014). Evaluation of WIN
55,212-2 self-administration in rats as a potential cannabinoid abuse liability
model. Pharmacol. Biochem. Behav.. 118: 30–35.
Leite JR, Carlini EA (1974) Failure to obtain “cannabis directed behavior” and abstinence
syndrome in rats chronically treated with cannabis sativa extracts.
Psychopharmacologia. 36: 133–145.
Lepore M, Vorel SR, Lowinson J, Gardner EL (1995) Conditioned place preference
induced by Δ9-tetrahydrocannabinol: comparison with cocaine, morphine, and
food reward. Life Sci. 56: 2073–2080.
Li CS, Sinha R, (2008) Inhibitory control and emotional stress regulation: neuro- imaging
evidence for frontal-limbic dysfunction in psycho-stimulant addiction. Neurosci.
Biobehav. Rev. 32: 581-597.
50
Li J-X, Koek W, France CP (2012) Interactions between Δ(9)-tetrahydrocannabinol and
heroin: self-administration in rhesus monkeys. Behav Pharmacol. 23: 754–761.
Lindgren JE, Ohlsson A, Agurell S, Hollister L, Gillespie H (1981) Clinical effects and
plasma levels of delta 9-tetrahydrocannabinol (delta 9-THC) in heavy and light
users of cannabis. Psychopharmacology. 74: 208–212.
Lupica CR, Riegel AC, Hoffman AF (2004) Marijuana and cannabinoid regulation of
brain reward circuits. Br J Pharmacol. 143: 227–234.
Lyons MJ, Bar JL, Panizzon MS, et al. (2004) Neuropsychological consequences of
regular marijuana use: A twin study. Psychol Med. 34: 1239 –1250.
Magill M, Ray LA (2009) Cognitive-behavioral treatment with adult alcohol and illicit drug
users: a meta-analysis of randomized controlled trials. J Stud Alcohol Drugs.
70:516-527.
Maldonado R (2002) Study of cannabinoid dependence in animals. Pharmacol Ther. 95:
153–64.
Mallet PE, Beninger RJ (1998) Delta 9-tetrahydrocannabinol, but not the endogenous
cannabinoid receptor ligand anandamide, produces conditioned place avoidance.
Life Sci. 62: 2431–2439.
Mansbach RS, Nicholson KL, Martin BR, Balster RL (1994) Failure of Δ9-
tetrahydrocannabinol and CP 55,940 to maintain intravenous self administration
under a fixed-interval schedule in rhesus monkeys. Behav Pharmacol. 5: 219–
225.
Mansbach RS, Rovetti CC, Winston EN, Lowe JA III (1996) Effects of the cannabinoid
CB1 receptor antagonist SR141716A on the behavior of pigeons and rats.
Psychopharmacology. 124: 315–322.
Mantilla-Plata B, Harbison RD (1975) Distribution studies of (14C) delta-9-
51
tetrahydrocannabinol in mice: effect of vehicle, route of administration, and
duration of treatment. Toxicol Appl Pharmacol. 34: 292–300.
Martellotta MC, Cossu G, Fattore L, Gessa GL, Fratta W (1998) Self- administration of
the cannabinoid receptor agonist WIN 55,212-2 in drug-naive mice.
Neuroscience. 85: 327–330.
McDonald, J, Schleifer, L, Richards, JB, and de Wit, H (2003) Effects of THC on
behavioral measures of impulsivity in humans. Neuropsychopharmacology 28:
1356-1365.
McMahon LR (2006) Characterization of cannabinoid agonists and apparent pA2
analysis of cannabinoid antagonists in rhesus monkeys discriminating delta9-
tetrahydrocannabinol. J Pharmacol Exp Ther. 319: 1211-1218.
McMahon LR (2011) Chronic Δ⁹-tetrahydrocannabinol treatment in rhesus monkeys:
differential tolerance and cross-tolerance among cannabinoids. Br J Pharmacol..
162: 1060-1073.
McKinney DL, Cassidy MP, Collier LM, Martin BR, Wiley JL, Selley DE et al. (2008)
Dose-related differences in the regional pattern of cannabinoid receptor
adaptation and in vivo tolerance development to D9-tetrahydrocannabinol. J
Pharmacol Exp Ther. 324: 664–673.
McGregor IS, Issakidis CN, Prior G (1996) Aversive effects of the synthetic cannabinoid
CP 55,940 in rats. Pharmacol Biochem Behav. 53: 657–664.
McMillan DE, Harris LS, Frankenheim JM, Kennedy JS (1970). l-D9-trans-
tetrahydrocannabinol in pigeons: tolerance to the behavioral effects. Science.
169: 501–503.
Medina KL, Hanson KL, Schweinsburg AD, Cohen-Zion M, Nagel BJ, Tapert SF (2007)
Neuropsychological functioning in adolescent marijuana users: subtle deficits
52
detectable after a month of abstinence. J Int Neuropsychol Soc. 13: 807-820.
Miczek KA (1979) Chronic D9-tetrahydrocannabinol in rats: effect on social interactions,
mouse killing, motor activity, consummatory behavior, and body temperature.
Psychopharmacology. 60: 137–146.
Miller WR (1985) Motivation for treatment: a review with special emphasis on alcoholism.
Psychol. Bull. 98. 84-107.
National Drug Intelligence Center (2011) The Economic Impact of Illicit Drug Use on
American Society. Washington, DC: United States Department of Justice.
Nordstrom BR, Levin FR (2007) Treatment of cannabis use disorders: A review of the
literature. Am J Addict. 16: 331–342.
Ohlsson A, Lindgren JE, Wahlen A, Agurell S, Hollister LE, Gillespie HK (1980) Plasma
delta-9 tetrahydrocannabinol concentrations and clinical effects after oral and
intravenous administration and smoking. Clin Pharmacol Ther. 28: 409–416.
Padula CB, Schweinsburg AD, and Tapert SF (2007). Spatial working memory
performance and fMRI activation interaction in abstinent adolescent marijuana
users. Psychology of Addictive Behaviors. 21: 478–487.
Perez-Reyes M, Di Guiseppi S, Davis KH, Schindler VH, Cook CE (1982) Comparison of
effects of marihuana cigarettes to three different potencies. Clin Pharmacol Ther.
31: 617–624.
Perez-Reyes M, White WR, McDonald SA, Hicks RE, Jeffcoat AR, Cook CE (1991) The
pharmacologic effects of daily marijuana smoking in humans. Pharmacol
Biochem Behav. 40: 691–694.
Perio A, Rinaldi-Carmona M, Maruani J, Barth F, Le Fur G, Soubrie P (1996) Central
mediation of the cannabinoid cue: activity of a selective CB1 antagonist, SR
141716A. Behav Pharmacol. 7: 65–71.
53
Petry NM (2006) Contingency management treatments. Br. J. Psychiatry 189. 97-98.
Pickens R, Thompson T, Muchow DC (1973) Cannabis and phencyclidine self-
administered by animals. In: Goldfarb L, Hoffmeister F (eds) Psychic
dependence [Bayer-Symposium IV]. Springer, Berlin Heidelberg New York. 78–
86.
Pieper WA (1976) Great apes and rhesus monkeys as subjects for psycho-
pharmacological studies of stimulants and depressants. Fed Proc. 35: 2254–
2257.
Pope HG, Jr, Yurgelun-Todd D (1996) The residual cognitive effects of heavy marijuana
use in college students. JAMA. 275: 521-527.
Pope HG Jr, Gruber AJ, Hudson JI, et al. (2001) Neuropsychological performance in
long-term cannabis users. Arch Gen Psychiatry. 58: 909–915.
Pope HG Jr, Gruber AJ, Hudson JI, et al. (2002) Cognitive measures in long-term
cannabis users. J Clin Pharmacol. 42: 41S–47S.
Pope HG, Gruber AJ, Hudson JI, et al. (2003) Early-onset cannabis use and cognitive
deficits: What is the nature of the association? Drug Alcohol Depend. 69: 303–
310.
Porrino LJ, Smith HR, Nader MA, Beveridge TJ, (2007) The effects of cocaine: a shifting
target over the course of addiction. Prog. Neuropsychopharmacol. Biol.
Psychiatry. 31: 1593-1600.
Prescott WR, Gold LH, Martin BR (1992) Evidence for separate neuronal mechanisms
for the discriminative stimulus and catalepsy induced by delta 9-THC in the rat.
Psychopharmacology (Berl). 107: 117-124.
Ramaekers JG, Kauert G, van Ruitenbeek P, Theunissen EL, Schneider E, Moeller MR
(2006) High-potency marijuana impairs executive function and inhibitory motor
54
control. Neuropsychopharmacology. 31: 2296-2303.
Rehm J, Mathers C, Popova S, Thavorncharoensap M, Teerawattananon Y, Patra J
(2009) Global burden of disease and injury and economic cost attributable to
alcohol use and alcohol-use disorders. Lancet. 373: 2223-2233.
Rupniak NM, Samson NA, Steventon MJ, et al. (1991) Induction of cogni- tive
impairment by scopolamine and noncholinergic agents in rhesus monkeys. Life
Sci 48: 893–899.
SAMSHA (20103) Results from the 2013 National Survey on Drug Use and Health:
National Findings. Rockville, MD: Office of Applied studies, DHHS.
Sanudo-Pena MC, Tsou K, Delay ER, Hohman AG, Force M, Walker JM (1997)
Endogenous cannabinoids as an aversive or counter-rewarding system in the rat.
Neurosci Lett. 223:125–128.
Schulze GE, McMillan DE, Bailey JR, et al. (1988) Acute effects of delta-9-
tetrahydrocannabinol in rhesus monkeys as measured by performance in a
battery of complex operant tests. J Pharmacol Exp Ther. 245: 178–186.
Schulze GE, McMillan DE, Bailey JR, et al. (1989) Acute effects of marijuana smoke on
complex operant behavior in rhesus monkeys. Life Sci 45: 465–475.
Schuster CR, Johanson CE (1988) Relationship between the discriminative stimulus
properties and subjective effects of drugs. Psychopharmacol Ser. 4: 161-175.
Schweinsburg AD, Schweinsburg BC, Medina KL, McQueeny T, Brown SA, and Tapert
SF (2010) The influence of recency of use on fMRI response during spatial
working memory in adolescent marijuana users. Journal of Psychoactive Drugs.
42: 401–412.
Skinner BF (1938) The Behavior of Organisms. New York, NY, USA: Appleton-Century-
Crofts.
Sofuoglu, M, DeVito, EE, Waters, AJ, and Carroll, KM (2013): Cognitive Enhancement
55
as a Treatment for Drug Addictions. Neuropharmacology. 64: 452-463.
Solinas M, Scherma M, Fattore L, Stroik J, Wertheim C, Tanda G, Fratta W, Goldberg
SR (2007a) Nicotinic alpha 7 receptors as a new target for treatment of cannabis
abuse. J Neurosci. 27: 5615–5620.
Solinas M, Tanda G, Justinova Z, Wertheim CE, Yasar S, Piomelli D, Vadivel SK,
Makriyannis A, Goldberg SR (2007b) The endogenous cannabinoid anandamide
produces delta-9-tetrahydrocannabinol- like discriminative and neurochemical
effects that are enhanced by inhibition of fatty acid amide hydrolase but not by
inhibition of anandamide transport. J Pharmacol Exp Ther. 321: 370–380
Solowij N (1995) Do cognitive impairments recover following cessation of cannabis use?
Life Sciences. 56: 2119-2126.
Solowij N, Michie PT, Fox AM (1995) Differential impairments of selective attention due
to frequency and duration of cannabis use. Biol Psychiatry 37:731-739.
Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. (2002)
Cognitive functioning of long-term heavy cannabis users seeking treatment.
JAMA 287:1123-31. Erratum in: JAMA. 287:1651.
Solowij N, and Battisti R (2008) The chronic effects of cannabis on memory in humans: a
review. Current Drug Abuse Reviews. 1: 81–98.
Solowij N, Jones KA, Rozman ME, Davis SM, Ciarrochi J, Heaven PC, Lubman DI,
Yücel M (2011) Verbal learning and memory in adolescent cannabis users,
alcohol users and non-users. Psychopharmacology (Berl). 216:131-44.
Spealman RD (1979) Behavior maintained by termination of a schedule of self-
administered cocaine. Science. 204: 1231-1233.
Taffe MA (2012) Δ9-Tetrahydrocannabinol attenuates MDMA-induced hyperthermia in
rhesus monkeys. Neuroscience. 201: 125-133.
Tait RJ, Mackinnon A, Christensen H. Cannabis use and cognitive function: 8-year
56
trajectory in a young adult cohort (2011) Addiction. 106: 2195-2203.
Tanda G, Pontieri FE, Di Chiara G (1997) Cannabinoid and heroin activation of
mesolimbic dopamine transmission by a common mu1 opioid receptor
mechanism. Science. 276: 2048–2050.
Takahashi RN, Singer G (1979) Self-administration of delta 9-tetrahydrocannabinol by
rats. Pharmacol Biochem Behav.. 11: 737-740.
Takahashi RN, Singer G (1980) Effects of body weight levels on cannabis self-injection.
Pharmacol Biochem Behav. 13: 877–881.
Tanda G, Pontieri FE, Di Chiara G (1997) Cannabinoid and heroin activation of
mesolimbic dopamine transmission by a common mu opioid receptor
mechanism. Science. 276: 2048–2050.
Tanda G, Munzar P, Goldberg SR (2000) Self-administration behavior is maintained by
the psychoactive ingredient of marijuana in squirrel monkeys. Nat Neurosci. 3:
1073–107 4.
Tanda G, Goldberg SR (2003) Cannabinoids: reward, dependence, and underlying
neurochemical mechanisms--a review of recent preclinical data.
Psychopharmacology (Berl). 169: 115-134.
Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA, et al. (2007)
Functional MRI of inhibitory processing in abstinent adolescent marijuana users.
Psychopharmacology (Berl). 194: 173–183.
Thomas H (1996) A community survey of adverse effects of cannabis use. Drug Alcohol
Depend. 42: 201–207.
Thompson T, Schuster CR (1964) Morphine self-administration, food-reinforced, and
avoidance behaviors in rhesus monkeys. Psychopharmacologia. 5: 87-94.
Tzschentke TM (1998) Measuring reward with the conditioned place preference
paradigm: a comprehensive review of drug effects, recent progress and new
57
issues. Prog Neurobiol. 56: 613–672.
Valjent E, Maldonado R (2000) A behavioural model to reveal place preference to Δ9-
tetrahydrocannabinol in mice. Psychopharmacology. 147:436–438.
van de Giessen E, Weinstein JJ, Cassidy CM, Haney M, Dong Z, Ghazzaoui R, et al.
(2016) Deficits in striatal dopamine release in cannabis dependence. Mol
Psychiatry. In press.
Van Ree JM, Slangen JL, de Wied D (1978) Intravenous self- administration of drugs in
rats. J Pharmacol Exp Ther. 204: 547– 557.
Verdejo-Garcia AJ, Lo ́pez-Torrecillas F, Aguilar de Arcos F, et al (2005) Differential
effects of MDMA, cocaine, and cannabis use severity on distinctive components
of the executive functions in polysubstance users: A multiple regression analysis.
Addict Behav. 30: 89–101.
Verendeev A, Riley AL (2013) The role of the aversive effects of drugs in self-
administration: assessing the balance of reward and aversion in drug-taking
behavior. Behav Pharmacol. 24: 363-374.
Verrico CD, Gu H, Peterson ML, Sampson AR, Lewis DA (2014) Repeated Δ9-
tetrahydrocannabinol exposure in adolescent monkeys: persistent effects
selective for spatial working memory. Am J Psychiatry. 171: 416-425.
Voruganti LN, Slomka P, Zabel P, Mattar A, Awad AG (2001) Cannabis induced
dopamine release: an in-vivo SPECT study. Psychiatry Res. 107: 173–177.
Wadsworth EJ, Moss SC, Simpson SA, and Smith AP (2006) Cannabis use, cognitive
performance and mood in a sample of workers. Journal of Psychopharmacology.
20: 14–23.
Wachtel SR, ElSohly MA, Ross SA, Ambre J, de WH (2002) Comparison of the
subjective effects of delta 9-tetrahydrocannabinol and marijuana in humans.
Psychopharmacol- ogy (Berl). 161: 331–339.
58
Wang X, Dow-Edwards D, Keller E and Hurd YL (2003) Preferential limbic expression of
the cannabinoid receptor mRNA in the human fetal brain. Neuroscience. 118:
681–694.
Ward AS, Comer SD, Haney M, Foltin RW, Fischman MW (1997) The effects of a
monetary alternative on marijuana self-administration. Behav Pharmacol. 8 :275–
286.
Weeks JR (1962) Experimental morphine addiction: method for automatic intravenous
injections in unrestrained rats. Science. 138: 143–144.
Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM, et al.
(2004) Long-term heavy marijuana users make costly decisions on a gambling
task. Drug Alcohol Depend. 76: 107-11.
Wiley JL. Cannabis (1999) discrimination of "internal bliss"? Pharmacol Biochem Behav.
64: 257-60.
Wiley JL, Barrett RL, Balster RL, Martin BR (1993a) Tolerance to the discriminative
stimulus effects of delta(9)-tetrahydrocannabinol. Behav Pharmacol. 4: 581–585.
Wiley JL, Barrett RL, Britt DT, Balster RL, Martin BR (1993b) Discriminative stimulus
effects of delta 9-tetrahydrocannabinol and delta 9–11-tetrahydrocannabinol in
rats and rhesus monkeys. Neuropharmacology. 32: 359–365.
Wiley JL, Balster RL, Martin BR (1995a) Discriminative stimulus effects of anandamide
in rats. Eur J Pharmacol. 276: 49–54.
Wiley JL, Barrett RL, Lowe J, Balster RL, Martin BR (1995b) Discriminative stimulus
effects of CP 55,940 and structurally dissimilar cannabinoids in rats.
Neuropharmacology. 34: 669–676.
Wiley JL, Huffman JW, Balster RL, Martin BR (1995c) Pharmacological specificity of the
discriminative stimulus effects of Δ9-tetrahydrocannabinol in rhesus monkeys.
59
Drug Alcohol Depend. 40: 81–86.
Wiley JL, Golden KM, Ryan WJ, Balster RL, Razdan RK, Martin BR (1997) Evaluation of
cannabimimetic discriminative stimulus effects of anandamide and methylated
fluoroanandamide in rhesus monkeys. Pharmacol Biochem Behav. 58: 1139–
1143.
Wiley JL, Ryan WJ, Razdan RK, Martin BR (1998) Evaluation of cannabimimetic effects
of structural analogs of anandamide in rats. Eur J Pharmacol. 355: 113–118.
Wiley JL, Martin BR (1999) Effects of SR141716A on diazepam substitution for delta9-
tetrahydrocannabinol in rat drug discrimination. Pharmacol Biochem Behav. 64:
519–522.
Winsauer PJ, Lambert P, and Moerschbaecher JM (1999) Cannabinoid ligands and their
effects on learning and performance in rhesus monkeys. Behav Pharmacol. 10:
497–511.
Wu TC, Tashkin DP, Rose JE, Djahed B (1988) Influence of marijuana potency and
amount of cigarette consumed on marijuana smoking pattern. J Psychoactive
Drugs. 20: 43–46.
Wu X, French ED (2000) Effects of chronic delta9-tetrahydrocannabinol on rat midbrain
dopamine neurons: an electrophysiological assessment. Neuropharmacology.
39: 391–398.
Yanagita T, Deneau GA, Seevers MH (1965a) Evaluation of pharmacologic agents in the
monkey by long term intravenous self- or programmed administration. Excerpta
Medica Int Congr Ser. 87: 453–457.
Yanagita T, Deneau GA, Seevers MH (1965b) A technique for chronic intravenous
programmed or self-administration of drugs to monkeys. Proc Int Union of
Physiol Sci, Tokyo. 453–457.
Zernig G, Wakonigg G, Madlung E, Haring C, Saria A (2004) Do vertical shifts in dose-
60
response rate-relationships in operant conditioning procedures indicate
"sensitization" to "drug wanting"? Psychopharmacology (Berl). 171: 349-351.
Zimmer A, Valjent E, Konig M, Zimmer AM, Robledo P, Hahn H, Valverde O, Maldonado
R (2001) Absence of Δ-9-tetrahydrocannabinol dysphoric effects in dynorphin-
deficient mice. J Neurosci. 21: 9499–9505.
61
Chapter II
BEHAVIORAL DETERMINANTS OF CANNABINOID SELF-ADMINISTRATION IN
OLD-WORLD MONKEYS
William S. John1, Thomas J. Martin2, and Michael A. Nader1
1Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC 2Department of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, NC
The following manuscript was submitted to Neuropsychopharmacology on August 3,
2016. Stylistic variations are due to the requirements of the journal. William S. John
performed experiments, analyzed the data, and prepared the manuscript. Thomas J.
Martin and Michael A. Nader acted in an advisory and editorial capacity.
62
Abstract
Reinforcing effects of Δ9-tetrahydrocannabinol (THC), the primary active ingredient in
marijuana, as assessed with self-administration (SA), has only been established in New
World primates (squirrel monkeys). The objective of this study was to investigate some
experimental factors that may enhance intravenous SA of THC and the cannabinoid
receptor (CBR) agonist CP55,940 in Old World monkeys (rhesus and cynomolgus), a
species more closely related to humans. In one experiment, male rhesus monkeys (N=9)
were trained to respond under a fixed-ratio 10 schedule of food presentation. The effects
of CP55,940 and THC on food-maintained responding and body temperature were
determined in these subjects prior to giving them access to SA each drug. Both drugs
dose-dependently decreased food-maintained responding. CP55,940 (0.001-3.0 μg/kg)
functioned as a reinforcer in three monkeys, while THC (0.01-10 μg/kg) did not have
reinforcing effects in any subject. CP55,940 was least potent to decrease food-
maintained responding in the monkeys in whom CP55,940 functioned as a reinforcer.
Next, monkeys were administered THC daily until tolerance developed to rate-
decreasing effects. When THC SA was reexamined, it functioned as a reinforcer in three
monkeys. In a group of cocaine-experienced male cynomolgus monkeys (N=4), THC SA
was examined under a second-order schedule of reinforcement in which reinforcement
frequency is relatively independent of response rates; THC functioned as reinforcer in
two monkeys. These data suggest that sensitivity to rate-decreasing effects may not be
a critical determinant for the lack of CBR agonist SA by Old World monkeys.
Understanding individual differences in vulnerability to THC SA may lead to novel
treatment strategies for marijuana abuse.
Key Words: THC; CP55,940; Marijuana abuse; Self-administration; Rhesus monkey;
cynomolgus monkey
63
INTRODUCTION
Marijuana (Cannabis sativa) is the most commonly abused illicit drug in the
United States, with an estimated 4.2 million people that meet DSM criteria for
dependence (SAMHSA, 2013). Moreover, marijuana use has been steadily increasing
among 12th graders and the current watershed in cannabis legalization suggests that
rates of use will increase further (Johnston et al, 2014; Cerdá et al, 2012). As a result,
there is a need for more preclinical research to better understand the pharmacological,
environmental, and biological determinants of cannabis use in order to develop more
effective treatment strategies for cannabis use disorder. A major obstacle preventing
such progress has been the difficulty in demonstrating the reinforcing effects of Δ9-
tetrahydrocannabinol (THC), the main psychoactive component of marijuana, using self-
administration (SA) procedures in animals. Successful efforts have been limited to one
laboratory using squirrel monkeys (Tanda et al, 2000; Justinova et al, 2003). Every other
THC SA attempt using other nonhuman primate (NHP) species (i.e. rhesus monkeys) or
rodents has been unsuccessful (Kaymakcalan 1972, 1973; Pickens et al, 1973; Harris et
al, 1974; Leite and Carlini 1974; Carney et al, 1977; Van Ree et al, 1978; Mansbach et
al, 1994; Li et al, 2012; Lefever et al, 2014).
It is currently unknown whether previous unsuccessful attempts in rhesus
monkeys were due to methodology or a direct species difference between squirrel (New
World) and rhesus (Old World) monkeys. Thus, one goal of the present study was to
make THC available to rhesus monkeys under similar SA conditions as used in squirrel
monkeys, schedule of reinforcement and vehicle preparation. With THC being a low
efficacy, partial agonist at the CB1/CB2 receptors, the present study also evaluated the
reinforcing effects of the synthetic bicyclic cannabinoid receptor (CBR) analog,
CP55,940, a high efficacy, full CB1/CB2 agonist, in the same monkeys to allow for a
better understanding of the role of pharmacological efficacy at the CBR in SA.
64
Furthermore, the characterization of individual qualitative and quantitative differences in
CBR agonist SA was examined. One hypothesis to explain the failure of THC to function
as a reinforcer among experimental animals involves the initial rate-decreasing effects of
THC, which may mask the reinforcing effects, such that subjects less sensitive to the
rate-decreasing effects of CP55,940 and THC would have greater rates of SA of both
drugs. To test this hypothesis, the potency of both drugs to decrease food-maintained
responding was correlated with rates of SA.
The rate-decreasing effects of cannabinoids represent a variable that could be
modified to facilitate acquisition of SA. For instance, data suggest that through repeated
drug exposure, tolerance may develop to the aversive/rate-decreasing effects but not the
reinforcing effects of THC (Lepore et al, 1995; Valjent and Maldonado, 2000; Ghozland
et al, 2002; Lindgren et al, 1981; Perez-Reyes et al, 1991; Kirk and de Wit, 1999;
Sherma et al, 2016). Whether tolerance to THC-induced rate-decreasing effects will
enhance THC SA is an empirical question that has not been documented in animal
models; this was examined in the present study. We hypothesized that tolerance would
develop to the rate-decreasing effects of THC and this would result in increases in rates
of THC SA compared to the initial assessment.
Another behavioral variable that can influence drug-self-administration is the
schedule of reinforcement, especially for drugs that are not considered highly efficacious
reinforcers (Nader et al, 2015). As it relates to THC self-administration, schedules of
reinforcement where the frequency of reinforcement is relatively rate independent may
be better suited to uncover the reinforcing effects. Thus, the present study also
examined THC SA in another group of monkeys in whom responding was maintained
under a second-order schedule of reinforcement. It was hypothesized that responding
under a second-order schedule, in which behavior was maintained across long intervals
between drug injections by conditioned stimuli, would lead to THC reinforcement.
65
MATERIALS AND METHODS
Subjects. Nine individually housed adult male rhesus (Macaca mulatta) and four pair
housed adult male cynomolgus (M. fascicularis) monkeys served as subjects. Six rhesus
monkeys had histories of methamphetamine or cocaine SA (John et al, 2015a,b) but had
been abstinent for approximately four months before these experiments began. Three
rhesus monkeys did not have a SA history prior to this study (Table 1). All four
cynomolgus monkeys had extensive cocaine histories (~ 8 years) at the initiation of this
study. Each monkey was fitted with an aluminum collar (Model B008, Primate Products,
Redwood City, CA) and trained to sit in a primate restraint chair (Primate Products).
Monkeys were fed sufficient standard laboratory chow (Purina LabDiet 5045, St Louis,
MO) to maintain healthy body weights (determined by veterinary staff). Animal housing
and all experimental procedures were performed in accordance with the 2011 National
Research Council Guidelines for the Care and Use of Mammals in Neuroscience and
Behavioral Research and were approved by the Animal Care and Use Committee of
Wake Forest University.
66
Table 1. Ages, weights, and self-administration histories for rhesus monkeys
Subject Age (years) Weight (kg) Drug SA history (mg/kg)
R-1567 20 10.0 134.9 (methA)
R-1690 12 12.2 373.9 (methA)
R-1691 10 8.7 366.8 (methA)
R-1693 11 11.7 283.7 (methA)
R-1710 9 9.8 68.7 (cocaine)
R-1711 9 11.2 10.3 (cocaine)
R-1682 15 13.8 0
R-1713 9 14.4 0
R-1756 19 13.3 0
Surgery. Subjects were prepared with chronic indwelling venous catheters under
aseptic conditions as previously described (John et al, 2015a,b). All monkeys were
implanted subcutaneously with a transponder (Model IPTT-300; Bio Medic Data
Systems, Inc., Seaford, DE) to non-invasively measure body temperature.
Apparatus. Behavioral sessions were carried out in ventilated, sound-attenuating
chambers (1.5x0.74x0.76m; Med Associates, East Fairfield, VT) designed to
accommodate a primate chair. Two photo-optic switches (5 cm wide; Model 117-1007;
Stewart Ergonomics, Inc., Furlong, PA) were located on one side of the chamber with a
horizontal row of three stimulus lights positioned 14 cm above each switch. A food
receptacle was located between the switches and connected with a Tygon tube to a
pellet dispenser (Med Associates) located on the top of the chamber. A peristaltic
infusion pump (7531-10, Cole-Parmer Co., Chicago, IL) for delivering drug injections at a
67
rate of approximately 1.5 ml/10 sec was also located on the top of the chamber. White
noise was continuously present to mask extraneous noise.
Experiment 1: Self-administration of THC and CP55,940 and potential behavioral
phenotypes related to reinforcement in rhesus monkeys.
Behavioral phenotype assessments: Rhesus monkeys were trained to respond
under a fixed-ratio (FR) 10 schedule of food presentation, followed by a 60-sec time-out
(TO). Sessions began with illumination of a white stimulus light above one of two photo-
optic switches in the chamber, counterbalanced between monkeys. Ten consecutive
responses emitted on the appropriate switch delivered a 1.0-g banana-flavored food
pellet (Bio-Serv, Frenchtown, NJ) accompanied by extinction of the white light and
illumination of a red light above the food receptacle for 3 sec. Responses emitted on the
alternate switch had no programmed consequence. Sessions lasted 60 min or until 30
reinforcers were obtained, whichever occurred first. Once responding was stable (3
consecutive sessions with response rates within ±20% of the mean rate for those
sessions, with no trends), non-contingent injections of CP55,940 (1.0-10 μg/kg, i.v.) and
THC (3.0-300 μg/kg, i.v.) were administered one minute prior to operant sessions. All
doses were tested 2-3 times for each monkey; CP55,940 was tested first in all monkeys.
Body temperature was taken non-invasively using the implanted telemetry device (Bio
Medic Data Systems, Inc.) immediately prior to CP55,940 or THC administration and
then again 60-min following the start of the session.
Substitution studies: After completion of the non-contingent dose-response
curves and when stable food-maintained responding was re-established, i.v. injections of
vehicle (0.4-1.0% Tween 80 and 0.4-1.0% ethanol in sterile water) and different doses of
CP55,940 (0.001-3.0 μg/kg) and THC (0.01-10 μg/kg) were substituted for food pellets
for at least 5 sessions and until responding was stable. CP55,940 SA dose-response
68
curves were determined first in all monkeys followed by THC SA dose-response curves
with the exception of one monkey that only completed the CP55,940 dose-response
curve and another monkey that only completed the THC dose-response curve.
Completion of each FR delivered an i.v. infusion (~ 0.5 ml in 3-s), followed by a 60-s TO;
sessions ended after a maximum of 30 injections or 60 min elapsed, whichever occurred
first. There was a return to food-maintained responding for at least 3 sessions between
substitutions of different drug doses. Doses were tested in quasi-random order for each
monkey, such that the highest dose was not tested first in any monkey.
Experiment 2. Effects of chronic THC treatment on THC SA
Following determination of SA dose-response curves for both drugs, responding
was maintained by food presentation for at least 5 sessions and until stable. Next, the
ED50 for THC to decrease food-maintained responding, individually determined for each
monkey, was administered non-contingently (i.v.) one min prior to each session for at
least 3 sessions and until tolerance developed to the rate-decreasing effects. Tolerance
was said to occur when average rates of responding were within 20% of baseline rates
for three consecutive sessions. At that point, the dose for non-contingent administration
was increased between an eighth and one-half log-units, depending on the monkey, and
administered for at least 5 sessions and until tolerance developed to those rate-
decreasing effects on food-maintained responding. Once responding was stable, THC
SA dose-response curves were redetermined. During THC SA, non-contingent THC
injections (10-100 μg/kg, i.v.) were administered after SA sessions in order to avoid a
direct interference with SA but to maintain consistent daily levels of THC. Between THC
SA dose substitutions, there was a return to food-maintained responding for at least 3
sessions with THC administered non-contingently, using a dose that reduced food-
maintained responding to below 20% of baseline rates, prior to those sessions.
69
Experiment 3. THC self-administration under a second-order schedule of reinforcement
Four cynomolgus monkeys had been trained to self-administer cocaine under a
second-order fixed-interval (FI) 600-s [FR 30:S] schedule of i.v. cocaine injection. At the
beginning of the session, a white light was illuminated over one of the photo optic
switches (counterbalanced between monkeys), which served as a discriminative
stimulus. Following the completion of every 30th response (FR 30) during the 600-s FI,
the white light was extinguished and a red light was illuminated for 2-s. Once the interval
elapsed, the first FR 30 completed produced an i.v. injection of cocaine delivered over
10 s paired with the red light. Thus, the red light served as the conditioned stimulus
(CS). A 60-s TO followed each injection, during which all lights were extinguished and
responses had no scheduled consequences. Daily sessions ended after the completion
of five cycles of the schedule or 90 min elapsed, whichever occurred first.
Following the determination of a cocaine dose-response curve (3.0 – 560
μg/kg/injection), saline was substituted for the training dose of cocaine (100
μg/kg/injection) and the CS was turned off until responding was reduced to 20% of
baseline rates for 3 consecutive sessions. Next, the CS was reintroduced for at least 3
consecutive sessions during which saline was still available for SA. When responding
was stable, 3.0 μg/kg THC was substituted for at least 7 consecutive sessions and until
the overall rate of responding was stable (3 consecutive sessions with response rates
within ±20% of the mean rate for those sessions, with no trends). A dose response curve
was determined by substituting vehicle, which included presentation of the CS, and a
range of doses (1.0 – 30 μg/kg/injection) in random order separated by five consecutive
sessions of substitution of either 3.0 μg/kg THC or the dose of THC that maintained the
highest rate of responding.
70
Data analysis. For the effects of CP55,940 and THC on food-maintained responding,
the primary dependent variable was response rate (responses/second). Drug effects
were expressed as a percentage of baseline responding, which was designated as 3
consecutive sessions of stable responding prior to the start of the study. Change in body
temperature (°C) following administration of either CP55,940 or THC was determined by
subtracting the temperature recorded prior to the start of the session from the
temperature recorded at the end of the 60-min session. The potency of CP55,940 and
THC to decrease food-maintained responding was estimated by calculating the ED50
using the linear portion of the curve that crossed 50% reduction in baseline response
rate. To calculate the relative potency of both drugs to decrease body temperature, the
dose decreasing temperature by 0.5 °C was estimated by interpolation of the data.
For SA, the primary dependent variables were the number of reinforcers earned
per session and mean rate of responding. Data were expressed as the mean (± S.D.)
over the last three sessions for each drug dose. Dose-response curves for THC and
CP55,940 were analyzed using one-way repeated-measures ANOVA; significant main
effect was followed by Dunnett’s multiple comparisons post-hoc tests. The criterion for
significance was set a priori at p<0.05. For Experiment 1, Pearson’s correlation analysis
was used to examine the relationship between reinforcing effects and sensitivity to rate-
decreasing and hypothermic effects. For this analysis, reinforcing effects were quantified
by calculating the area under the curve; sensitivity to rate-decreasing effects and
hypothermic effects were quantified by the CP55940 and THC ED50 values to decrease
food-maintained responding and the slope of the regression lines of temperature change
relative to vehicle, respectively.
Drugs. Δ9-tetrahydrocannabinol (THC) and CP55,940, both obtained from the National
Institute on Drug Abuse (NIDA), Bethesda, MD, were dissolved in a vehicle containing
71
0.001–4% Tween 80 and 0.001–4.0 % ethanol in sterile water. (−)-Cocaine HCl (NIDA)
was dissolved in sterile 0.9% saline. Different SA doses were studied by varying the
concentration of drug available per injection. Drug doses are expressed in terms of the
salt form of cocaine.
RESULTS
Experiment 1. The mean (± S.E.M.) rate of FR food-maintained responding was
1.16±0.2 responses/second in which the maximum number of reinforcers (30) was
always earned. Although two monkeys showed increases at low THC doses, mean
response rates were dose-dependently decreased by non-contingent administration of
CP55,940 [F(5,36)=11.5; p<0.0001; Fig. 1, closed circles] and THC [F(5,33)=3.37;
p<0.05; Fig. 1, closed triangles]. The mean ED50 values (95% confidence limits) for
CP55,940 and THC to decrease food-maintained responding were 3.8 (2.1–6.8) and
59.8 (28.6–125.1) μg/kg, respectively. CP55,940 was ~16-fold more potent at
decreasing food-maintained responding than THC. Non-contingent administration of
CP55,940 and THC also produced dose-dependent reductions in body temperature,
which corresponded to the rate-decreasing effects on food-maintained responding,
although individual differences were noted (Fig. 1, open symbols). The dose of CP
55,940 and THC that produced a 0.5 °C decrease in body temperature (ED-0.5 °C value)
was estimated to be 17 and 180 μg/kg, respectively; CP55,940 was ~11-fold more
potent than THC.
72
Figure 1. Effects of CP55,940 (circles) and THC (triangles) on food-maintained
responding (filled symbols) and body temperature (open symbols) in individual rhesus monkeys (n = 9). CP55,940 and THC were administered non-contingently 1 min prior to the start of the session and body temperature was recorded prior to each injection and then again 60 min following the start of the session. Tolerance to the rate-decreasing effects of THC on food-maintained responding was developed by repeated administration across consecutive sessions (filled squares). Abscissae: Dose of CP55,940 and THC in μg/kg. Left ordinate: Mean (± S.D.) response rate
expressed as a percentage of baseline. Right ordinate: Mean (± S.D.) change in body temperature expressed in °C.
73
When CP55,940 was substituted for food pellets, reinforcing effects were
achieved in 3 of 8 rhesus monkeys (Fig. 2, closed circles, Fig. 3) as determined by
repeated-measures one-way ANOVA (R-1567: F(5,10)=21.76, p<0.0001; R-1690:
F(5,10)=13.48, p<0.001; R-1691: F(5,10)=9.07, p<0.01). The peak injections of
CP55,940 occurred at 0.04, 0.02 and 0.00125 μg/kg/injection for R-1567, R-1690 and R-
1691, respectively. CP55,940 was least potent at decreasing food-maintained
responding in the 3 monkeys in whom the drug functioned as a reinforcer (Table 2). No
relationship was found between CP55,940 reinforcing effects and CP55,940-induced
changes in body temperature.
In contrast to CP55,940, THC over a broad range of doses (0.03-10 μg/kg) did
not maintain responding above vehicle levels, even in the 3 monkeys in which CP55,940
functioned as a reinforcer (Fig. 2, closed triangles). In 4 of 8 monkeys, however, THC
produced transient reinforcing effects in which the number of injections earned was
greater than vehicle levels for 2 or more consecutive sessions (Fig. 4).
Table 2. Relationship between CP55,940 potency to decrease food-maintained
responding and CP55,940 self-administration Subject ED50 (95% confidence interval) Max injections (SD) AUC
R-1567 0.007 (0.006-0.008) 20.67 (1.16) 68.67
R-1691 0.006 (0.004-0.009) 11.67 (1.16) 39.33
R-1690 0.006 (0.004-0.009) 6.67 (0.58) 13.83
R-1693 0.0009 (NA) 1 (0) 4.5
R-1682 0.003 (0.001-0.01) 2.0 (1.73) 4.83
R-1711 0.001 (0.0006-0.002) 1.67 (0.58) 6.17
R-1713 0.003 (0.002-0.006) 1.33 (0.58) 4.33
R-1756 0.003 (0.001-0.008) 2.33 (1.53) 7.33
74
0
5
10
15
20
25
30THC
********
THC post tolerance
CP 55,940
R-1567
****
0
5
10
15
20
25
30
R-1693
V 0.001 0.01 0.1 1 10 100
0
5
10
15
20
25
30R-1711
***
*
****
R-1691
R-1756
V 0.001 0.01 0.1 1 10 100
R-1713
***
R-1690
R-1682
V 0.001 0.01 0.1 1 10 100
R-1710
***
Unit Dose (µg/kg/injection)
Nu
mb
er
of
Inje
cti
on
s
Figure 2. Self-administration dose-response curves for CP55,940 (filled circles),
THC (closed triangles) and THC after the development of tolerance to rate-decreasing effects on food-maintained responding (open triangles). Abscissae: Unit
dose (μg/kg/injection) available for self-administration. Ordinate: Number of injections earned in the 60-min session. Data represent mean ± S.D. of last three sessions. *p < 0.05, **p < 0.01 compared to vehicle.
75
Figure 3. Acquisition of CP 55,940 self-administration in individual rhesus monkeys.
Data are expressed as number of reinforcers earned across consecutive sessions. Open squares represent number of food reinforcers earned prior to substituting CP 55,940 (filled symbols) or vehicle (open circles) for self-administration.
1 2 3 4 5 6-3 -2 -1
0
5
10
15
20
25
300.0025 µg/kg CP 55,940
R-1567
0.02 µg/kg CP 55,940
Food
Vehicle
0.04 µg/kg CP 55,940
1 2 3 4 5 6 7 8 9 10-3 -2 -1
R-1691
1 2 3 4 5 6 7 8-3 -2 -1
R-1690
Nu
mb
er
of R
ein
forc
ers
Session
76
Figure 4. Transient reinforcing effects of THC in individual rhesus monkeys. Data
are expressed as number of reinforcers earned across consecutive sessions. Squares represent number of food reinforcers earned prior to substituting THC
(filled symbols) or vehicle (open circles) for self-administration.
1 2 3 4 5 6 7-3 -2 -1
0
5
10
15
20
25
30
1.0 µg/kg THC
0.1 µg/kg THC
R-1710
0.3 µg/kg THC
1.7 µg/kg THC
10 µg/kg THC
Food
Vehicle
1 2 3 4 5 6 7 8 9-3 -2 -1
R-1691
1 2 3 4 5 6 7-3 -2 -1
R-1690
1 2 3 4 5 6 7 8 9 10-3 -2 -1
0
5
10
15
20
25
30
R-1711
Session
Nu
mb
er
of
Rein
forc
ers
77
Experiment 2. Daily administration of the THC ED50 resulted in tolerance to the
rate-decreasing effects within 5.25 (±0.75 S.E.M.) sessions. This dose was increased by
an eighth to one-half log units and tolerance developed to those effects within 6.17
(±1.14 S.E.M.) sessions (closed squares, Fig. 1). Following the development of
tolerance to the rate-decreasing effects of THC, SA was reexamined and THC
functioned as a reinforcer in 3 monkeys (Fig. 2 open triangles; Fig. 5), as determined by
repeated measures one-way ANOVA (R-1710: F(3,6)=65.69; p<0.001; R-1567:
F(3,6)=95.43; p<0.0001; R-1711: F(4,8)=19.47; p<0.001).
78
Figure 5. THC functioned as a reinforcer in three monkeys after the development of
tolerance to rate-decreasing effects on food-maintained responding. Open squares represent food-maintained responding following increasing doses of daily THC treatment. The vertical line designates the development of tolerance after which THC (open triangles) and vehicle (closed triangles) were substituted for self-administration. There was a return to food-maintained responding between doses of THC and vehicle. Left ordinate: Percentage of baseline rate of food-maintained responding. Right ordinate: Number of THC or vehicle injections earned per session. Abscissae: Consecutive sessions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0
25
50
75
100
125
150
175
0
5
10
15
20
25
30
+ THC ED50
(7.0 µg/kg)
R-1710
+ 10 µg/kgTHC
Vehicle
Food
THC (3.0 µg/kg/injection)
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
0
25
50
75
100
125
150
175
200
225
0
5
10
15
20
25
30
+ THC ED50 (12 µg/kg)+ 30 µg/kg
THC
R-1711
THC (30 µg/kg/injection)
Vehicle
Food
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
0
25
50
75
100
125
150
0
5
10
15
20
25
30
+ THC ED50 (60 µg/kg) + 100 µg/kgTHC
R-1567
THC (100 µg/kg/injection)
Food
Vehicle
Resp
on
se R
ate
(%
of b
aselin
e)
Session
Nu
mb
er o
f Inje
ctio
ns
79
Experiment 3. Prior to the start of THC SA, monkeys responded under a second-
order FI 600-s (FR 30:S) schedule of cocaine reinforcement. Cocaine dose-response
curves, determined for each monkey, were represented as an inverted U-shaped
function of cocaine dose (Fig. 6, open squares). After saline substitution, with removal of
the CS, responding declined; response-contingent presentation of the CS did not
increase responding. Substituting different THC doses resulted in THC maintaining
responding at significantly higher rates than vehicle in two of four subjects (Fig. 6, closed
triangles) as determined by repeated measures one-way ANOVA (C-7427: F(4,8)=14.94,
p<0.001; C-6526: F(4,8)=41.72, p<0.001), with maximal rates maintained when 10 and
3.0 μg/kg/injection were available, respectively. All monkeys responded for the maximal
number of injections available for each dose of THC and vehicle except C-6526, who
responded for no vehicle injections and 0.67 (± 0.58 S.D.) and 2.67 (±1.53 S.D.) THC
injections at 1.0 and 30 μg/kg/injection, respectively.
80
Figure 6. THC self-administration under a second-order [FI 600-s (FR 30:S)]
schedule of reinforcement. Each dose was studied for a minimum of 7 sessions and until responding was stable. Data represent mean (±S.D.) response rates for THC (filled triangles) and cocaine (open squares) during last 3 sessions of substitution. Abscissae: Unit dose (μg/kg/injection) available for self-administration. Ordinate: Mean overall rate of responding (responses/sec).
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
D9-THC
Cocaine
C-7427
***
V 1 10 100 1000
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4C-7078
C-6526
*******
*
V 1 10 100 1000
C-7084
Unit Dose (µg/kg/injection)
Resp
on
se R
ate
(re
sp
/sec)
81
DISCUSSION
One goal of the present study was to investigate cannabinoid SA in rhesus
monkeys under similar parameters successfully used to demonstrate the reinforcing
effects of cannabinoids in squirrel monkeys. We also examined behavioral phenotypes
that may mediate individual differences in SA. The CBR agonist CP55,940 functioned as
a reinforcer in 3 of 8 monkeys; reinforcing effects were inversely related to CP55,940-
induced behavioral disruption. In those same monkeys, THC did not have reinforcing
effects in any animal. Daily THC treatment with a dose that initially produced significant
rate-decreasing effects resulted in THC functioning as a reinforcer in three monkeys. In
another group of monkeys, THC functioned as a reinforcer in two of four animals
responding under a second-order schedule. Across all conditions, of the 13 monkeys
studied, CBR agonists functioned as reinforcers in 7 animals. Understanding these
individual differences may lead to novel treatments for marijuana abuse.
Studies in humans show that individuals who are more sensitive to the aversive
effects of cannabis were less likely to be current users (Haertzen et al, 1983; Thomas,
1996). The prominent aversive effects of cannabinoids have been demonstrated in
conditioned place preference studies in rodents where several studies report that THC
and synthetic CBR agonists generally induce conditioned place avoidance rather than
place preference, except following a period of extended drug exposure (Lepore et al,
1995; Valjent and Maldonado, 2000; Ghozland et al, 2002). Thus, one hypothesis for the
lack of cannabinoid SA in experimental animals is that the aversive (and rate-
decreasing) effects of THC and other CBR agonists are inhibiting measures of
reinforcement in macaques (and probably rodents) to a greater degree than is apparent
in squirrel monkeys. Results with CP55,940 supported this hypothesis; CP55,940
functioned as a reinforcer in the three monkeys least sensitive to the drug’s rate-
disruptive effects. However, in those same three monkeys, THC was also the least
82
potent to decrease rates of food-maintained responding, but THC did not function as a
reinforcer in any monkey following the initial determination.
These results suggest that there may be a relationship between CB1 receptor
agonist efficacy and reinforcing effects considering the low efficacy CB1 partial agonist
THC did not function as reinforcer in the same monkeys that the high efficacy CB1 full
agonist CP55,940 did. However, due to the large number of CB1 receptors in the central
nervous system (Gifford et al, 1999), THC often produces the same maximum effect as
that obtained with high efficacy CB1 agonists for a range of behaviors including drug
discrimination, antinociception, and locomotor activity (Fan et al, 1994; McMahon, 2011);
however, exceptions have been noted for hypothermic effects (Paronis et al., 2012). The
other exception may also include reinforcing effects. Several CBR agonist SA studies in
rodents support this notion in which the high efficacy CBR agonist WIN55,212, but not
THC, maintained behavior above vehicle levels (Martellotta et al, 1998; Fattore et al,
2001; Lefever et al, 2014).
These data also suggest a direct inter-species difference between rhesus and
squirrel monkeys as it relates to the initial reinforcing effects of THC. Consistent with our
hypothesis, the ED50 for intravenous THC to decrease food-maintained responding in
squirrel monkeys (Justinova et al, 2013) is ~1.0 log-units higher (i.e., less potent)
compared to the rhesus monkeys used in the present study. These data indicate that the
reinforcing effects of THC may only be unmasked when there is a certain degree of
insensitivity to the rate-decreasing effects, similar to what was shown in the present
study involving CP55,940 SA.
The precise mechanism underlying the rate-decreasing effects of CBR agonists,
including individual-subject variability, is uncertain. Previous studies in rats and monkeys
indicate that CB1 receptors do not exclusively mediate the rate-decreasing effects of
THC as evidenced by the inability of the prototypic CB1 antagonist SR141716A to
83
attenuate these effects (McMahon et al, 2005; Järbe et al, 2003; DeVry and Jentzsch,
2004). On the other hand, SR141716A has been reported to attenuate many of the
physiological and unconditioned behavioral effects of THC such as the hypothermic,
antinociceptive, respiratory depressant, and cardiovascular effects (McMahon et al,
2005; Vivian et al, 1998). As a result, the factors mediating individual differences in the
ability of CP55,940 to function as a reinforcer may not be related to CB1 receptor
function considering no relationship was found between reinforcing effects and
CP55,940-induced changes in body temperature. Moreover, the doses of CP55,940 that
had reinforcing effects in these subjects were approximately 1.0-log units lower than
what was previously tested in an unsuccessful attempt to obtain CP55,940
reinforcement in rhesus monkeys (Mansbach et al, 1994). These results highlight the
importance of using low doses to demonstrate the reinforcing effects of CBR agonists,
as emphasized by Goldberg colleagues (Tanda et al, 2000).
To further evaluate the relationship between sensitivity to the rate-decreasing
effects of THC and SA, we conducted two additional experiments. In one study, THC
was administered daily in order for tolerance to develop to the rate-decreasing effects of
THC. After reassessing SA, THC only functioned as a reinforcer in three animals.
Interestingly, one of these subjects (R-1567) did not completely develop tolerance to the
rate-decreasing effects prior to reassessing SA. This suggests that a period of pre-
exposure to THC, rather than the development of tolerance to rate-decreasing effects,
could be the more crucial factor for acquiring THC SA. Future studies could determine if
other experimental manipulations (e.g., lengthen TO or change the schedule to
progressive-ratio) could yield more robust THC SA. Furthermore, no observational signs
of physical withdrawal were noted during daily THC administration, which is consistent
with previous studies showing that much higher doses (e.g., 1.0 mg/kg/12 hr or 0.05-
0.17 mg/kg/hr) were required to produce physical withdrawal symptoms in rhesus
84
monkeys (Beardsley et al., 1986; Stewart and McMahon, 2010) than what was used for
the present study. It remains to be determined if physical dependence would enhance
the likelihood of THC SA.
A second method to address the role of sensitivity to rate-disruptive effects and
SA was to use a schedule of reinforcement in which response rates and reinforcement
frequency were not directly related. Two of four monkeys self-administering THC under a
second-order FI 600-s (FR30:S) schedule showed reinforcing effects at a doses similar
to those that resulted in reinforcing effects in squirrel monkeys responding under a
second order schedule (Justinova et al., 2008); there was no apparent relationship
between rates of responding maintained by cocaine and the ability for THC to have
reinforcing effects. Mansbach et al. (1994) also used an FI 600-s schedule but did not
report any evidence that THC maintained responding higher than vehicle, suggesting
that perhaps the use of conditioned stimuli may enhance the likelihood that future
investigators can obtain robust THC self-administration under similar schedule
conditions.
The failure of THC to function as a reinforcer in the majority of subjects following
the development of tolerance to rate-decreasing effects suggests other factors may be
involved. One possible explanation is that THC reinforcement was related to individual
differences in THC pharmacokinetics and/or metabolism following tolerance. However,
studies in animals and humans have shown that chronic THC exposure does not
significantly change THC metabolism or disposition (Dewey et al, 1973; McMillan et al,
1973; Siemens and Kalant, 1974; Martin et al, 1976; Hunt and Jones, 1980; Ginsburg et
al, 2014). Even so, this possibility cannot be rejected and should be investigated with
particular attention to individual differences in correspondence with other behavioral
endpoints related to reinforcement.
85
It is important to note that there was a relationship between drug history and
CBR agonist SA in that three of the four monkeys that had an extensive
methamphetamine SA history were the subjects in whom CP55,940 functioned as a
reinforcer. Although previous studies in squirrel monkeys have demonstrated that a drug
SA history is not necessary for acquiring robust levels of THC SA (Justinova et al, 2003),
the present results suggest a potential relationship as it relates to the acquisition of
CP55,940 SA. A better understanding of the importance of drug history, schedule of
reinforcement, behavioral, and physiological phenotypes related to pharmacological
sensitivity to cannabinoid SA are needed to establish a reliable preclinical model of CBR
abuse.
86
ACKNOWLEDGEMENTS. This work was supported by the National Institutes of Health
grants R37 DA10584 (MAN), P50 DA06634 (MAN), F31 DA041825 (WSJ) and T32 AA-
007565 (WSJ).
CONFLICT OF INTEREST. The authors declare no conflicts of interest.
CONTRIBUTIONS. WSJ, TJM, and MAN designed the studies. WSJ conducted the
experiments and analyzed the data. WSJ, TJM, and MAN wrote the manuscript.
87
REFERENCES
Beardsley PM, Balster RL, and Harris LS (1986). Dependence on tetrahydrocannabinol
in rhesus monkeys. J Pharmacol Exp Ther 239:311-319.
Carney JM, Uwaydah IM, Balster, RL (1977). Evaluation of a suspension system for
intravenous self-administration of water insoluble substances in the rhesus
monkey. Pharmacol Biochem Behav 7:357–364.
Cerdá M, Wall M, Keyes KM, Galea S, Hasin D (2012). Medical marijuana laws in 50
states: investigating the relationship between state legalization of medical
marijuana and marijuana use, abuse and dependence. Drug Alcohol Depend
120: 22–27.
Dewey WL, McMillan DE, Harris LS, Turk RF (1973). Distribution of radioactivity in brain
of tolerant and nontolerant pigeons treated with 3H-Δ9-tetrahydrocannabinol.
Biochem Pharmacol 22: 399–405.
De Vry J, Jentzsch KR (2004). Partial agonist-like profile of the cannabinoid receptor
antagonist SR141716A in a food-reinforced operant paradigm. Behav Pharmacol
15: 13 20.
Fan, F, Compton, DR, Ward, S, Melvin, L, Martin, BR, (1994). Development of cross-
tolerance between delta 9-tetrahydrocannabinol, CP 55,940 and WIN 55,212. J.
Pharmacol. Exp. Ther. 271: 1383–1390.
Fattore L, Cossu G, Martellotta CM, Fratta W (2001). Intravenous self-administration of
the cannabinoid CB1 receptor agonist WIN 55,212-2 in rats.
Psychopharmacology 156: 410–416.
Ghozland S, Mathews H, Simonin F, Filliol D, Kieffer BL, Maldonado R (2002).
Motivational effects of cannabinoids are mediated by - and k-opioid receptors. J
Neurosci 22: 1146– 1154.
Gifford AN, Bruneus M, Lin S, Goutopoulos A, Makriyannis A, Volkow ND, Gatley SJ
88
(1999). Potentiation of the action of anandamide on hippocampal slices by the
fatty acid amide hydrolase inhibitor, palmitylsulphonyl fluoride (AM 374). Eur J
Pharmacol 383: 9–14.
Ginsburg BC, Hruba L, Zaki A, Javors MA, McMahon LR (2014). Blood levels do not
predict behavioral or physiological effects of Δ9-tetrahydrocannabinol in rhesus
monkeys with different patterns of exposure. Drug Alcohol Depend 139: 1-8.
Haertzen CA, Kocher TR, Miyasato K (1983). Reinforcements from the first drug
experience can predict later drug habits and/or addiction: results with coffee,
cigarettes, alcohol, barbiturates, minor and major tranquilizers, stimulants,
marijuana, hallucinogens, heroin, opiates and cocaine. Drug Alcohol Depend 11:
147–165.
Harris RT, Waters W, McLendon D (1974). Evaluation of reinforcing capability of 9-
tetrahydrocannabinol in monkeys. Psychopharmacologia 37:23–29
Hunt CA, Jones RT (1980). Tolerance and disposition of tetrahydrocannabinol in man. J
Pharmacol Exp Ther 215: 35–44.
Järbe TU, Lamb RJ, Liu Q, Makriyannis A (2003). (R)-Methanandamide and delta9-
tetrahydrocannabinol-induced operant rate decreases in rats are not readily
antagonized by SR-141716A. Eur J Pharmacol 466: 121–127.
John WS, Banala AK, Newman AH, Nader MA (2015a). Effects of buspirone and the
dopamine D3 receptor compound PG619 on cocaine and methamphetamine self-
administration in rhesus monkeys using a food-drug choice
paradigm.Psychopharmacology (Berl). 232: 1279-89.
John WS, Newman AH, Nader MA (2015b). Differential effects of the dopamine D3
receptor antagonist PG01037 on cocaine and methamphetamine self-
administration in rhesus monkeys. Neuropharmacology. 92: 34-43.
Johnston LD, et al (2014). Monitoring the Future: National Survey Results on Drug Use,
89
1975 2014-Overview, Key Findings on Adolescent Drug Use. (Institute for Social
Research, University of Michigan, Ann Arbor) Available at
http://monitoringthefuture.org//pubs/monographs/mtf-vol1_2014.pdf.
Justinova Z, Tanda G, Redhi GH, Goldberg SR (2003). Self-administration of delta(9)-
tetrahydrocannabinol (THC) by drug naive squirrel monkeys.
Psychopharmacology 169: 135-140.
Justinova Z, Munzar P, Panlilio LV, Yasar S, Redhi GH, Tanda G, Goldberg SR (2008).
Blockade of THC-seeking behavior and relapse in monkeys by the cannabinoid
CB(1)-receptor antagonist rimonabant. Neuropsychopharmacology 33: 2870-
2877.
Justinova Z, Mascia P, Wu H-Q, Secci ME, Redhi GH, Panlilio L et al (2013). Reducing
cannabinoid abuse and preventing relapse by enhancing endogenous brain
levels of kynurenic acid. Nat Neurosci 16: 1652–1661.
Kaymakcalan S. (1972). Physiology and psychological dependence on THC in rhesus
monkeys. In: Paton WDM, Crown J (eds) Cannabis and its derivatives. Oxford
University Press, London 142–149.
Kirk JM, De Wit H (1999). Responses to oral delta9-tetrahydrocannabinol in frequent
and infrequent marijuana users. Pharmacol Biochem Behav 63: 137–142.
Lefever, TW, Marusich, JA, Antonazzo, KR, and Wiley, JL (2014). Evaluation of WIN
55,212-2 self-administration in rats as a potential cannabinoid abuse liability
model. Pharmacol. Biochem. Behav. 118: 30–35.
Leite JR, Carlini EA (1974). Failure to obtain “cannabis directed behavior” and
abstinence syndrome in rats chronically treated with cannabis sativa extracts.
Psychopharmacologia 36: 133– 145.
Lepore M, Vorel SR, Lowinson J, Gardner EL (1995). Conditioned place preference
90
induced by D9-tetrahydrocannabinol: comparison with cocaine, morphine, and
food reward. Life Sci 56: 2073–2080.
Li J-X, Koek W, France CP (2012). Interactions between Δ(9)-tetrahydrocannabinol and
heroin: self-administration in rhesus monkeys. Behav Pharmacol 23: 754–761.
Lindgren JE, Ohlsson A, Agurell S, Hollister L, Gillespie H (1981). Clinical effects and
plasma levels of delta 9-tetrahydrocannabinol (delta 9-THC) in heavy and light
users of cannabis. Psychopharmacology 74: 208–212.
Mansbach RS, Nicholson KL, Martin BR, Balster RL (1994). Failure of Δ9-
tetrahydrocannabinol and CP 55,940 to maintain intravenous self administration
under a fixed-interval schedule in rhesus monkeys. Behav Pharmacol 5: 219–
225.
Martellotta MC, Cossu G, Fattore L, Gessa GL, Fratta W (1998). Self-administration of
the cannabinoid receptor agonist WIN 55,212–2 in drug-naive mice.
Neuroscience 85: 327–330.
Martin BR, Dewey WL, Harris LS, Beckner JS (1976). 3H-Δ(9)-tetrahydrocannabinol
tissue and subcellular distribution in the central nervous system and tissue
distribution in peripheral organs of tolerant and nontolerant dogs. J Pharmacol
Exp Ther 196: 128–144.
McMahon LR, Amin MR, and France CP (2005). SR 141716A differentially attenuates
the behavioral effects of delta9-THC in rhesus monkeys. Behav. Pharmacol. 16:
363–372.
McMahon LR (2011). Chronic delta9-tetrahydrocannabinol treatment in rhesus monkeys:
differential tolerance and cross-tolerance among cannabinoids. Br. J. Pharmacol.
162: 1060–1073.
McMillan DE, Dewey WL, Turk RF, Harris LS, McNeil JH (1973). Blood levels of 3H-D9-
tetrahydrocannabinol and its metabolites in tolerant and nontolerant pigeons.
91
Biochem Pharmacol 22: 383–397.
Nader MA, Lile JA, John WS, Czoty PW (2015). Nonhuman primate models of abuse
liability. In GF Weinbauer, F Vogel (Eds) “Primate Biologics Research at a
Crossroads”. Waxmann Publishing Co: Muenster, Germany, pp. 37-59.
Paronis CA, Nikas SP, Shukla VG, Makriyannis A (2012). Δ(9)-Tetrahydrocannabinol
acts as a partial agonist/antagonist in mice. Behav Pharmacol 23:802-805.
Perez-Reyes M, White WR, McDonald SA, Hicks RE, Jeffcoat AR, Cook CE (1991). The
pharmacologic effects of daily marijuana smoking in humans. Pharmacol
Biochem Behav 40: 691–694.
Pickens R, Thompson T, Muchow DC. Cannabis and phencyclidine self-administered by
animals (1973). Goldfarb L, Hoffmeister F (eds) Psychic dependence [Bayer-
Symposium IV]. Springer, Berlin Heidelberg New York, pp. 78–86.
Scherma M, Dessì C, Muntoni AL, Lecca S, Satta V, Luchicchi A, Pistis M, Panlilio LV,
Fattore L, Goldberg SR, Fratta W, Fadda P (2016). Adolescent Δ(9)-
tetrahydrocannabinol exposure alters WIN55,212-2 self-administration in adult
rats. Neuropsychopharmacology 41: 1416-1426.
Siemens AJ, Kalant H (1974). Metabolism of delta1-tetrahydrocannabinol by rats tolerant
to cannabis. Can J Physiol Pharmacol 52: 1154–11566.
Stewart JL and McMahon LR (2010). Rimonabant-induced Δ9-THC withdrawal in rhesus
monkeys: discriminative stimulus effects and other withdrawal signs. J
Pharmacol Exp Ther. 334: 347-356.
Tanda G, Munzar P, Goldberg SR (2000). Self-administration behavior is maintained by
the psychoactive ingredient of marijuana in squirrel monkeys. Nat Neurosci 3:
1073-1074.
Thomas H (1996). A community survey of adverse effects of cannabis use. Drug Alcohol
Depend 42: 201–207.
92
Valjent E, Maldonado R (2000). A behavioural model to reveal place preference to D9-
tetrahydrocannabinol in mice. Psychopharmacology 147: 436–438.
Van Ree JM, Slangen JL, de Wied D (1978). Intravenous self- administration of drugs in
rats. J Pharmacol Exp Ther 204: 547– 557.
Vivian JA, Kishioka S, Butelman ER, Broadbear J, Lee KO, Woods JH (1998).
Analgesic, respiratory and heart rate effects of cannabinoid and opioid agonists
in rhesus monkeys: antagonist effects of SR 141716A. J Pharmacol Exp Ther
286: 697–703.
93
CHAPTER III
CHRONIC Δ9-THC IN RHESUS MONKEYS: EFFECTS ON COGNITIVE
PERFORMANCE AND DOPAMINE D2/D3 RECEPTOR AVAILABILITY
William S. John1, Thomas J. Martin2, Kiran Kumar Solingapuram Sai3, Susan H. Nader1,
H. Donald Gage3, Akiva Mintz3, and Michael A. Nader1,3
1Department of Physiology and Pharmacology, Wake Forest School of Medicine,
Winston-Salem, NC
2Department of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, NC
3Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC
The following manuscript was submitted to Biological Psychiatry August, 2016. Stylistic
variations are due to the requirements of the journal. William S. John performed
experiments, analyzed the data, and prepared the manuscript. Kiran K. S. Sai, Donald
Gage, and Akiva Mintz conducted positron emission tomography studies. Susan H.
Nader assisted in the analysis of positron emission tomography data. Thomas J. Martin
and Michael A. Nader acted in an advisory and editorial capacity.
94
ABSTRACT
Background: Cannabis-related impairments to cognitive function may represent novel
therapeutic targets for cannabis-use disorder; however, the specificity, persistence, and
reversibility of those deficits remain unclear.
Methods: Each day, adult male rhesus monkeys (N=6) performed cognitive tasks and
responded under a fixed-ratio (FR)10 schedule of food presentation afterwards. After the
acute effects of Δ9-tetrahydrocannabinol (THC; 0.01-0.56 mg/kg, i.v.) on cognitive
performance and food-maintained responding were assessed, 1.0–2.0 mg/kg THC (s.c.)
was administered after FR10 sessions for 12 weeks during which the effects on
cognition, 22 hours after administration, were assessed daily. Prior to discontinuing
treatment, the acute THC dose-response curve was redetermined. Dopamine D2/D3
receptor availability was assessed after 4 weeks of chronic THC exposure and
compared to drug-naïve controls (N=4/group) using positron emission tomography and
[11C]-raclopride.
Results: Acute administration of THC dose-dependently deceased cognitive
performance and food-maintained responding. During chronic treatment, THC produced
persistent impairment 22 hrs after administration to working memory but not to
discrimination and reversal learning nor attentional set-shifting performance. Chronic
THC administration resulted in tolerance to acute rate-decreasing effects on food-
maintained responding and the acute impairments on cognitive performance. During
abstinence, there was recovery of impairments within 2 weeks. D2/D3 receptor
availability was not different in chronic THC-treated monkeys compared to control
subjects.
Conclusions: Chronic THC-induced disruptions in cognitive performance depended on
the cognitive domain, cognitive load, and time of assessment following THC
administration. These cognitive-domain specific effects of THC were not accompanied
95
by changes in dopamine D2/D3 receptor availability, which suggest that long-term
marijuana use may not produce comparable neuropharmacological adaptations as other
drugs of abuse.
Key Words: THC; CANTAB; Marijuana; Rhesus monkey; delayed-match-to sample,
PET imaging, set-shifting
96
INTRODUCTION
The disruption in cognitive function is a major consequence of drug abuse
(Jentsch and Pennington, 2014) and a factor that has been shown to predict relapse and
is associated with poorer treatment retention and outcome (Aharonovich et al., 2006,
2008; Carroll et al., 2011). As a result, cognitive enhancement may hold promise as an
adjunct treatment strategy for substance use disorders (Sofuoglu et al., 2013; Rezapour
et al., 2016).
Marijuana is the most widely abused illicit substance in the United States
(SAMHSA, 2013) and the neurocognitive effects of Δ9-tetrahydrocannabinol (THC), the
major psychoactive constituent of marijuana, have been extensively studied (Batalla et
al., 2013). Although studies in humans have shown that the acute effects of cannabis
use consistently impair executive cognitive functions such as attention (Hart et al., 2001;
D’Souza et al., 2008; Anderson et al., 2010), working memory (Heishman et al., 1997;
Hart et al., 2001, 2010;), and inhibition/impulsivity (McDonald et al., 2003; Ramaekers et
al., 2006), the residual effects (i.e. after the intoxicating effects have subsided) and
effects during abstinence, which are relevant for treatments that target cognitive
functioning, are equivocal for most cognitive domains (Crean et al., 2011; Crane et al.,
2013). Some of these disparate findings may be the result of experimental factors that
are difficult to control in studies with human subjects such as differences in time since
last use, amount and duration of cannabis use, previous drug histories, social variables,
as well as concurrent use of other substances. In addition, studies in humans cannot
distinguish whether marijuana abusers exhibit innate cognitive differences prior to
initiating drug taking so it is difficult to clearly establish a causal role of long-term THC
exposure in producing cognitive deficits. Therefore, it is critical to employ longitudinal,
within-subject studies in preclinical animal models that can control for these variables in
a systematic manner.
97
The present study used a within-subjects study design in monkeys to examine
the acute effects of THC 30-min prior to cognitive sessions and during chronic treatment,
and the effects of THC 22 hours after administration in an effort to model residual drug
effects, on multiple cognitive domains. Long-term cannabis use has also been shown to
produce differential tolerance to the acute effects of THC on cognitive function compared
to infrequent users (Kirk and de Wit, 1999; D’Souza et al., 2008; Ramaekers et al.,
2009). In the present study, the cognitive domains were 1) attention and working
memory using the delayed match to sample task, 2) associative learning using the
simple/compound discrimination task, 3) behavioral inhibition using the reversal learning
task, and 4) behavioral flexibility using dimensional set-shifting tasks.
Intact functioning within these particular cognitive domains is important for
positive treatment response as they underlie the skills necessary for behavioral change
and for reducing the likelihood of relapse during treatment (Rezapour et al., 2016);
however, studies in chronic cannabis users show differential effects within these
cognitive domains regarding the time course of impairments (Crane et al., 2013; Crean
et al., 2011). For instance, some studies report deficits in attentional abilities, learning
and memory, inhibition, and cognitive flexibility in recently abstinent (12 hr–3 d) cannabis
users (Solowij et al., 1995, 2002, 2011; Pope and Yurgelun-Todd, 1996; Harvey et al.,
2007; Hanson et al., 2010) while others do not (Whitlow et al., 2004; Kanayama et al.,
2004; Fisk and Montgomery, 2008; Grant et al., 2011). Moreover, other studies show
cognitive impairments after 28 days of abstinence or longer (Solowij et al., 1995; Bolla et
al., 2002; Jacobson et al., 2004, 2007; Medina et al., 2007). As a baseline for
comparison, the effects of chronic THC treatment was also studied on food-maintained
responding, which has been shown to result in tolerance to THC-induced decreases in
operant responding (McMahon, 2011). It was hypothesized that THC-induced
impairments to cognitive function would be independent of nonselective disruption to
98
operant responding and be most sensitive to working memory performance based on
previous research in nonhuman primates (Taffe, 2012; Wright Jr. et al., 2013; Kangas et
al., 2016).
As it relates to neurobiological substrates that mediate cognition and executive
function, dopamine (DA) D2-like receptors, consisting of the DA D2 and D3 receptor
subtypes, are particularly relevant (Tomasi and Volkow, 2013). For instance, D2/D3
receptors in the dorsal striatum have been shown to be associated with activation of
prefrontal brain regions implicated in executive function (Volkow et al., 1993, 1998) and
with frontostriatal circuitry implicated in inhibitory control (Ghahremani et al., 2012).
Moreover, chronic drug abuse in humans has been associated with a reduction in D2/D3
receptor availability compared to healthy control subjects for a variety of substances
(Volkow et al., 2012). Taken together, cognitive impairment resulting from chronic drug
use may be related to low striatal D2/D3 availability. Indeed, cognitive impairment in long-
term marijuana users has been associated with reduced activity in frontal cortical regions
(Block et al., 2002; Eldreth et al., 2004; Gruber et al. 2005; Bolla et al., 2005); however,
the relationship between chronic THC exposure, cognitive performance, and D2/D3
receptor availability has not been investigated. In the present study we assessed D2/D3
receptor availability using PET and [11C]-raclopride in relation to cognitive performance
during chronic THC treatment.
METHODS
Subjects
Eight individually housed adult male rhesus monkeys (Macaca mulatta) served
as subjects. Four monkeys were drug-naïve at the start of these studies and baseline
[11C]-raclopride scans were obtained (see below). Two of these monkeys, along with four
animals with extensive drug histories (see John et al., 2015a,b) were involved in the
99
THC-cognition studies. Each monkey was fitted with an aluminum collar (Model B008,
Primate Products, Redwood City, CA) and trained to sit in a primate restraint chair
(Primate Products). Monkeys were housed in stainless-steel cages with visual and
auditory contact with each other, ad libitum access to water in their home cage and were
fed sufficient standard laboratory chow (Purina LabDiet 5045, St Louis, MO) to maintain
healthy body weights as determined by veterinary staff. Environmental enrichment was
provided as outlined in the Guide for the Care and Use of Laboratory Animals (National
Research Council, 2011) and was approved by the Animal Care and Use Committee of
Wake Forest University.
Apparatus
The apparatus for food-maintained responding was a ventilated, sound-
attenuating chamber (1.5 x 0.74 x 0.76 m; Med Associates, East Fairfield, VT) designed
to accommodate a primate chair. Two photo-optic switches (5 cm wide) were located on
one side of the chamber with a horizontal row of three stimulus lights 14 cm above each
switch and a food receptacle between the switches. The food receptacle was connected
with tygon tubing to a pellet dispenser (Gerbrands Corp., Arlington, MA) located on the
top of the chamber for delivery of 1.0-g banana-flavored food pellets (Bio-Serv,
Frenchtown, NJ). An infusion pump (Cole-Palmer, Inc., Chicago, IL) was located on the
top of the chamber.
The apparatus for cognitive testing was a separate sound-attenuating, ventilated
chamber (0.8 x 0.8 x 1.32 m) consisting of a CANTAB panel (0.38 x 0.56 x 0.31 m) that
included a touch-sensitive screen (0.3 x 0.23 m), a stimulus light, a non-retractable
response lever, and a pellet receptacle located on the right side. For each task,
responding was maintained by 190-mg sucrose pellets.
100
Surgery
Monkeys included in behavioral experiments were surgically prepared with a
chronic indwelling venous catheter (femoral, internal or external jugular) and
subcutaneous vascular port (Access Technologies, Skokie, IL) using aseptic surgical
procedures, as described in detail elsewhere (John et al., 2015a). These monkeys were
also implanted subcutaneously with a transponder (Model IPTT-300; Bio Medic Data
Systems, Inc., Seaford, DE) to non-invasively measure body temperature.
Experimental Timeline
Six monkeys underwent two behavioral sessions each day (Figure 1). First,
cognitive performance was assessed using the CANTAB apparatus (Lafayette
Instruments, Lafayette, IN). Immediately following, monkeys were transported to a
different room and operant sessions were conducted in separate experimental chambers
where monkeys responded under a fixed-ratio (FR) 10 schedule of food (1.0 g banana-
flavored pellets) reinforcement with a 60 second timeout (TO) following each food
presentation. Monkeys were returned to their cages following the completion of the
second behavioral session.
Acute THC Treatments: The acute effects of THC were determined separately on
food-maintained responding and cognitive performance. For FR 10 food-maintained
responding, when stable (± 20% of the mean rate of responding for 3 consecutive
sessions, with no trends), non-contingent injections of THC (vehicle, 0.003-0.3 mg/kg,
i.v.) were administered one minute prior to operant sessions; all doses were tested 2-3
times for each monkey. Body temperature was taken immediately prior to THC
administration and then again 60-min following the start of the session. Between each
test session, at least two sessions were conducted where no drug or vehicle was
administered. Following one week of chronic THC (1.0 mg/kg, s.c.) administration, the
101
effects of acutely administered THC on FR responding was redetermined during which
monkeys were still treated daily with 1.0 mg/kg THC after the session. For cognitive
performance, following stable performance, the acute effects of vehicle and THC (0.003
– 0.3 mg/kg, i.v.) were determined on each task prior to chronic THC treatment. Each
dose was administered 30-min prior to the start of the session; all doses were tested 2-3
times for each monkey and at least two intervening sessions were conducted where no
drug or vehicle was administered. Following 8 weeks of chronic THC administration, the
acute effects of THC on cognitive performance were redetermined over the course of
two weeks while monkeys still received 1.0 mg/kg THC at the end of FR sessions.
Chronic THC Treatments: After completion of all acute THC dose-response
curves, THC was administered chronically for 12 weeks with daily injections (1.0 mg/kg
for 10 weeks and 2.0 mg/kg, s.c., for 2 weeks) occurring immediately following the
second behavioral session (i.e. FR food-maintained responding) or at similar times when
sessions did not occur (i.e. weekends). Thus, during this phase of the study, cognitive
performance and FR responding were measured approximately 22 and 23 hours after
daily THC administration, respectively. These doses were selected based on pilot
studies on FR responding. Moreover, doses of THC within the same range administered
to rhesus monkeys via the same route (s.c.) have been shown to produce similar blood
levels of THC as those of humans smoking cannabis in controlled laboratory studies
(Ginsberg et al., 2014) The daily dose was not raised to 2.0 mg/kg in R-1710 due to
marked behavioral disruption during 1.0 mg/kg THC, to which tolerance did not develop.
Following completion of the chronic dosing phase, THC administration was discontinued
and performance during 4-6 weeks of abstinence was examined.
102
Experiment 1: Effects of acute and chronic THC on food-maintained responding
Sessions began with illumination of a white stimulus light above one of two
photo-optic switches in the chamber, counterbalanced between monkeys. Ten
consecutive responses emitted on the appropriate switch produced the delivery of a
banana-flavored food pellet accompanied by extinction of the white light and illumination
of a red stimulus light (conditioned stimulus) above the food receptacle for 3 sec.
Responses emitted on the alternate photo-optic switch had no programmed
consequence. Sessions lasted 60 min or until 30 reinforcers were obtained, whichever
occurred first. The effects of acute (1 min pretreatment) and chronic (23 hrs after daily
administration) THC were assessed on FR 10 responding, as described above.
Figure 1. Experimental timeline. Monkeys performed two operant behavioral
sessions each day, which included cognitive testing immediately followed by responding for food pellets under an FR 10 schedule. Following stable performance, the acute effects of THC (0.003–0.3 mg/kg, i.v.) were determined on FR responding (Experiment 1) and cognition (Experiment 2). THC (1.0–2.0 mg/kg, s.c.) was then administered chronically for 12 weeks during which the effects 22 hrs after administration were assessed daily on cognitive performance and FR responding (Experiment 2). During chronic treatment, dopamine D2 receptor availability was assessed using PET and [11C]-raclopride at four weeks (Experiment 3) and the acute effects of THC were redetermined on FR responding (Experiment 1) and cognition (Experiment 2) at one week and 8 weeks, respectively (Experiment 1). Behavioral performance continued to be assessed for 4 weeks following the discontinuation of THC treatment. Exp., Experiment; + THC #2, redetermination of acute effects of THC.
Cognitive assessment FR 10 (60 s TO)
+ 1.0 mg/kg THC (s.c.)
Baseline
+ 2.0 mg/kg THC (s.c.)
DMS/IDED
+ THC
(30 min PT, i.v.)
FR 10
+ THC
(1 min PT, i.v.)
PET
Week 1 Week 2
FR 10
+ THC #2
(1 min PT, i.v.)
DMS/IDED
+ THC #2
(30 min PT, i.v.)
Week 3 Week 4 Week 6Week 5 Week 7 Week 8 Week 9 Week 12Week 11Week 10 Weeks 13-16
Exp. 1 Exp. 1
Exp. 2
Exp. 2Exp. 3
Exp. 2
103
Experiment 2: Effects of acute and chronic THC on cognitive performance
Each week, DMS performance was assessed Monday-Wednesday and stimulus
discrimination, reversal learning, and set-shifting performance were assessed on
Thursday and Friday.
Delayed Match-to-Sample (DMS): The DMS task is a measure of attention and
working memory. Each trial began with the appearance of a “target” stimulus in the
center of the screen (sample phase). A response on this stimulus was followed by a
delay and then the presentation of a stimulus matching the previous image and at least
two distractor stimuli that do not match. Responding on the match resulted in delivery of
a sucrose pellet followed by a 10-s TO, whereas responding on the non-match resulted
in trial termination and the 10-s TO. Three delays were randomly distributed throughout
a total of 60 trials per session (20 trials/delay). Delays and number of distractor stimuli
were individually determined (Table 1) in order to generate delay-effect curves that met
the following criteria: short delay (80-100% accuracy); middle delay (60-79% accuracy);
long delay (<60% accuracy). The stimuli used were unique abstract patterns
preprogrammed in CANTAB software, which consisted of four rectangular quadrants of
different colors and shapes.
104
Table 1. Individual parameters used throughout study that produced delay-dependent
effects on DMS performance; dist., distractor stimuli.
Delays (sec)
# dist. Subject Short Mid Long
R-1567 1 30 60 2
R-1682 0 30 90 4
R-1710 0 10 30 2
R-1711 0 30 100 4
R-1713 0 30 60 3
R-1756 1 30 60 3
Stimulus discrimination, reversal learning and intradimensionl/extradimensional
set-shifting (ID/ED): This task involved 5 distinct stages that tested attention, rule
learning and reversal, and executive function. Stages advanced within-session following
acquisition of performance criteria, defined as 16 of 20 consecutive trials correct. Stage
1, simple discrimination (SD): Two stimuli (e.g. shapes) were presented, one of which
was reinforced upon selecting (S+) while the other was not reinforced (S-) and resulted
in trial termination. Stage 2, compound discrimination (CD): The same stimuli as in the
previous stage remained present, however, additional stimuli (e.g. lines) were
superimposed to construct compound stimuli that consisted of shape and line
dimensions. The same stimulus dimension that was associated with reinforcement in the
first stage (e.g. shapes) remained the relevant stimuli in this stage, while the other
dimension (e.g., lines) was irrelevant. Stage 3, compound reversal (CR): Stage 2 rules
were reversed such that the previously non-reinforced stimulus within the dimension was
reinforced. Stage 4, intra-dimensional shift (ID): A new pair of compound stimuli
consisting of shape and line dimensions was presented; however, despite new stimuli,
105
the same dimension of the stimuli as in stages 1 - 3 (e.g. shapes) remained relevant for
reinforcement. Stage 5, extra-dimensional shift (ED): another novel set of compound
stimuli was introduced; however, in this stage the previous irrelevant stimulus dimension
(e.g. lines) became relevant for reinforcement. Each session lasted for 100 min or after
criterion was met for each stage, whichever occurred first.
Experiment 3: Effects of chronic THC on D2/D3 receptors
Two groups of monkeys underwent [11C]-raclopride PET scans. One group (N=4)
was studied after 4 weeks of daily THC administration while another group (N=4) was
drug-naïve and served as control subjects. Two of the control subjects became subjects
in Experiments 1 and 2 and were also included in the group of monkeys scanned after
THC administration. Magnetic resonance imaging (MRI) scans were acquired for each
monkey. For those images, subjects were anesthetized with ketamine (10 mg/kg, i.m.)
and transported to the MRI facility, where anesthesia was maintained during the
scanning procedure with supplemental ketamine (up to 15 mg/kg, i.m.). T1-weighted
images of the brain were acquired with a 3.0-T Siemans SKYRA scanner. Images were
used to anatomically define regions of interest (ROI), including the caudate nucleus,
putamen, ventral striatum, and cerebellum, for later registration with PET images.
For the [11C]-raclopride studies, approximately 30 min prior to the PET scan, the
monkey was anesthetized with ketamine (10 mg/kg, i.m.) and transported to the PET
Center. Anesthesia was maintained during the scan by inhaled isoflurane (1.5 %). The
monkey was placed in the scanner (GE 64 slice PET/CT Discover VCT scanner, GE
Medical Systems, Milwaukee, WI) and a catheter was inserted into an external vein for
tracer injection and fluid replacement throughout the study. Body temperature was
maintained at 40°C and vital signs (heart rate, blood pressure, respiration rate, and
temperature) were monitored throughout the scanning procedure. A 5-min transmission
106
scan was acquired in 2D mode. Next, the monkey received a bolus dose of [11C]-
raclopride (6.5–8.0 mCi) and a 90-min dynamic acquisition scan was acquired consisting
of 18 frames over 90 min (18 × 5 min) in 3D mode (i.e., septa retracted). Image
reconstruction of 3D data was done using the 3D-reprojection method (Kinahan and
Rogers, 1989) with full quantitative corrections. Once scanning was complete, the
transmission scan data were smoothed transaxially using a 4-mm Gaussian filter and
segmented (Bettinardi, 1999). Emission data were corrected for attenuation and
reconstructed into 128 × 128 matrices using a Hanning filter with a 4-mm cutoff
transaxially and a ramp filter with an 8.5-mm cutoff axially.
Data Analysis
Experiment 1: The primary dependent variable for food-maintained responding
was response rate (responses/second). Drug effects were expressed as a percentage of
baseline responding, which was designated as 3 consecutive sessions of stable
responding prior to the start of the study. Change in body temperature (°C) following
administration of either vehicle or THC was determined by subtracting the temperature
recorded prior to the start of the session from the temperature recorded at the end of the
60-min session. The potency of THC to decrease food-maintained responding was
estimated by calculating the ED50 using the linear portion of the curve that crossed 50%
reduction in baseline response rate.
Experiment 2: The primary dependent variable for DMS performance was
percent accuracy, which was determined by dividing the number of correct responses by
the total number of trials completed for each delay (short, mid, long) multiplied by 100.
Omitted trials during either the sample or match phase were not counted towards the
total trials. During each condition (THC treatment and abstinence), changes in percent
accuracy from baseline, defined as 7 consecutive sessions with percent accuracy for
107
each delay ±25% of the mean rate for those sessions with no trends, were examined by
repeated-measures two-way repeated measures ANOVA using delay (short, middle,
long) and treatment condition (baseline, chronic, abstinence) as factors. Additional
dependent variables for DMS performance measured were number of omissions for
sample and match phases, response latencies for sample and match phases, and pellet
retrieval latencies. One-way repeated measures ANOVA was used to compare test
conditions with baseline conditions. Sessions during the chronic THC treatment phase in
which the total number of omissions exceeded 50% of the total trials were excluded from
analyses. The primary dependent variable for the ID/ED task was percent accuracy
(number of correct trials divided by number of total trials X 100) from baseline (four
consecutive sessions over two weeks with percent accuracy for each stage ±25% of the
mean rate for those sessions, with no trends). Repeated measures two-way ANOVA
was conducted using stage (SD, CD, CDR, ID, ED) and treatment condition (baseline,
chronic, abstinence) as factors. Omitted trials were excluded from all analyses.
Significant main effects were followed by post-hoc Fisher LSD tests. For all analyses, p
< 0.05 was considered statistically significant.
Experiment 3: PET data were co-registered to individual T1-weighted MRIs,
which were used to anatomically define regions of interest (ROI) including the caudate
nucleus, putamen, ventral striatum, and cerebellum. PMOD Biomedical Image
Quantification Software (version 3.1; PMOD Technologies, Zurich, Switzerland) was
used for image registration and was used to calculate distribution volume ratios (DVRs)
for the caudate nucleus, putamen, and ventral striatum by implementing the “Logan
method” of analysis (Logan et al. 1990) using the cerebellum as the reference region.
DVRs for each region were not different between left and right sides therefore data from
each monkey was expressed as a mean of both sides. To compare DVRs between
108
control and THC-treated subjects, a two-way ANOVA was conducted using region and
group as factors.
RESULTS
Experiment 1: Effects of acute and chronic THC on food-maintained responding
Mean (±SEM) rate of responding under the FR 10 (TO 60-s) schedule of food
presentation, before THC administration was 1.18 (0.19) responses/sec and mean body
temperature was 35.97 (0.54) °C. Acute THC administration resulted in dose-dependent
decreases in food-maintained responding (Figure 2A, open circles) and biphasic effects
on body temperature (Figure 2A, open triangles). The ED50 (± 95% confidence limits) for
THC on reducing response rates was 0.034 (0.012-0.099) mg/kg and for hypothermia
was 0.068 (0.024-0.19) mg/kg. In general, daily post-session administration with 1.0
mg/kg THC for 10 weeks and 2.0 mg/kg THC for 2 weeks, did not affect food-maintained
responding (Figure 2B). However, after one week of daily THC treatment (1.0 mg/kg,
s.c.), the ED50 for acutely administered THC to decrease food-maintained responding
was 0.34 (0.16-0.51) mg/kg - a 9.7–fold (p < 0.05) reduction in potency (Figure 2A,
closed circles). The ED50 for THC to decrease body temperature after treatment was
0.46 (0.23-0.9) mg/kg, which was increased by 6.8-fold (Figure 2A, closed triangles).
109
Experiment 2: Effects of THC on cognitive performance
DMS performance
For each monkey, three delays were then chosen (short, mid, long) that resulted
in similar delay-effect curves between monkeys (Table 2). For the acute effects of THC
on DMS performance prior to chronic treatment, there was a main effect of dose [F(4,
63) = 13.55, p < 0.0001]; post-hoc analyses revealed that 0.03 mg/kg THC significantly
reduced percent accuracy from baseline at the short delay and 0.03 and 0.056 mg/kg
THC dose-dependently decreased percent accuracy from baseline at the middle and
long delays (Fig. 3A, open symbols). These effects occurred at doses that did not
produce omissions in more than 30% of total trials (Fig. 3B, open symbols). When the
Figure 2. Effects of acute THC on food-maintained responding and body
temperature before and during chronic THC (1.0 mg/kg, s.c.) treatment (A) and mean (±SD) food-reinforced response rates for sessions before (BL, baseline), during (23 hours after daily administration), and after chronic THC treatment for individual monkeys (B). Panel A, Abscissae: Dose of THC in mg/kg. Left ordinate: Mean (± SEM) response rate expressed as a percentage of baseline (n = 6). Right ordinate: Mean (± SEM) change in body temperature expressed in °C. Panel B,
Abscissae: Consecutive weeks before, during, and after chronic THC treatment. Ordinate: Response rate (responses/sec). Filled symbols indicate weeks during chronic THC treatment. Open symbols indicate weeks where THC was not administered. All monkeys were treated with 1.0 mg/kg THC during weeks 1-10 and 2.0 mg/kg THC during weeks 11-12 with the exception of one monkey, R-1710, who was only treated with 1.0 mg/kg THC during weeks 1-8.
V 0.01 0.1 1
0
25
50
75
100
125
150
-1.0
-0.5
0.0
0.5
D9-THC (mg/kg)
Re
sp
on
se
Rate
(%
of
bas
elin
e)
Before (food)
During (food)
Ch
an
ge
In B
od
y T
em
p. (°C
)
During (Δ temp)
Before (Δ temp)
BL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0.0
0.4
0.8
1.2
1.6
2.0
2.4
Week
Resp
on
se R
ate
(resp
/sec)
R-1756
R-1567
R-1682
R-1710
R-1711
R-1713
A B
110
THC dose-response curve was redetermined during chronic THC treatment, there was a
main effect of delay [F(2, 54) = 37.36, p < 0.0001], dose [F(5, 54) = 5.38, p < 0.0001],
and a significant interaction [F(10, 54) = 4.52; p = 0.0001). Post-hoc analyses indicated
that tolerance developed to the performance-disrupting effects of THC such that 0.03
and 0.056 mg/kg THC did not alter percent accuracy from baseline at the short and
middle delays. However, a larger dose (0.3 mg/kg) of THC produced comparable
decreases to other doses in the initial assessment at the middle but not short delay.
The effects of THC (1.0-2.0 mg/kg, s.c.) 22 hours after treatment on DMS
performance over the course of 12 weeks of treatment and during 4 weeks of abstinence
(Figure 4A) showed a significant main effect of condition (THC treatment regimen) on
percent change in accuracy from baseline [F(14, 42) = 2.29; p < 0.05). Significant
impairments were detected at the middle delay during week four of treatment and at the
long delay during the first four weeks of treatment and again during weeks 6 and 7. By
week 8, tolerance developed to THC-induced impairments such that decreases in
percent accuracy at the long delay were not different from baseline. At week 11, the
dose of daily administered THC was increased to 2.0 mg/kg (s.c.) and percent accuracy
from baseline at the longest delay was significantly decreased again at week 12, which
persisted until 2 weeks of abstinence (Figure 4A).
111
Table 2. Baseline percent accuracy (± SD) at each delay for individual monkeys
responding under the DMS task.
Delays
Subject Short Mid Long
R-1567 78.8 (7.2) 46.5 (17.2) 38.3 (9.9)
R-1682 81.7 (5.9) 70.9 (9.8) 58.7 (9.8)
R-1710 75.8 (4.0) 52.9 (11.4) 41.7 (13.6)
R-1711 82.2 (10.2) 72.0 (15.5) 58.1 (8.7)
R-1713 87.1 (5.7) 56.5 (10.2) 49.3 (14.0)
R-1756 72.7 (11.2) 52.0 (11.0) 48.5 (5.1)
Stimulus discrimination, reversal learning, and set-shifting performance
For the acute effects of THC on set-shifting performance prior to chronic THC
treatment, two-way ANOVA demonstrated a main effect of stage [F(16,98) = 10.69, p <
0.001] and dose [F(4, 98) = 3.49, p < 0.05]. Post-hoc analysis revealed a significant
decrease in percent accuracy for CD at 0.1 mg/kg THC (p < 0.05) and for ED at 0.056
and 0.1 mg/kg THC (p < 0.05) (Figure 3C). During chronic treatment, the acute effects of
only 0.1 mg/kg THC were reexamined because it was the highest dose that produced
impairments without producing a significant number of omissions in the initial
assessment prior to treatment. Two-way ANOVA demonstrated a main effect of stage
[F(4, 68) = 4.35, p < 0.05] and condition [F(3, 68) = 3.55, p < 0.05); post-hoc analysis
revealed that percent accuracy was significantly lower for vehicle administration during
chronic treatment compared to vehicle prior to treatment. Percent accuracy following 0.1
mg/kg THC was not significantly different compared to vehicle administration prior to
chronic treatment (Fig. 3D).
112
The effects of THC 22 hours after administration on SD, CD, CDR, ID and ED
set-shifting performance (Figure 4B) during chronic treatment and abstinence showed a
significant main effect of stage [F(4, 296) = 12.53; p < 0.0001] although post-hoc
analysis revealed no significant decreases in percent accuracy from baseline at any
stage during the course of treatment and throughout abstinence.
113
V 0.01 0.1 1
25
50
75
100
125
Perc
en
t C
orr
ec
t(%
of
ba
selin
e)
Short Delay
Before
During
**
V 0.01 0.1 1
Mid Delay
*
** **
V 0.01 0.1 1
Long Delay
**
****
*
** *
**
****
Δ9-THC (mg/kg)
V 0.01 0.1 10
10
20
30
40
50
60
Nu
mb
er
of
Om
issio
ns
Phase 1
Before
During
*
***
V 0.01 0.1 1
Phase 2
A
B
V 0.01 0.1 1
40
50
60
70
80
90
100
Δ9-THC (mg/kg)
Perc
en
t C
orr
ect
SD
EDIDCDR CD
DC
SD CD CDR ID ED50
60
70
80
90
100
Perc
en
t C
orr
ect
*
#
*
+ Vehicle
+ 0.1 mg/kg THC
+ Vehicle (During)
+ 0.1 mg/kg THC (During)
Δ9-THC (mg/kg)
114
Figure 3. Effects of acute THC on cognitive performance before and during chronic THC (1.0 mg/kg, s.c.) treatment. Doses of THC were administered 30 min before experimental sessions. Panel A: Delayed-match-to-sample performance at short (left panel), mid (middle panel), and long (right panel) delays. Abscissae: dose of THC in mg/kg that was administered 30 min prior to assessment before and during chronic THC treatment. Ordinate: Percent accuracy as a percent of baseline performance. Panel B: Number of omissions during sample (left panel) and target phase (right panel) of delayed-match-to-sample task. Panel C: Acute effects of THC on SD, CD, CDR, ID, and ED set-shifting performance prior to chronic THC treatment. Panel D: Effects of 0.1 mg/kg THC and vehicle on SD, CD, CDR, ID, and ED set-shifting performance before and during chronic THC treatment. Asterisks indicate significant difference from baseline. *p < 0.05, **p < 0.01, ****p < 0.0001. #, significant difference from vehicle administration during chronic THC treatment. Data represent mean (SEM) of n = 6. SD, stimulus discrimination, CD, compound discrimination, CDR, compound reversal, ID, intradimensional set-shifting, and ED, extradimensional set-shifting.
115
Figure 4. Effects of THC 22 hrs after administration during chronic treatment and effects of abstinence on delayed match-to-sample performance (panel A) and discrimination and reversal learning and attentional set-shifting performance (panel B). Data are expressed as percent of baseline performance. Filled symbols indicate a significant difference from baseline (p < 0.05). Break in x-axis indicates two-week period where acute effects of THC on cognitive performance were reassessed during THC treatment. Vertical line indicates discontinuation of chronic THC treatment after which effects during abstinence were assessed. SD, stimulus discrimination, CD, compound discrimination, CDR, compound reversal, ID, intradimensional set-shifting, and ED, extradimensional set-shifting.
1 2 3 4 5 6 7 8 11 12 13 14 15 16
50
75
100
125
150
DMS
+ 1.0 mg/kg THC(22 hr PT)
+ 2.0 mg/kgTHC
(22 hr PT)
Short
Mid
Long
1 2 3 4 5 6 7 8 11 12 13 14 15 16
50
75
100
125
150ID/ED
SD
CD
CDR
ID
ED + 1.0 mg/kg THC(22 hr PT)
+ 2.0 mg/kgTHC
(22 hr PT)
Week
Accu
racy (
% b
aselin
e)
A.
B.
116
Experiment 3: Effects of chronic THC on D2/D3 receptors
In control monkeys, the distribution volume ratio (DVR) for [11C]-raclopride was
highest in the putamen, followed by the caudate nucleus and ventral striatum (Table 3).
When D2/D3 receptor availability was compared between control monkeys and monkeys
receiving chronic THC administration, there were no significant differences in the
caudate nucleus, putamen and ventral striatum (Table 2, Fig. 5; p<0.05). For the two
monkeys that participated in scans both before and during THC treatment, DVRs were
decreased in the caudate nucleus (R-1710: -19.6%; R-1711: -1.51%) and increased for
both monkeys in the ventral striatum (R-1710: +10.2%; R-1711: +3.9%). However, DVRs
in the putamen were decreased by 14.2 percent during THC treatment in one monkey
(R-1710) and increased by 15.3 percent in the other (R-1711).
Table 3. Individual and mean (±SEM) [11C]-raclopride DVRs in control and THC treated
monkeys.
Caudate Putamen
Ventral Striatum
Control
R-1710 2.36 2.55 1.51 R-1712 2.14 2.15 1.89 R-1711 2.39 2.15 1.82 R-1681 1.68 2.53 1.99
Mean 2.14 2.35 1.80 ±SEM 0.16 0.11 0.10
Δ9-THC
R-1710 1.90 2.18 1.67 R-1711 2.35 2.48 1.89 R-1713 2.07 2.62 2.57 R-1682 2.04 2.32 1.54 Mean 2.09 2.40 1.92 ±SEM 0.09 0.10 0.23
Difference -2.3% +2.1% +6.7%
117
Control THC Control THC Control THC1.0
1.5
2.0
2.5
3.0
DV
R
Caudate Putamen Ventral Striatum
R-1710
R-1682
R-1711
R-1710
R-1712
R-1681
R-1711
R-1713
Figure 5. Effects of chronic THC treatment on DA D2/D3 receptor availability as
measured with [11C]-raclopride. PET scans occurred after four weeks of chronic THC treatment. Different shaped symbols represent individual subjects while open symbols represent control subjects and filled symbols represent THC-treated subjects. Two monkeys (R-1710, R-1711) served as both control and THC-treated subjects.
118
DISCUSSION
The main goal of the present study was to examine the effects of THC on
multiple cognitive domains in nonhuman primates. Two sets of studies were conducted:
examination of the acute effects of THC before and during chronic THC treatment and
examination of the effects of THC 22 hrs after daily, 12 week, chronic treatment as a
model of residual drug effects, which reflect the period after direct intoxicating effects
have subsided. Indeed, studies in humans and nonhuman primates have confirmed the
direct effects of THC are absent at the time of behavioral assessment used presently
(Hollister et al., 1981; Ginsberg et al., 2014). Acutely, THC dose-dependently decreased
working memory (DMS performance) and attentional set-shifting (ID/ED performance).
During chronic treatment, differential tolerance developed to impairments in
discrimination-based learning but not working memory when the cognitive demand was
high. When cognitive performance was assessed 22 hours after THC administration
during daily treatment, impairments were specific to working memory. Tolerance
developed to the deficits in working memory, but an increase in THC dose resulted the
reemergence of working memory impairments. Working memory deficits recovered
within 2 weeks of abstinence. In an effort to examine potential mechanisms of action
mediating the differential effects of THC on cognition, dopamine D2/D3 receptor
availability was examined and found not to be different compared to control monkeys.
Tolerance to the acute effects of cannabinoids following a period of chronic
cannabinoid exposure can differ depending on the behavioral outcome measured
(Lichtman and Martin, 2005). As such, studies in humans suggest that chronic THC
exposure can produce differential tolerance to the acute effects of THC on cognition by
comparing groups of frequent vs. infrequent cannabis users. For instance, compared to
infrequent users, studies showed that frequent users were less impaired to the acute
effects of THC on measures of critical thinking, divided attention, reaction time, verbal
119
learning, and memory (D’Souza et al., 2008; Ramaekers et al, 2009). However, these
studies depended on the use of a group design, therefore, the extent to which the drug
history was actually a factor is unclear without knowing the effects of acute THC on
cognitive performance prior to the initiation of frequent marijuana use. By using a within-
subjects design, the present study systematically showed that tolerance developed to
THC-induced cognitive impairments in working memory and discrimination learning.
These data may be particularly relevant for determining levels of THC that reflect
impairment for driving and other tasks and suggest that cannabis use history is a
corresponding factor that should be considered.
With regard to the residual effects of chronic THC exposure on cognition, results
from studies in humans that assessed cognitive performance within a similar time frame
are inconsistent with the present results. For instance, several studies in recently
abstinent (6 -36 hr) adult cannabis users showed no differences in working memory
performance compared to infrequent or non-users (Pope and Yurgelun-Todd, 1996;
Solowij et al., 2002; Kanayama et al., 2004; Whitlow et al., 2004; Fisk and Montgomery,
2008). Moreover, studies assessing the residual effects of cannabis use on inhibition
and cognitive flexibility are more mixed with some studies showing impairments (Pope
and Yurgelun-Todd, 1996; Solowig et al., 2002) and others that do not (Whitlow et al.,
2004; Gruber and Yurgelun-Todd, 2005; Herman et al., 2007); associative learning in
recently abstinent adolescent and adult users has been shown to be unaffected (Harvey
et al., 2007; Fisk and Montgomery, 2008; Jager et al., 2010), which is consistent to the
present findings.
One possibility for discrepant findings may be due to the daily amount of THC
consumed across studies. Although participants in the previously mentioned studies
used marijuana on a near-daily basis per month, the number of marijuana cigarettes
smoked per day is often not reported, which makes it difficult to discern THC-induced
120
effects across studies. It is also possible that tolerance or certain deficits emerge after
longer periods of chronic THC exposure than what was presently studied. For instance,
Solowij (2002) found that the severity of cognitive deficits after at least 12 hrs of
abstinence in frequent cannabis users was correlated with years of use. Furthermore,
inconsistent results between studies could be a reflection of differential cognitive
demand across the tasks employed. This notion is supported by several studies in
humans that have examined the effects of chronic cannabis use on working memory.
For example, in one study, adolescent regular cannabis users demonstrated worse
working memory after 3 and 13 days of abstinence compared to control subjects during
a challenging task that required active manipulation of items (Hanson et al., 2010), but
not after 8 days during a simpler matching task (Jager et al., 2010). Similarly, Jacobsen
et al. (2007) reported worse verbal working memory in the n-back test in abstinent
adolescent cannabis users relative to control subjects but only during high memory load.
Thus, it may be reasoned that the DMS task had the highest cognitive demand
compared to the other tasks used in the present study, thus making it the most sensitive
to impairment during chronic THC treatment. Future studies examining the effects of
THC on cognition should incorporate varying degrees of complexity into tasks that
assess different cognitive domains, which would eliminate potential ceiling effects and
make assessments more task-specific and not just related to general behavioral
complexity.
Another explanation for the specificity of chronic THC to impair working memory
in the present study may be the result of regional differences in brain CB1 receptor
function related to this domain of executive function. Electrophysiological and lesion
studies demonstrate that working memory is preferentially activated by the ventrolateral
prefrontal cortex (Jones and Miskin, 1978; Miskin and Manning, 1978; Wilson et al.,
1993), while discrimination reversal learning and set-shifting are preferentially
121
associated with activation of the orbitofrontal cortex and dorsolateral prefrontal cortex,
respectively (Dias et al., 1996a, b). Thus, it is likely that working memory impairments
during chronic THC treatment were a reflection of greater dysregulation of CB1 receptor
dynamics (e.g. downregulation/densensitization) or signaling in regions that mediate
working memory such as the ventrolateral prefrontal cortex.
Also related to the residual effects of chronic THC treatment on cognition in the
present study, previous studies in rhesus monkeys showed that indications of withdrawal
(e.g., yawning, anorexia, irritability, headshaking, onset of cannabinoid receptor
antagonist discriminative stimulus effects) can be observed within 24-48 hr of
discontinuation of chronic THC treatment (Deneau and Kaymakcalan, 1971; Beardsley
et al., 1986; Stewart and McMahon et al., 2011), thus raising the question of whether
cognitive deficits were related to withdrawal as opposed to pharmacological-induced
changes to brain function that mediate such behavior. Although cognitive assessment in
the present study occurred at a similar time frame as withdrawal symptoms detected in
previous studies, no overt physical signs of withdrawal were noted throughout the study.
This was mostly likely due to the once-a-day administration of a lower daily dose of THC
compared to continuous infusion of higher daily doses used in previous studies (e.g. 1.0
mg/kg/12hr by Stewart and McMahon, 2010 or 0.05-0.17 mg/kg/hr by Beardsley et al.,
1986) shown to produce physical withdrawal symptoms. Nevertheless, it cannot be
completely discounted that cognitive impairment was the result of THC withdrawal
considering this may have been the only measure sensitive enough to detect such
effects under the present treatment regimen. Regardless of whether impairments were
the result of physical withdrawal or pharmacokinetics, rates of food-maintained
responding and numbers of omissions in five of six monkeys were not altered during the
course or after discontinuation of chronic treatment, which suggests that cognitive
impairment was not the result of a non-selective disruption in operant responding.
122
The present study did not find any differences in striatal D2/D3 receptor
availability measured by PET and [11C]-raclopride in monkeys chronically treated with
THC compared to control subjects, even though deficits in working memory performance
at the same time point were apparent. These findings are consistent with studies in
humans also showing no differences in baseline D2/D3 receptor availability between
marijuana abusers and healthy controls (Stokes et al., 2012; Urban et al., 2012; Albrect
et al., 2013; Volkow et al., 2014) and suggest that THC-induced cognitive impairment
was not directly related to striatal DA D2/D3 receptor function, despite the fact that the DA
D2/D3 system plays a prominent role in modulating prefrontal cortical function (Groman
and Jentsch, 2012).
One of the two monkeys (R-1710) scanned both before and during chronic THC
treatment showed a large reduction in D2/D3 receptor availability in the dorsal striatum
(Cd: -19.6%, Pt: -14.2%) during chronic THC treatment. Interestingly, this monkey was
also the most sensitive to the residual rate-decreasing effects of chronic THC treatment
on food-reinforced responding in which reduced rates of responding did not fully recover
to baseline levels until 6 weeks after discontinuation of daily THC treatment. These
findings suggest that there may be an inverse relationship between behavioral sensitivity
to THC and THC-induced changes to D2/D3 receptor availability. These findings could
also imply that higher daily doses of THC may be required to produce reductions in
D2/D3 receptor availability. Despite showing no changes in D2/D3 receptor availability in
the striatum, recent PET studies have shown reduced amphetamine-elicited striatal
dopamine release in cannabis abusers (Volkow et al., 2014; Van Giessen et al., 2016).
These results suggest that striatal dysfunction is a component of cannabis abuse, albeit
to a lesser extent perhaps than other drugs of abuse, and may have implications in THC-
induced cognitive impairment.
123
In summary, the present study demonstrated that chronic THC exposure
produced persistent and selective impairment to aspects of working memory by using a
longitudinal, within-subject study design in nonhuman primates. These data suggest that
cognitive impairments in cannabis dependent patients, particularly those involving
working memory, may not be the result of innate differences in cognitive function, but
rather a vulnerable, pharmacological-induced target that can be harnessed for
therapeutic value.
124
REFERENCES
Aharonovich E, Brooks AC, Nunes EV, Hasin DS (2008): Cognitive deficits in marijuana
users: Effects on motivational enhancement therapy plus cognitive behavioral
therapy treatment outcome. Drug Alcohol Depend 95:279-283.
Aharonovich E, Hasin DS, Brooks AC, Liu X, Bisaga A, Nunes EV (2006): Cognitive
deficits predict low treatment retention in cocaine dependent patients. Drug
Alcohol Depend 81:313-322.
Albrecht DS, Skosnik PD, Vollmer JM, Brumbaugh MS, Perry KM, Mock BH, et al.
(2013): Striatal D(2)/D(3) receptor availability is inversely correlated with
cannabis consumption in chronic marijuana users. Drug Alcohol Depend 128:52-
57.
Anderson, B. M., Rizzo, M., Block, R. I., Pearlson, G. D., & O’Leary, D. S. (2010): Sex,
drugs, and cognition: effects of marijuana. Journal of Psychoactive Drugs 42:
413–424.
Batalla A, Bhattacharyya S, Yücel M, Fusar-Poli P, Crippa JA, Nogué S, Torrens M,
Pujol J, Farré M, Martin-Santos R (2013): Structural and functional imaging
studies in chronic cannabis users: a systematic review of adolescent and adult
findings. PLoS One 8: e55821.
Beardsley PM, Balster RL, and Harris LS (1986): Dependence on tetrahydrocannabinol
in rhesus monkeys. J Pharmacol Exp Ther 239:311-319.
Bettinardi V (1999): An automatic classification technique for attenuation correction in
positron emission tomography. Eur J Nucl Med 26:447–458.
Block RI, O’Leary DS, Hichwa RD, Augustinack JC, Boles Ponto LL, et al. (2002):
Effects of frequent marijuana use on memory-related regional cerebral blood
flow. Pharmacol Biochem Behav 72: 237–250.
Bolla KI, Brown K, Eldreth D, Tate K, Cadet JL (2002): Dose-related neurocognitive
125
effects of marijuana use. Neurology 59:1337-1343.
Bolla KI, Eldreth DA, Matochik JA, Cadet JL (2005): Neural substrates of faulty decision-
making in abstinent marijuana users. Neuroimage 26: 480–492.
Carroll KM, Kiluk BD, Nich C, Babuscio TA, Brewer JA, Potenza MN, et al. (2011):
Cognitive function and treatment response in a randomized clinical trial of
computer-based training in cognitive-behavioral therapy. Substance use and
misuse 46:23–34.
Crane NA, Schuster RM, Fusar-Poli P, et al. (2013): Effects of cannabis on
neurocognitive functioning: recent advances, neurodevelopmental influences,
and sex differences. Neuropsychol Rev. 23:117–37.
Crean, RD., Crane, NA, and Mason, BJ (2011): An evidence based review of acute and
long-term effects of cannabis Use on executive cognitive functions. Journal of
Addiction Medicine. 5(1), 1–8.
Deneau, GA and Kaymakcalan, S (1971): Physiological and psychological dependence
to synthetic delta-9-tetrahydocannabinol (THC) in rhesus monkeys.
Pharmacologist 13:246.
Dias R, Robbins TW, Roberts AC (1996a): Dissociation in prefrontal cortex of affective
and attentional shifts. Nature 380: 69-72.
Dias R, Robbins TW, Roberts AC (1996b): Primate analogue of the Wisconsin Card
Sorting Task: effects of excitotoxic lesions in the prefrontal cortex in the
marmoset. Behav Neurosci 110: 872-76.
D’Souza, D. C., Braley, G., Blaise, R., Vendetti, M., Oliver, S., Pittman, B., et al. (2008):
Effects of haloperidol on the behavioral, subjective, cognitive, motor, and
neuroendocrine effects of Delta-9-tetrahydrocannabinol in humans.
Psychopharmacology 198: 587–603.
Eldreth DA, Matochik JA, Cadet JL, Bolla KI (2004): Abnormal brain activity in prefrontal
126
brain regions in abstinent marijuana users. Neuroimage 23: 914– 920.
Fehr, C, Yakushev, I, Hohmann, N, Buchholz, HG, Landvogt, C, Deckers, H, et al.
(2008): Association of low striatal dopamine d2 receptor availability with nicotine
dependence similar to that seen with other drugs of abuse. Am. J. Psychiatry
165: 507–514.
Fisk JE and Montgomery C (2008): Real-world memory and executive processes in
cannabis users and non-users. Journal of Psychopharmacology 22:727-736.
Ghahremani DG, Lee B, Robertson CL, Tabibnia G, Morgan AT, De Shetler N, et al.
(2012): Striatal dopamine D₂/D₃ receptors mediate response inhibition and
related activity in frontostriatal neural circuitry in humans. J Neurosci 32:7316-
7324.
Ginsburg BC, Hruba L, Zaki A, Javors MA, McMahon LR (2014): Blood levels do not
predict behavioral or physiological effects of Δ⁹-tetrahydrocannabinol in rhesus
monkeys with different patterns of exposure. Drug Alcohol Depend 139:1-8.
Gould RW, Garg PK, Garg S, Nader M.A. Effects of nicotinic acetylcholine receptor
agonists on cognition in rhesus monkeys with a chronic cocaine self-
administration history. Neuropharmacology. 64: 479-488, 2013.
Grant, JE, Chamberlain, SR, Schreiber, L, and Odlaug, BL (2011): Neuropsychological
deficits associated with cannabis use in young adults. Drug and Alcohol
Dependence 121: 159-162.
Groman SM and Jentsch JD (2012): Cognitive control and the dopamine D₂-like
receptor: a dimensional understanding of addiction. Depress Anxiety 29:295-306.
Gruber SA and Yurgelun-Todd DA (2005): Neuroimaging of marijuana smokers during
inhibitory processing: a pilot investigation. Brain Res Cogn Brain Res 23:107–
118.
127
Hanson KL, Winward JL, Schweinsburg AD, Medina KL, Brown SA, Tapert SF (2010):
Longitudinal study of cognition among adolescent marijuana users over three
weeks of abstinence. Addict Behav. Nov;35(11):970-6.
Hart CL, van Gorp W, Haney M, et al. (2001): Effects of acute smoked marijuana on
complex cognitive performance. Neuropsychopharmacology 25:757–765.
Hart, C. L., Ilan, A. B., Gevins, A., Gunderson, E. W., Role, K., Colley, J., et al. (2010).
Neurophysiological and cognitive effects of smoked marijuana in frequent users.
Pharmacology, Biochemistry, and Behavior, 96(3), 333–341.
Harvey, MA, Sellman, JD, Porter, RJ, and Frampton, CM (2007): The relationship
between non-acute adolescent cannabis use and cognition. Drug and Alcohol
Review 26:309-319.
Heishman SJ, Arasteh K, Stitzer ML (1997): Comparative effects of alcohol and
marijuana on mood, memory, and performance. Pharmacol Biochem Behav
58:93-101.
Hermann D, Sartorius A, Welzel H, Walter S, Skopp G, Ende G, et al. (2007):
Dorsolateral prefrontal cortex N acetylaspartate/total creatine (NAA/tCr) loss in
male recreational cannabis users. Biol Psychiatry 61:1281-1290.
Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S (1981): Do
plasma concentrations of delta 9-tetrahydrocannabinol reflect the degree of
intoxication? J Clin Pharmacol 21 (8-9 Suppl.): 171S-177S.
Jacobsen, LK, Mencl, WE, Westerveld, M, and Pugh, KR (2004): Impact of cannabis use
on brain function in adolescents. Annals of the New York Academy of Sciences
1021:384–390.
Jacobsen LK, Pugh KR, Constable RT, Westerveld M, Mencl WE (2007): Functional
correlates of verbal memory deficits emerging during nicotine withdrawal in
abstinent adolescent cannabis users. Biol Psychiatry 61:31-40.
128
Jager, G, Block, RI, Luijten, M, and Ramsey, NF (2010). Cannabis use and memory
brain function in adolescent boys: a cross-sectional multicenter functional
magnetic resonance imaging study. Journal of the American Academy of Child
and Adolescent Psychiatry 49: 561–572.
Jentsch JD, Pennington ZT (2014): Reward, interrupted: Inhibitory control and its
relevance to addictions. Neuropharmacology 76: 479-486.
John WS, Newman AH, Nader MA (2015): Differential effects of the dopamine D3
receptor antagonist PG01037 on cocaine and methamphetamine self-
administration in rhesus monkeys. Neuropharmacology 92:34-43.
Jones B and Mishkin M (1972): Limbic lesions and the problem of stimulus
reinforcement associations. Exp Neurol 36:362–377.
Kanayama G, Rogowska J, Pope HG, Gruber SA, Yurgelun-Todd DA (2004): Spatial
working memory in heavy cannabis users: a functional magnetic resonance
imaging study. Psychopharmacology 176:239-247.
Kangas BD, Leonard MZ, Shukla VG, Alapafuja SO, Nikas SP, Makriyannis A, et al.
(2016): Comparisons of Δ9-Tetrahydrocannabinol and Anandamide on a Battery
of Cognition-Related Behavior in Nonhuman Primates. J Pharmacol Exp Ther
357:125-33.
Kinahan PE and Rogers JG (1989): Analytic 3-D image reconstruction using all detected
events. IEEE Trans Nucl Sci 36:964–968.
Kirk JM, de Wit H (1999): Responses to oral delta9-tetrahydrocannabinol in frequent and
infrequent marijuana users. Pharmacol Biochem Behav 63:137-142.
Lichtman AH and Martin BR (2005): Cannabinoid tolerance and dependence. Handb
Exp Pharmacol 168:691-717.
Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, et al. (1990):
Graphical analysis of reversible radioligand binding from time- activity
129
measurements applied to [N-11C-methyl]-(−)-cocaine PET studies in human
subjects. J Cereb Blood Flow Metab 10:740–747.
McDonald, J, Schleifer, L, Richards, JB, and de Wit, H (2003): Effects of THC on
behavioral measures of impulsivity in humans. Neuropsychopharmacology 28:
1356-1365.
McMahon LR (2011): Chronic Δ⁹-tetrahydrocannabinol treatment in rhesus monkeys:
differential tolerance and cross-tolerance among cannabinoids. Br J Pharmacol.
162:1060-73.
Medina KL, Hanson KL, Schweinsburg AD, Cohen-Zion M, Nagel BJ, Tapert SF (2007):
Neuropsychological functioning in adolescent marijuana users: subtle deficits
detectable after a month of abstinence. J Int Neuropsychol Soc 13:807-820.
Mishkin M and Manning FJ (1978): Non-spatial memory after selective prefrontal lesions
in monkeys. Brain Res 143:313–323.
Pope HG, Jr, Yurgelun-Todd D (1996): The residual cognitive effects of heavy marijuana
use in college students. JAMA 275:521-527.
Ramaekers JG, Kauert G, van Ruitenbeek P, Theunissen EL, Schneider E, Moeller MR
(2006): High-potency marijuana impairs executive function and inhibitory motor
control. Neuropsychopharmacology 31:2296-2303.
Ramaekers JG, Kauert G, Theunissen EL, Toennes SW, Moeller MR (2009):
Neurocognitive performance during acute THC intoxication in heavy and
occasional cannabis users. J Psychopharmacol 23:266–277.
Ramaekers JG, Theunissen EL, de Brouwer M, Toennes SW, Moeller MR, Kauert G
(2011): Tolerance and cross-tolerance to neurocognitive effects of THC and
alcohol in heavy cannabis users. Psychopharmacology (Berl) 214:391–401.
Rezapour T, DeVito EE, Sofuoglu M, Ekhtiari H (2016): Perspectives on neurocognitive
rehabilitation as an adjunct treatment for addictive disorders: From cognitive
130
improvement to relapse prevention. Prog Brain Res 224:345-69.
SAMSHA. Results from the 2013 National Survey on Drug Use and Health: National
Findings. Rockville, MD: Office of Applied studies, DHHS; 2013.
Sofuoglu, M, DeVito, EE, Waters, AJ, & Carroll, KM (2013): Cognitive Enhancement as a
Treatment for Drug Addictions. Neuropharmacology 64: 452-463.
Solowij N (1995): Do cognitive impairments recover following cessation of cannabis use?
Life Sciences 56:2119-2126.
Solowij N, Michie PT, Fox AM (1995): Differential impairments of selective attention due
to frequency and duration of cannabis use. Biol Psychiatry 37:731-739.
Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. (2002):
Cognitive functioning of long-term heavy cannabis users seeking treatment.
JAMA 287:1123-31. Erratum in: JAMA2002 Apr 3;287(13):1651.
Solowij, N, Jones, KA, Rozman, ME, Davis, SM, Ciarrochi, J, Heaven, PC, et al. (2011):
Verbal learning and memory in adolescent cannabis users, alcohol users and
non-users. Psychopharmacology 216: 131–144.
Stewart JL and McMahon LR (2010): Rimonabant-induced Δ9-THC withdrawal in rhesus
monkeys: discriminative stimulus effects and other withdrawal signs. J
Pharmacol Exp Ther. 334: 347-356.
Stokes PR, Egerton A, Watson B, Reid A, Lappin J, Howes OD, et al. (2012): History of
cannabis use is not associated with alterations in striatal dopamine D2/D3
receptor availability. J Psychopharmacol 26:144-149.
Taffe MA (2012): Δ⁹Tetrahydrocannabinol impairs visuo-spatial associative learning and
spatial working memory in rhesus macaques. J Psychopharmacol 26:1299-306.
Tomasi D, Volkow ND (2013): Striatocortical pathway dysfunction in addiction and
obesity: differences and similarities. Crit Rev Biochem Mol Biol 48:1-19.
Urban NB, Slifstein M, Thompson JL, Xu X, Girgis RR, Raheja S, et al. (2012):
131
Dopamine release in chronic cannabis users: a [11c]raclopride positron emission
tomography study. Biol Psychiatry 71:677-683.
van de Giessen E, Weinstein JJ, Cassidy CM, Haney M, Dong Z, Ghazzaoui R, et al.
(2016): Deficits in striatal dopamine release in cannabis dependence. Mol
Psychiatry In press.
Volkow ND, Fowler JS, Wang GJ, Hitzemann R, Logan J, Schlyer DJ, et al (1993):
Decreased dopamine d2 receptor availability is associated with reduced frontal
metabolism in cocaine abusers. Synapse 14:169–177.
Volkow N, Gur R, Wang G, Fowler J, Moberg P, Ding Y, et al. (1998): Association
between decline in brain dopamine activity with age and cognitive and motor
impairment in healthy individuals. Am J Psychiatry 155:344–349.
Volkow ND, Wang GJ, Fowler JS, Tomasi D (2012): Addiction circuitry in the human
brain. Annu Rev Pharmacol Toxicol 52:321-336.
Volkow ND, Wang GJ, Telang F, Fowler JS, Alexoff D, Logan J, et al. (2014): Decreased
dopamine brain reactivity in marijuana abusers is associated with negative
emotionality and addiction severity. Proc Natl Acad Sci U S A 111:3149-3156.
Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM, et al.
(2004): Long-term heavy marijuana users make costly decisions on a gambling
task. Drug Alcohol Depend 76:107-11.
Wilson FA, Scalaidhe SP, Goldman-Rakic PS (1993): Dissociation of object and spatial
processing domains in primate prefrontal cortex. Science 260:1955–1958.
Wright MJ Jr, Vandewater SA, Parsons LH, Taffe MA (2013): Δ(9)Tetrahydrocannabinol
impairs reversal learning but not extra-dimensional shifts in rhesus macaques.
Neuroscience 235:51-58.
132
CHAPTER IV
DISCUSSION
Recent epidemiological data report that nearly 3 of every 10 marijuana users,
approximately 6.8 million Americans, have a diagnosis of marijuana use disorder (Hasin
et al., 2015); however, there is currently no FDA-approved pharmacotherapy to treat
cannabis dependence and abuse and behavioral modification strategies are only
modestly effective (Nordstrom and Levin, 2007). Thus, more basic research is needed in
order to improve our understanding of the neurobiological and behavioral mechanisms
underlying the effects of THC, the main psychoactive component of marijuana. A better
understanding of these variables will aid in the development of new treatments for
maintaining abstinence in patients with cannabis use disorder. Preclinical studies with
other drugs of abuse demonstrate that a multitude of factors govern their contingent
delivery such as dose, behavioral history, drug history, and schedule of reinforcement.
These findings have been instrumental in shaping the current understanding of drug
abuse as a disease process and for guiding more effective treatment strategies. In this
regard, a major obstacle preventing such progress for cannabis abuse has been the
difficulty in demonstrating the reinforcing effects of THC in self-administration (SA)
animal models. Thus, the work described in this dissertation contributed to furthering our
understanding of the behavioral mechanisms involved in the reinforcing effects of THC
that has implications for 1) potential risk factors that may predict vulnerability to cannabis
abuse, 2) the variables that control cannabis use in humans that could be amenable to
therapeutic intervention and 3) parametric manipulations that can be used to advance
the cannabinoid self-administration methodology in preclinical animal models.
In addition to the reinforcing effects, a better understanding of the neurocognitive
effects of THC will also aid in the improvement of treatment strategies for cannabis
dependent patients. For instance, long-term marijuana use has been associated with
133
deficits in executive function across multiple domains integral in facilitating behavioral
modification including behavioral flexibility and memory (Bolla et al., 2002; Pope and
Yurgelun-Todd, 1995, 1996; Solowij e al., 1995, 2002). Moreover, cognitive function at
the time of treatment initiation has correlated with treatment success (Aharonovich et al.,
2003, 2006, 2008; Carroll et al., 2011). As a result, cognitive enhancement has been
proposed as a strategy to improve behavioral modification practices to treat substance
abuse, including cannabis use disorder (Sofuoglu et al., 2013). However, the exact
nature and time course of THC-induced deficits to executive function are unclear (Crean
et al., 2011; Crane et al., 2013). Through the use of a longitudinal, within-subjects study
design in nonhuman primates, the second major contribution of the work described in
this dissertation provided insight on the specificity and temporal nature of chronic THC
exposure on cognitive function. This information can be used to direct more targeted
therapies aimed at enhancing cognitive function in patients with cannabis use disorder.
These results, taken together, provide a better understanding of not only how
THC exerts its control over behavior, but also the resulting consequences of chronic
exposure, namely on cognitive functioning.
Cannabinoid self-administration in nonhuman primates
Reliable and persistent SA in experimental animals has been demonstrated for
virtually every drug that is abused by humans including psychostimulants, opiates,
alcohol, and nicotine. Thus, the SA procedure has strong face validity to the human
condition. However, the one drug class that has functioned as a false negative in the
drug self-administration procedure by a majority of laboratories is the cannabinoids. It is
important to establish the conditions for demonstrating the reinforcing effects of
cannabinoids through the SA procedure for multiple reasons. First, the SA procedure
provides a baseline of drug-reinforced behavior that can be used to better understand
134
the environmental, biological, and pharmacological determinants of the reinforcing
effects of drugs. This information can be useful for understanding both the factors that
render one more susceptible to the abuse-related effects of THC as well as those that
can be used to mitigate the reinforcing effects of THC in order to facilitate abstinence.
Secondly, development of cannabinoid SA will be important because of its utility to
screen novel cannabinoid compounds for abuse liability and their potential scheduling as
controlled substances by the DEA (see Ator and Griffiths, 2003; Carter and Griffiths,
2009). As such, there is an increasing interest for the use of cannabis and other
cannabinoids for medicinal purposes to relieve symptoms associated with glaucoma,
nausea in patients undergoing chemotherapy, AIDS-associated anorexia, chronic pain,
inflammation, multiple sclerosis, and epilepsy (Hill, 2015). However, the abuse liability of
THC and other cannabinoids raises particular concerns for long-term use by vulnerable
populations. In this regard, the preclinical SA model as an abuse liability assessment will
be an important component for their future development and ultimate utility of novel
cannabinoid compounds in the clinical context.
As described in Chapter 1, currently, successful efforts at demonstrating the
reinforcing effects of THC and other cannabinoids by the SA procedure is limited to one
laboratory in the world using squirrel monkeys and parameters not previously employed
in rhesus monkeys or rodents including schedule of reinforcement, a broader range of
doses, faster injection speed, and a clear drug solution rather than a drug in suspension
(Tanda et al., 2000). However, the extension of cannabinoid SA procedures to rhesus
monkeys is highly desirable because this species is phylogenetically closer to humans
than squirrel monkeys, and would aid in the most accurate translation of results. It is also
important to extend the characterization of cannabinoid SA to other species in addition to
squirrel monkeys and the use of rhesus monkeys will allow for the systematic
manipulation of environmental (e.g., schedule of reinforcement) and pharmacological
135
(e.g., chronic treatment) variables using a within-subjects, longitudinal design over
years. Such information will allow for a better understanding of the critical variables
necessary to establish cannabinoid SA that may extend to other species, including
rodent models.
However, it is currently unknown whether previous unsuccessful attempts in
rhesus monkeys were due to methodology or a direct species difference between
squirrel and rhesus monkeys. To address this, in Chapter 2, THC was made available to
rhesus monkeys under similar SA methodological conditions as used in the successful
demonstrations in squirrel monkeys, including dose range, schedule of reinforcement,
and vehicle preparation. Moreover, the reinforcing effects of THC were compared to
reinforcing effects of the synthetic bicyclic cannabinoid analog, CP 55,940, a high
efficacy CB1/CB2 agonist, which allowed for a better understanding of the role of
pharmacological efficacy at the CB receptor in SA, such as differences in potency and
magnitude of response. We hypothesized that the reinforcing effects of the synthetic
cannabinoid receptor agonist CP55,940 and THC will be established by utilizing
methods that have shown success for demonstrating the reinforcing effects of
cannabinoids in squirrel monkeys.
The results from these studies indicated that CP55,940, but not THC, functioned
as a reinforcer in a subset (3 of 8) of rhesus monkeys when studied under similar
experimental conditions that have been used to demonstrate cannabinoid reinforcement
in squirrel monkeys. Notably, the doses of CP55,940 that had reinforcing effects were
considerably lower than the doses tested in the only other study assessing the
reinforcing effects of this compound in rhesus monkeys, which was unsuccessful
(Mansbach et al., 1994). As described below, one goal from this initial assessment was
to determine differences in biomarkers related to the three monkeys that showed
CP55,940 reinforcement versus the five that did not.
136
The results from this part of the dissertation have several important implications.
First, these experiments demonstrated, for the first time, reinforcing effects of a
cannabinoid receptor agonist (i.e. CP55,940) without the use of induction procedures
such as food deprivation, forced automatic injections, or the development of physical
dependence. Although reinforcing effects were not achieved in all subjects, a surprising
discovery was the dose range that was required for the subjects that did demonstrate
reinforcing effects. These data can be especially useful for comparing the potency of
CP55,940 reinforcing effects to other behavioral effects of CP55,940 in nonhuman
primates. For example, the discriminative stimulus effects of i.v. CP55,940 in rhesus
monkeys occurred at a dose of 0.003 mg/kg (McMahon, 2006), which is 75 - 2,400-fold
higher than the unit doses of CP55,940 that had reinforcing effects in the 3 monkeys in
the present study. The enormous differences in potency between the reinforcing and
discriminative stimulus effects of CP55,940 remain an intriguing area for future
investigation.
Moreover, the SA data generated with CP55,940 provide an important foundation
that future studies can build on, especially with regard to the range of doses that may be
used as a starting point to demonstrate reinforcing effects of this compound. Although
CP55,940 SA may have limited utility as a screening procedure for the assessment of
cannabinoid abuse liability (since the reinforcing effects of THC, as a positive control,
were not also achieved in the same subjects that had reinforcing effects of CP55,940), it
would still provide valuable information on the reinforcing effects that may be extended
to other synthetic cannabinoids, which are becoming increasingly abused (Rosenbaum
et al., 2012; Johnson et al., 2013; Nelson et al., 2014).
Furthermore, these studies have important implications because they allow for a
direct comparison of THC SA between squirrel monkeys and rhesus monkeys. The fact
that THC reinforcement was not obtained in rhesus monkeys using the same schedule
137
of reinforcement, dose range, and vehicle used in THC SA studies with squirrel monkeys
suggest that inherent species differences may be the biggest determinant regarding the
expression of the reinforcing effects of THC under these conditions. Interestingly, other
atypical reinforcers such as nicotine and response-contingent shock presentation also
seem to be more effective reinforcers in squirrel monkeys compared to other species of
nonhuman primates (e.g., rhesus monkeys); however, the potency and efficacy of drugs
such as cocaine, methamphetamine, and heroin to maintain behavior are similar
between Old- (e.g., rhesus monkey) and New- (e.g., squirrel monkey) World primate
species. Therefore, to address the factors underlying the qualitative differences in the
reinforcing effects of cannabinoids across species, future studies should systematically
investigate the behavioral, physiological, and pharmacokinetic effects of THC between
species, using squirrel monkeys as a positive control. These data will provide valuable
information regarding the factors that may correlate with the reinforcing effects of
cannabinoids that can then be accounted for by future cannabinoid SA studies in order
to optimize the model for more widespread use.
Behavioral and Physiological phenotypes underlying the reinforcing effects of
cannabinoids
Another important contribution of the work presented in this dissertation was the
characterization of behavioral and physiological phenotypes underlying the individual
differences in cannabinoid SA. The characterization of such factors are important for
predicting drug effects and for understanding what renders certain individuals more
vulnerable to the abuse-related effects of marijuana. Ultimately, this information can be
used to generate more targeted treatment and prevention strategies. In addition, a better
understanding of the individual differences in cannabinoid SA will be helpful for
optimizing the SA model for more widespread use in laboratory animals. One such
138
variable that was investigated in Chapter 2 was the relationship between behavioral
sensitivity to CP55,940 and THC and the ability of those drugs to function as reinforcers.
It was hypothesized that individual subjects less sensitive to the rate-decreasing effects
of CP55,940 and THC will have greater rates of SA of both drugs. To test this
hypothesis, the potency of both drugs to decrease food-maintained responding was
correlated with rates of SA in each subject.
It was found that there was an inverse relationship between the reinforcing
effects of CP55,940 and its behavioral-disruptive effects (Chapter 2, figure 4). Such an
analysis was precluded for THC considering it did not function as a reinforcer in any
subject. When administered non-contingently, CP55,940 was ~ 15-times more potent
than THC; when studied in SA, doses of THC overlapped this potency relationship in
order to assure a broad range of doses that encompassed no behavioral effects to
doses that non-contingently decreased operant behavior. Nevertheless, these data
indicate a novel variable (i.e., sensitivity to rate-decreasing effects) involved in the
reinforcing effects of cannabinoids that has never been empirically validated. The
significance of this finding indicates that 1) this is a factor that may predispose certain
cannabis users to subsequent abuse and dependence and 2) this is a factor could be
accounted for in animal models in order to obtain cannabinoid SA at greater levels and
in more subjects.
Studies in humans have indicated other behavioral and biological phenotypes
that may predict vulnerability to cannabis abuse and dependence involving personality,
subjective effects, and genetic factors (Hurd et al., 2014). For instance, genes encoding
the cannabinoid type 1 receptor CB1R (CNR1) and fatty acid amide hydrolase (FAAH),
an enzyme responsible for the hydrolysis of the endogenous cannabinoid anandamide,
have been shown to modulate risk for developing cannabis dependence (Agrawal et al.,
2009; Hopfer et al., 2006; Tyndale et al., 2007). Other studies indicate that personality
139
traits such as neuroticism, anxiety, and depression are risk factors associated with
cannabis dependence. Moreover, in population based cohorts, increased risk of
cannabis dependence has been associated with self-reported subjective effects of
cannabis use such that positive effects (e.g., feeling energetic, creative, euphoric, and
sociable) were associated with cannabis abuse and dependence and negative effects
(e.g., feeling confused, paranoid, and anxious) were associated with shorter duration
and lower frequency of cannabis use (Lyons et al., 1997; Grant et al., 2005; Scherrer et
al., 2009). Although informative, the interpretation of these findings are complicated due
retrospective design, reliance on self-report, polysubstance use, and uncontrolled
exposure to THC. Thus, the present results represent the first empirical evidence in
animal models indicating a relationship between behavioral sensitivity and subsequent
SA of cannabinoids. These findings emphasize the role of individual differences involved
in the reinforcing effects of THC and suggest that the behavioral sensitivity to THC,
during the early stages of drug use, may form part of a liability for future cannabis use
and abuse.
Another phenotype that was found to predict the cannabinoid reinforcing effects,
specifically CP55,940, was the individual subject’s drug history. In particular, it was
found that three of the four monkeys that had an extensive methamphetamine SA history
were the subjects in whom CP55,940 functioned as a reinforcer. Previous studies in
squirrel monkeys have demonstrated that a drug SA history was not necessary for
acquiring robust levels of THC self-administration in that drug-naïve subjects with no
drug SA experience acquired and maintained THC SA at similar, if not higher, levels
than subjects with an extensive history of self-administering cocaine (Justinova et al.,
2003). The studies in Chapter 2 also found no relationship between previous SA history
and the acquisition of THC SA (only one subject with a history of methamphetamine SA
acquired THC SA but only after daily treatment), therefore, the relationship between
140
methamphetamine SA history and the acquisition of cannabinoid SA appears to be
specific to CP 55,940. The mechanisms underlying this relationship and why it is specific
to the synthetic full cannabinoid receptor agonist (i.e., CP55,940), however, remains to
be determined. One possibility may involve specific neurobiological adaptations induced
by a chronic methamphetamine SA history and the involvement of those changes in
enhancing the reinforcing effects of a full agonist at the cannabinoid receptor. For
instance, we have previously reported that monkeys with a methamphetamine SA
history demonstrated increased sensitivity to the unconditioned behavioral effects of the
dopamine D3 receptor preferring agonist quinpirole relative to drug-naïve monkeys or
those that had a cocaine SA history (Martelle et al., 2014). It has also been shown that
THC and synthetic CB1 agonists potentiate some behavioral effects (e.g.
hyperlocomotion) of quinpirole (Gorriti et al., 2005; Moreno et al., 2005). Taken together,
one might hypothesize that sensitivity at the D3 receptor is positively related to
cannabinoid SA. However, the present study found no relationship between the
unconditioned behavioral effects of quinpirole and CP55,940 SA, which suggests
another methamphetamine-induced neuroadaptation could be involved. A better
understanding of the relationship between previous drug history and qualitative and
quantitative differences in cannabinoid SA may provide novel insight into the
mechanisms involved in mediating the reinforcing effects of cannabinoids.
Effects of attenuating the rate-decreasing effects of THC on its reinforcing effects
In order to extend this observation to factors that may be inhibiting the
expression of THC’s reinforcing effects in experimental animals, monkeys were treated
daily with THC in order to develop tolerance to the rate-decreasing effects of non-
contingent THC on food-maintained responding. After daily treatment, THC SA was
reassessed and was found to function as a reinforcer in 3 subjects. Thus, despite daily
141
THC treatment in all eight monkeys, our hypothesis was confirmed in only three
monkeys. Interestingly, in one monkey in whom THC functioned as a reinforcer,
tolerance did not develop to the rate-decreasing effects prior to reassessment
suggesting that another factor resulting from the daily treatment was responsible for
uncovering the reinforcing effects of THC in these subjects. At present, there are no
substantial observational differences in those three monkeys compared to the other five,
but identification of potential biomarkers, including perhaps changes in CB1 receptor
density and function (as measured by PET) may provide further insight into vulnerable
phenotypes.
Furthermore, THC self-administration was also assessed in a separate group of
monkeys responding under a second-order schedule of reinforcement that used
response-contingent conditioned stimuli to maintain behavior by relatively few drug
injections spaced between long intervals of time. It was hypothesized that reducing the
frequency of drug injection would allow time for rate-decreasing effects to dissipate while
the presence of conditioned stimuli within those intervals would help to maintain high
rates of behavior leading to subsequent injections. Under these schedule contingencies,
THC functioned as a reinforcer in two of four monkeys.
The failure of THC to function as a reinforcer in the majority of subjects following
the development of tolerance to rate-decreasing effects and by accounting for those
effects through the use of a second-order schedule does not support our hypothesis and
suggest other factors may be involved, as described in Chapter 2. This is not to say,
however, that rate-decreasing effects have no role in influencing the reinforcing effects
of cannabinoids. The fact that reinforcing effects were achieved in a subset of subjects
suggest that attenuating the rate-decreasing effects is a feasible strategy but the
methods used in Chapter 2 may not have been the most appropriate. The following
142
sections will discuss other strategies to further investigate the reinforcing effects of
cannabinoids in the context of accounting for their simultaneous rate-decreasing effects.
Other schedules of reinforcement
Similar to animal models, the reinforcing effects of THC were also surprisingly
not reliably demonstrated in early laboratory studies in human subjects. For instance,
early studies showed that cigarettes containing THC were not self-administered more
than placebo under free-access conditions (Zacny and de Wit, 1991; Kelly et al., 1994a,
b). Early studies also could not demonstrate a systematic relationship between THC
dose and the amount self-administered (Chait, 1989), despite dose-related differences in
ratings of intoxication or “high.” However, one approach that was later implemented to
detect dose-related reinforcing effects of THC in human subjects was the use of a
discrete-trials choice procedure in which subjects were offered a choice between
obtaining a particular dose of THC and obtaining a non-drug alternative (Mendelson and
Mello, 1984; Chait and Zacny, 1992; Chait and Burke, 1994; Kelley et al., 1994; Haney
et al., 1997; Hart et al., 2005). Under these conditions, a subject first sampled a dose of
THC and its matching placebo, and on a subsequent occasion the subject chose which
of the two to self-administer. The frequency with which the active drug was chosen over
placebo provided an index of the reinforcing efficacy of the drug.
Chait and Zacny (1992) provided some of the first evidence of THC’s reinforcing
effects using a choice procedure. Here, a two-trial choice procedure was used in which
experienced marijuana smokers sampled active drug and placebo during separate
sessions and then on two subsequent sessions were required to choose between these
two options. They found that ten of ten participants chose to self-administer smoked
THC (2.3%) on both trials and ten of eleven participants chose to self-administer oral
THC (10-15 mg based their subjective responses during practice sessions) on both
143
trials, i.e., they chose active drug on ∼90% of choice opportunities. Further, Haney et al.
(1997) compared choice between marijuana cigarettes (0, 2.2, 3.9% THC w/w) and
snack food where multiple choices were available each day, i.e., each concentration of
THC was made available on 4 days with each day consisting of one sample trail and six
choice trials. It was found that the participants selected cigarettes containing THC on
more than 75% of choice opportunities compared to only about 40% when placebo
cigarettes were available. Other studies have also shown that marijuana cigarettes with
a greater THC content are consistently preferred to cigarettes containing lower amount
of THC, even when the choice between them was not mutually exclusive (Haney et al.,
1997; Kelly et al., 1997; Ward et al., 1997).
Despite the success of choice procedures to detect the reinforcing effects of THC
in human subjects, there are surprisingly no published reports attempting to use this
method to demonstrate the reinforcing effects of THC in laboratory animals. However,
many of the features inherent to choice procedures for demonstrating the reinforcing
effects of drugs may be particularly well suited for cannabinoids. For instance, one of the
major advantages of choice procedures is that this method permits the dissociation of
reinforcing effects from reinforcement-independent rate-decreasing effects because the
primary dependent variable is behavioral allocation (e.g., percent drug choice vs.
percent choice of a non-drug alternative) as opposed to rate of responding. As a result,
the dose-response curve under a choice schedule is represented as a monotonic
increase in frequency of drug choice with increases in dose because the high doses that
typically constitute the descending limb of dose-response function under single response
procedures are still chosen relative to a non-drug reinforcer (e.g. food pellets).
Therefore, this measure reinforcement (allocation of behavior away from a non-drug
144
reinforcer) may be more applicable for demonstrating the reinforcing effects of
cannabinoids because it would not be sensitive to the rate-decreasing effects of
cannabinoids that are hypothesized to inhibit rates of responding under single response
procedures.
Figure 1 shows a hypothetical dose-response curve for THC self-administration
under a concurrent THC-food choice procedure. Similar to what was demonstrated in
Chapter 2 when THC was initially made available for self-administration under a FR 10
schedule, it is likely that the number of injections of THC will not be very high relative to
vehicle across a similar dose range. However, at a certain dose of THC (e.g. 3.0 µg/kg),
there may be a reallocation of behavior where THC is now exclusively chosen over food
reinforcement, despite low rates of responding for those injections. If this were the case,
the reinforcing effects of THC would have been effectively demonstrated due to its
relative reinforcing efficacy compared to a non-drug alternative. Such an outcome would
suggest that perhaps we need to shift our perspective of viewing the reinforcing effects
of THC in terms of numbers of injections and rates of responding but instead to THC
choice in the presence of an alternative reinforcer.
145
Using other drugs that are “atypical reinforcers” in laboratory animals as models
for developing cannabinoid self-administration
Going forward, it will be of utmost importance to also study the methodology
used for demonstrating the reinforcing effects of other drugs that are relatively less
efficacious reinforcers in preclinical models in order to better understand potential
pharmacological and behavioral factors that may also apply to demonstrating the
reinforcing effects of cannabinoids. The reinforcing effects of nicotine in nonhuman
primates, for example, have been historically difficult to demonstrate and are largely
Figure 1. Hypothetical dose-response curve under a THC-food schedule of
reinforcement. Subjects chose between different doses of THC (0 – 10 µg/kg/injection) and food pellets under a concurrent FR 30:FR30 schedule of THC injections and food availability. Abscissa: unit dose of THC in micrograms per kilogram per injection. Left ordinate: percent THC choice relative to food pellets. Right ordinate: Number of injections earned per session.
0 0.3 1 3 10
0
25
50
75
100
0
2
4
6
8
10
Unit dose THC (µg/kg/injection)
% T
HC
ch
oic
e
Nu
mb
er o
f inje
ctio
ns
THC choice
Number of injections
146
dependent on several specific factors including the use intermittent schedules of
reinforcement, drug history, and repeated presentation of associated stimuli (Goldberg
and Henningfield, 1988). Similar to the hypothesis tested in Chapter 2 for THC, it has
been suggested that one of the reasons behind the difficulty in demonstrating the
reinforcing effects of nicotine in laboratory animals is because nicotine also has
prominent aversive effects that may be masking its reinforcing effects. For example,
studies in squirrel monkeys have shown that response-produced injections of the same
doses of nicotine (0.01 – 0.03 mg/kg/injection) that maintain behavior under a fixed-
interval (FI) or second-order schedules of reinforcement can also function effectively to
suppress food-maintained responding in squirrel monkeys under FR reinforcement
schedules (Goldberg and Spealman, 1982). Further, it has been shown that squirrel
monkeys will respond at high rates to postpone the scheduled delivery of the same
doses of nicotine (0.03 and 0.056 mg/kg/injection) that function as a positive reinforcer in
other studies with squirrel monkeys (Spealman and Goldberg, 1983). These examples
demonstrate that nicotine can function as both a reinforcer or punisher depending on the
conditions governing its delivery. Therefore, the experimental manipulations that have
been successful in producing the reinforcing effects of nicotine may have also been
successful in limiting its aversive effects to a degree that the reinforcing effects could be
unmasked. In this regard, studies of nicotine self-administration should serve as an
excellent resource for studying the reinforcing effects of cannabinoids as similar
behavioral mechanisms between these drug classes could be determining their ability to
maintain behavior. The following section will describe the conditions that are most
effective for demonstrating the reinforcing effects of nicotine and will also include a
discussion on how these principles could be applied to uncovering the reinforcing effects
of cannabinoids.
147
Early nicotine self-administration studies using FR 1 reinforcement schedules
resulted in very low rates of responding (0.008 – 0.005 responses/second) maintained
by nicotine injections (Deneau and Inoki, 1967; Hanson et al., 1979; Lang et al., 1977).
Although these rates of nicotine-maintained responding were higher than rates of
responding maintained by saline, they were relatively insensitive to changes in dose and
pretreatments to the nicotine antagonist mecamylamine. Thus, it could not be discounted
that nicotine-maintained responding in these studies were due to nonspecific rate-
increasing effects. In contrast, Goldberg and Spealman (1978) and Spealman and
Goldberg (1982) reported much higher rates of nicotine-maintained responding under an
FI 5-min schedule of reinforcement with a one-minute timeout. During acquisition, 0.03
mg/kg nicotine was made available for self-administration and the FI value was gradually
increased to 5 minutes. Another contingency during acquisition was also added in which
nicotine was automatically injected if a response did not occur within 2 minutes after the
interval elapsed. Once rates of responding were stable, the automatic injections were
discontinued and nicotine subsequently maintained stable rates of responding at
approximately 0.1 responses/second. Ator and Griffiths (1983) also studied nicotine self-
administration under an FI 5-minute, one-minute timeout schedule of reinforcement in
baboons. However, in contrast to the studies in squirrel monkeys by Goldberg and
Spealman, much lower rates (0.007 – 0.02 responses/second) of nicotine-maintained
responding were achieved, which were only marginally distinguishable from rates of
responding maintained by saline. The major methodological differences between these
two studies, despite species differences, was that Ator and Griffiths (1983) made
available a lower dose (0.01 mg/kg/injection) of nicotine during acquisition and the use of
non-contingent nicotine injections during the acquisition period were not employed.
Taken together, these studies demonstrated that interval-based schedules by
themselves were no more effective than low FR schedules of reinforcement for
148
demonstrating the reinforcing effects of nicotine in nonhuman primates. However, the
combination of a fixed-interval schedule with the implementation of forced, automatic
nicotine injections and a relatively higher training dose initially made available for self-
administration may have been the optimal conditions for demonstrating high levels of
nicotine-maintained responding. Moreover, several other sets of studies in squirrel
monkeys have demonstrated that nicotine can maintain even higher rates of responding
when studied under a second-order schedule of reinforcement (Goldberg et al., 1981;
Spealman and Goldberg, 1982). Under these conditions, completion of an FR 10 during
a 1-, 2-, or 5-minute interval produced a brief visual stimulus that had been paired with a
nicotine injection; the first ratio completed after the interval timed out then produced both
the visual stimulus and an i.v. injection of nicotine, which was followed by a 1- or 3-
minute timeout. Peak rates of responding maintained by nicotine ranged from 0.8 – 1.7
responses/second at a dose of 0.03 mg/kg/injection. Thus, the peak rates of nicotine-
maintained responding under the second-order contingencies were 8 - 17-fold higher
compared to what was produced under the fixed-interval schedule. Considering the
interval between consecutive nicotine injections was similar between the two schedules,
the higher overall rates produced by the second-order schedule were most likely
attributable to the presence of contingent conditioned stimuli.
Overall, these findings with nicotine SA have several important implications for
studying the reinforcing effects of cannabinoids in preclinical animal models. First of all,
a critical determinant of robust levels of nicotine-maintained responding was due to the
use of intermittent schedules of reinforcement where the frequency of reinforcement was
independent of the rate of responding. This is in contrast to ratio-based schedules of
reinforcement where the frequency of reinforcement is contingent on the rate of
responding. Importantly, nicotine SA under a second-order schedule, which employed
the use of conditioned stimuli, produced the most robust level of nicotine-maintained
149
behavior. These findings with nicotine parallel the findings in Chapter 2 (Figure 6) of this
dissertation in which the reinforcing effects of THC were demonstrated in some subjects
under a second-order schedule but in no subjects under an FR reinforcement schedule
(prior to chronic treatment). Considering both nicotine and THC have well documented
negative effects, the effectiveness of interval-based schedules of reinforcement,
particularly second-order schedules, may be due to their ability to limit these negative
effects that are probably more apparent from fast accumulation of drug intake. Moreover,
as the FI nicotine SA studies by Spealman and Goldberg (1982) and Ator and Griffiths
(1983) showed, the use of automatic, forced injections to facilitate acquisition of SA may
be a feature that can be added to future THC SA studies to engender even greater
levels of drug-maintained behavior. Although the principle behind this method is similar
to what was tested in Chapter 2 (i.e., developing tolerance to the rate-deceasing effects
in order to increase levels of SA), forced injections during the session could be a more
effective way to test this hypothesis.
The methods used to establish ethanol as an oral reinforcer in experimental
animals can also be used to recognize potential behavioral and environmental factors
that may determine the reinforcing effects of cannabinoids. Similar to cannabinoids,
ethanol does not readily initiate and maintain behavior in experimental animals despite
its clear reinforcing effects in humans. For instance, when ethanol is initially presented to
monkeys, they typically only drink low levels of ethanol and water is generally preferred
over even a small concentration (5%) of ethanol (Mello, 1973; Meisch, 1977). This
discrepancy has been attributed to a variety of factors including the taste of alcohol, the
delay between consumption and the onset of pharmacological effects, the volume
needed for a pharmacological effect, and negative effects of alcohol. As a result,
initiation procedures are commonly used to establish the reinforcing effects of ethanol in
laboratory animals. One of the most common ways to induce robust oral ethanol
150
consumption is through a schedule-induction procedure. Schedule-induced drinking
involves the intermittently scheduled presentation of one reinforcer that results in an
increase in the reinforcing effects of another stimulus concurrently available in the
environment. This procedure was first described by Falk (1961) in which he observed in
rats that the intermittent delivery of food pellets at fixed intervals of time was
accompanied by the consumption of large quantities of water as a form of adjunctive
behavior. Rats were maintained at 80 percent of their free-feeding weight and would
consume approximately one half of their total weight in water within a few hours while
food pellets were presented intermittently. Subsequent experiments showed that
explanations for this phenomenon were not attributable to thirst, as pre-session water
loading did not reduce polydipsic drinking (Falk, 1969), or by adventitious reinforcement
(Falk, 1964).
Since then, schedule-induced polydipsia has been widely used to address the
problem of demonstrating the reinforcing effects of ethanol in preclinical animal models.
The application of this procedure to induce high levels of ethanol drinking typically
involves food restricting an animal and then making a certain concentration of ethanol
available with small quantities of food delivered intermittently either contingently under
an FI or schedule or non-contingently under a fixed-time (FT) contingency. For instance,
in rodents, the optimal conditions typically involve an FT 2-min schedule of pellet
presentation to induce drinking of 5% v/v ethanol (Falk, 1966; Falk et al., 1972; Samson
and Falk, 1975). In cynomolgus monkeys, Shelton et al. (2001) determined that 4% (w/v)
ethanol and FT 5-min was the optimal schedule of food pellet delivery to induce
consumption of 1.0 g/kg ethanol. Other studies in monkeys have also demonstrated that
schedule-induced ethanol consumption can be produced without food restriction (Grant
and Johanson, 1988; Vivian et al., 2001). Furthermore, an important feature of this
method is that high concentrations of ethanol can maintain behavior following the
151
discontinuation of the scheduled delivery of food pellets (Vivian et al., 2001; Grant et al.,
2008).
Taken together, the effectiveness of schedule-induced procedures for
demonstrating the reinforcing effects of ethanol may represent an environmental factor
that is important for demonstrating the reinforcing effects of cannabinoids. Indeed,
schedule-induction has been used for intravenous SA of several drugs in rodents (Singer
et al., 1982) as well as for nicotine in nonhuman primates (Slifer, 1983; Slifer and
Balster, 1985). Only one study, however, has reported the use of a schedule-induction
procedure to study the reinforcing effects of THC (Takahasi and Singer, 1979). In this
study, THC (6.25-50 µg/kg) and vehicle were made available to rats under an FR 1
schedule of reinforcement during 1-hour sessions while food pellets were concurrently
delivered under an FT 1-min reinforcement schedule. This study included four groups of
rats, in which THC SA was assessed both in the presence or absence of the schedule-
induction procedure and at either 80% reduced body weight or at free-feeding weight. It
was found that reinforcing effects of THC were achieved only in the food-restricted
animals tested on an FT 1-min schedule. The peak number of infusions, 15.14 ± 4.31,
was maintained by 125 µg/kg, which was significantly different than the number of
injections earned when vehicle was available during the FT 1-min schedule (~ 4
injections).
Although the Takahasi and Singer (1979) study was successful in demonstrating
the initiation of THC’s reinforcing effects through the use of a schedule-induction
procedure, some limitations were also present. For instance, the reinforcing effects of
THC were only assessed when the inducing schedule was in effect. However, future
studies should also examine the maintenance of those reinforcing effects when
scheduled pellet delivery is discontinued and the necessary conditions may need to be
developed in order to do so. For instance, the schedule-induced ethanol self-
152
administration literature indicates a bitonic function that describes the relationship
between the FT interval of pellet delivery and the volume of ethanol consumed following
schedule removal with maximal intake occurring at FT values of 2-3 minutes for rats and
monkeys (see Ford, 2014). However, the optimal inter-pellet interval may be different for
inducing cannabinoid SA and subsequent maintenance of reinforcing effects. Thus,
future studies should pay particular attention to the relationship between the length of
the interval between pellet delivery and the amount of THC self-administered. The
optimization of such a procedural variable may be the difference between maintaining
cannabinoid SA after the removal of the inducing schedule.
Another aspect of ethanol SA procedures, particularly in rodent models, that
could apply to producing high levels of cannabinoid SA involves the frequency of drug
access. For instance, compared to ethanol SA protocols using a continuous-access-
drinking paradigm (i.e., 24 hrs/day, 7 days/week), intermittent access to ethanol SA (i.e.,
24 hrs/day, 3 days/week: typically Monday, Wednesday, and Friday) in rodents yields
considerably higher levels of ethanol intake (Wayner et al., 1972; Wise, 1973; Simms et
al., 2008). The effectiveness of this procedure may in part reflect negative reinforcement
to alleviate ethanol-induced symptoms of withdrawal. The potential effectiveness of
intermittent access to increase the reinforcing effects of THC may also involve negative
reinforcement, although probably unlikely considering the long half-life of THC
attenuates profound physical withdrawal (Hollister, 1986) and the doses required to
produce physical symptoms of withdrawal in nonhuman primates, for example (2.0
mg/kg/day; Stewart and McMahon, 2010), are unlikely to be self-administered in one
daily session. Instead, intermittent access to THC SA may allow time for other, more-
subtle drug effects to dissipate that may be inhibiting the expression of reinforcing
effects. Future studies should test this hypothesis by designing THC SA studies that
153
intersperse 24 hr-48 hour abstinence periods in between SA sessions, instead of making
the drug available for SA across consecutive daily sessions.
Using drug discrimination to complement THC self-administration
Until the cannabinoid SA procedure has been further refined to produce
consistent, reproducible reinforcing effects across multiple subjects and species, the
drug discrimination assay represents a valid alternative in terms of cannabinoid abuse
liability assessment. The discriminative stimulus effects of a drug have been suggested
to be analogous to human subjective effects (Schuster and Balster, 1977; Holtzman,
1985). Thus, although the drug discrimination procedure does not measure the
reinforcing effects of drugs per se, drugs that share discriminative stimulus effects
usually have similar reinforcing effects as a result of similar pharmacological
mechanisms of action (Schuster and Johanson, 1988).
As such, the discriminative stimulus effects of THC have been produced in
multiple experimental animal species and across several different laboratories (Tanda,
2016). Drug discrimination with cannabinoids is pharmacologically selective such that
naturally occurring psychoactive cannabinoids fully substitute for THC and drugs from
other classes do not. Thus, THC is a well-established training drug that can provide
rapid feedback on the underlying pharmacological mechanisms and abuse liability of
substituted novel cannabinoid compounds. For instance, there are a number of potential
therapeutic applications of cannabinoids involving their analgesic, antiemetic,
antiglaucoma, and anticonvulsive effects (Hill, 2015). However, a cannabinoid
compound considered for these uses would be void of any clinical utility if there also
exists abuse potential of that compound since THC is Schedule I. Therefore, the
evaluation of a candidate compound to produce THC-like discriminative stimulus effects
is an effective way to test for this, with the goal of developing one that either does not
154
substitute for THC or retains its therapeutic activity at doses below those that produce
THC-like discriminative stimulus effects. Moreover, cannabinoid drug discrimination
procedures can be useful for evaluating potential pharmacotherapies for cannabis
abuse. For instance, drugs that block or attenuate the discriminative stimulus effects of
THC might be considered for achieving abstinence or preventing relapse. On the other
hand, drugs that partially substitute for THC-like discriminative stimulus effects might be
considered as candidate agonist medications, similar to methadone for opioid
dependence, that can reduce cannabis SA or alleviate withdrawal symptoms associated
with relapse without too much concern for the abuse potential of the medication itself.
This is not to say, however, that drug discrimination should serve as a
replacement for cannabinoid SA. Although there is good correlation between a drug’s
subjective effects and its reinforcing effects, the SA procedure measures the reinforcing
effects of a drug directly, along with all the other complexities that determine its ability to
maintain behavior, which offers a more detailed perspective in terms of abuse liability
screening and pharmacotherapy assessment. For instance, drug-induced changes in
operant behavior can vary dramatically as a function of rates and patterns of behavior
independent of the reinforcer that maintains behavior (Kelleher and Morse, 1968).
Therefore, one can assume that even though a drug attenuates the discriminative
stimulus effects of THC, the effects of that drug to also attenuate the reinforcing effects
of THC may be more ambiguous depending on factors such as the contingencies for
THC reinforcement or other prevailing environmental conditions. Similarly, a drug that
substitutes for the THC stimulus may not necessarily produce reinforcing effects in the
self-administration procedure also depending on the schedule contingencies and other
trait/state variables. The point is that both drug discrimination and self-administration
should be used hand-in-hand to gain information about the abuse-related effects of
drugs of abuse from two different perspectives. For example, the optimal
155
pharmacotherapy may be one that reduces the reinforcing effects of THC, but not the
discriminative stimulus effects - this drug profile would be indicative of good therapeutic
efficacy without sacrificing medication compliance. Such an assessment could not be
gained from only using one procedure. However, until cannabinoid SA is more
developed, THC discrimination can provide useful information on cannabinoid
pharmacology, the abuse potential of novel cannabinoid therapeutic agents, and
potential pharmacotherapies to treat cannabis abuse.
Going forward, cannabinoid discrimination studies should also be utilized to
better understand the pharmacological variables that may influence cannabinoid SA. For
instance, a strength of the drug discrimination assay lies in its capacity to investigate not
only the receptor mechanisms of a drug class, but also other stimulus effects of a drug
such as time course of a drug (e.g. onset of behavioral effects, duration of action, etc.),
which have been shown to significantly affect the SA of other drugs of abuse (Balster
and Schuster, 1973; Stretch et al., 1976; Kato et al., 1987; Beardsley and Balster, 1994).
THC, in particular, has a relatively delayed onset of its behavioral effects and extended
elimination phase (Ohlson et al., 1980; Hollister et al., 1981). This time course of effects
is significantly different compared to other drugs that are readily self-administered (e.g.,
cocaine, methamphetamine, opiates, etc.) so it should not be assumed that similar self-
administration parameters will be effective for demonstrating the reinforcing effects of
cannabinoids. Instead, a more careful examination of the pharmacological profile of
cannabinoids needs to be taken into account and drug discrimination is one tool that can
elucidate many of these factors. For instance, in a study of the discriminative stimulus
effects of THC in rats, Prescot et al. (1992) showed that maximal THC-appropriate
responding did not occur until 60 min after intramuscular injection. Furthermore, this
study also showed that THC-appropriate responding persisted until 6 hours after initial
administration. These results suggest, inasmuch as discriminative stimulus effects
156
predict reinforcing effects, that THC’s delayed onset and long duration of action could be
preventing the expression of its reinforcing effects in studies that do not account for
these variables. Specifically, the delayed onset in action of THC could be functioning to
uncouple the behavioral effect of the drug from the response that produced it. On the
other hand, THC’s long duration of action could be preventing persistent SA within a
session either through satiation or decreased discriminability of additional drug
injections. In order to investigate these possibilities, future SA studies should implement
even longer intervals between drug injections than 10 minutes used during the second-
order schedule in Chapter 2.
Moreover, THC discrimination time course studies in the context of developing
the THC SA model should also be conducted with intravenous THC considering that is
the route of administration most likely to be used for contingent delivery. Future studies
should also attempt to train the discrimination of lower training doses of THC. For
instance, 0.1 mg/kg THC is typically used as the training dose in most drug
discrimination studies using nonhuman primates (McMahon 2006, 2011), which is
considerably higher than the unit dose of THC (0.003 mg/kg) that has reinforcing effects
in studies with squirrel monkeys and for the subjects in Chapter 2. Thus, the examination
of the discriminative stimulus effects of lower doses of THC would provide a better
understanding of the stimulus effects of doses of THC that are more likely to be self-
administered as opposed to studying the effects of a single, bolus dose that is probably
more of a reflection of the total intake during a self-administration session. However, the
relationship between doses of a drug that produce reinforcing effects compared to those
that produce discriminative stimulus effects should be interpreted cautiously. Previous
studies in nonhuman primates that were trained to discriminate and self-administer the
same drug demonstrated that, in general, the reinforcing effects of that drug were more
potent than its discriminative stimulus effects (Hoffmeister, 1988; Ator, 2002; Martelle
157
and Nader, 2009). For example, each of these studies showed that there were certain
doses of a drug that did not engender drug-appropriate responding during drug
discrimination but functioned as a reinforcer during self-administration. Some of these
discrepancies may be due to the training dose used for discrimination as this factor has
been shown to influence the potency of the dose-response curve (e.g. Terry et al., 1994;
Mumford and Holtzman, 1991; Young et al., 1992). Nevertheless, drug discrimination
can serve is an excellent platform to investigate many of the pharmacological factors
that may be related the reinforcing effects of cannabinoids.
Route of administration
One possibility for unsuccessful attempts at establishing cannabinoid SA in
preclinical animals may involve the route of administration, which is typically delivered
intravenously in preclinical animal models of SA, whereas in humans, cannabinoids are
most commonly abused via pulmonary SA (i.e., inhalation). Studies have shown that the
pharmacokinetics and metabolism of THC following intravenous injection are essentially
the same as inhalation with both routes producing rapid attainment of peak plasma
concentrations and a rapid distribution phase at similar time courses (Hollister et al.,
1981). Moreover, both routes produced a similar time course and magnitude of self-
reported ratings of feeling “high” (Hollister et al., 1981). Thus, the pharmacokinetic
parameters of THC and its positive subjective effects (e.g., subjective “high”) do not
appear to be different between both routes of administration. However, studies have also
shown that many adverse effects are reported by subjects when administered
intravenous THC, as well as smoked THC, such as dysphoria, anxiety, paranoia, and
nausea (D’Souza et al., 2008; Carbuto et al., 2012). In one study directly comparing the
psychotropic effects of both routes in humans at doses that produced similar plasma
concentrations, the adverse effects of intravenous THC were much more pronounced
158
compared to pulmonary administration (Naef et al., 2004). For instance, intravenous
administration produced more subjective reports of feeling irritated, anxious, tenseness
and aggressiveness, confusion and disorientation, nausea, headache, difficulties
breathing, and vertigo (Naef et al., 2004). Significantly lower amounts of the
psychoactive metabolite 11-OH-THC were formed following pulmonary administration
due to the absence of first-pass metabolism, which may partly explain the differences in
psychotropic side effects compared to intravenous administration. Thus, the more
pronounced adverse effects of intravenous THC may be overriding its reinforcing effects
when delivered through this route of administration, which could be playing a role in
preventing persistent THC SA in preclinical animal models. As a result, pulmonary
administration may increase the likelihood of THC functioning as a reinforcer in animal
models because the adverse effects that may be overriding the reinforcing effects are
more limited. Indeed, the development of inhaled delivery systems of THC have been
recently validated (Manwell et al., 2014; Nguyen et al., 2016); however, future studies
should continue to develop these technologies and incorporate them into operant
procedures to allow the study of factors mediating THC’s reinforcing effects.
Interactions of THC with other cannabinoids
Moving forward, it is important to consider that the Cannabis sativa plant contains
hundreds of known compounds and at least 70 cannabinoids in addition to THC (Elsohly
and Slade 2005). Although the present study provides no evidence of interactions
between THC and other marijuana constituents, it does not preclude the possibility that
such interactions may influence other mechanisms that are not reflected in the
behavioral endpoints studied, such as possible activity at non-CB1 sites or at CB1
receptors that are part of neural circuits distinct from those involved with reinforcement
or rate-decreasing effects. Cannabidiol (CBD), for instance, is another major
159
cannabinoid found in cannabis, and has opposing effects on CB1 and CB2 receptors
compared to THC. Although hashish may contain equal parts CBD and THC (ElSohly et
al, 2003; Hardwick and King, 2008; Potter et al, 2008), CBD is typically present in low
concentrations in recreational cannabis (<0.1%), based on samples seized by law
enforcement. There are multiple reports of CBD and THC interacting to modify each
other’s behavioral effects with some studies showing enhancement (Karniol and Carlini,
1973; Takahashi and Karniol, 1975) or antagonism (Karniol and Carlini, 1973; Borgen
and Davis, 1974; Karniol et al., 1974), depending on the endpoint of interest. In
particular, antagonism was previously reported in rhesus monkeys performing operant
conditioning and cognitive-based tasks (Brady and Balster, 1980; Wright et al., 2013). It
has also been shown that CBD significantly enhanced the potency of THC to produce
discriminate stimulus effects (McMahon, 2016), albeit at a dose 100 times greater than
THC, which far exceeds the amount obtained from marijuana.
Furthermore, there is evidence that CBD may reduce anxiety or transient
psychosis-like side-effects of THC observed in infrequent cannabis smokers or when
administered alone to patients with anxiety or psychosis (Bhattacharyya et al, 2010;
Crippa et al, 2011; Bergamaschi et al, 2011; Leweke et al, 2012; Niesink and van Laar,
2013). Older studies report that CBD changes the type of psychological reaction induced
by THC in infrequent cannabis smokers, reducing their anxiety and thereby rendering
THC more enjoyable (e.g., Karniol et al, 1974). Taken together, the addition of CBD to
THC may be a potential strategy for enhancing the reinforcing effects of THC compared
to just studying the drug by itself. The key will be finding an appropriate concentration of
CBD in combination with THC that attenuates THC’s rate-decreasing effects without
simultaneously cancelling out its reinforcing effects.
160
Relationship between THC and polysubstance use
Finally, one additional factor that deserves mention includes interactions of
cannabis with other substances. For example, co-use of marijuana and alcohol
increases risk for motor vehicle crashes (Hingson, et al., 2005), short- and long-term
memory impairment (Pope & Yurgelun-Todd, 1996), psychological disorders (Grech et
al., 2005; Hall 2009), and lower educational performance and attainment (Brook et al.,
1999; Lynskey and Hall, 2000; Brook et al., 2011; Arriaet al., 2013;). Tobacco and
marijuana use increases the risk for adverse respiratory and cardiovascular effects
(Tashkin, 1990; Polen, et al.,1993; Zhang et al., 1999; Mittleman et al., 2001; Aryana
and Williams, 2007) and increased susceptibility to cancer (Hashibe et al., 2005).
Additionally, we have shown that THC, in combination with cocaine, produced an
increase in the relative reinforcing strength of cocaine compared to cocaine alone as
assessed in a cocaine-food choice procedure (Figure 2A). In contrast to the low-efficacy
partial agonist THC, the full CB receptor agonist CP55,940 shifted the cocaine dose-
response curve to the right (Figure 2B), perhaps suggesting a novel treatment for
cocaine abuse involving full agonists at CB receptors. A better understanding of the
behavioral and neuropharmacological interactions of marijuana and other substances
will have important implications for the treatment of co-abuse.
161
Conclusions from studies in Chapter 2
Across all conditions, cannabinoid agonists (i.e., CP55,940 or THC) functioned
as a reinforcer in 7 of 13 monkeys studied (54%). Although our hypothesis that
attenuating the rate-decreasing effects of THC would enhance its reinforcing effects was
not supported considering reinforcement was not obtained in every subject, it should not
go unnoticed that these experiments have provided the most successful demonstration
of cannabinoid reinforcement in nonhuman primates in terms of number of subjects
acquiring SA and level of SA, besides only the group at NIDA-IRP using squirrel
monkeys (Tanda et al., 2000). Thus, these studies provide an important foundation for
which future investigations to build on and highlight the presence of individual
differences involved in the abuse-related effects of cannabinoids. Future research
Figure 2. Effects of THC (A) and CP55,940 (B) pretreatment on cocaine self-
administration under a food-drug choice procedure in rhesus monkeys (n = 4). Both drugs were administered intravenously one minute before the session. Abscissae: Unit dose of cocaine available for self-administration in mg/kg/injection. Left ordinate: Percent cocaine choice relative to food reinforcement. Right ordinate: Percent food choice relative to cocaine reinforcement. Data represent mean of at least two determinations per dose of THC for each monkey. *, p < 0.05. Unpublished data from John, Martin, and Nader, 2016.
162
should focus, rather than disregard, these individual differences in order to identify
behavioral, environmental, or biological determinants of cannabinoid self-administration
that can be further targeted to optimize the model. As discussed earlier,
recommendations for future studies examining the behavioral mechanisms underlying
THC reinforcement might include the use of intermittent or choice schedules of
reinforcement coupled with an initial induction procedure such as non-contingent drug
exposure and/or fixed-time schedules of food-delivery.
Cognitive effects of THC
The other goal of the research within this dissertation was to characterize the
effects of chronic THC treatment on cognitive performance, including the residual effects
(i.e., 22 hours after daily administration during chronic treatment) and during several
weeks of abstinence. Cognitive-enhancement is an emerging area for the treatment of
cannabis use disorder (Sofuoglu et al., 2013); however, it is not clear what specific
cognitive functions are most strongly related to improved treatment outcome. Much of
this discrepancy comes from the inconsistent results in human studies examining the
effects of long-term marijuana use on cognitive performance. For instance, the
interpretation of many studies are complicated by several variables such as the
heterogeneity of the participants, differences in drug histories and social variables, as
well as concurrent use of other substances, all of which are difficult to experimentally
control. In addition, studies in humans cannot distinguish whether marijuana abusers
exhibit innate cognitive differences prior to initiating drug taking. Therefore, it is difficult
to clearly establish a causal role of long-term THC exposure in producing cognitive
deficits. However, the use of preclinical animal models can control for these variables in
a systematic manner but have unfortunately been underutilized as it relates to
longitudinal, within-subject assessments of THC on cognition.
163
In Chapter 3, the effects of chronic THC exposure on four cognitive domains
were examined: working memory, discrimination learning, reversal learning, and
attentional set-shifting. Each of these cognitive domains are mediated by a different
neurobiological substrate and are integral to facilitating the behavioral change necessary
for positive treatment response (Rezapour et al., 2016). However, studies in chronic,
heavy cannabis users show that impairment within these domains is equivocal
depending on the time since last use (Crean et al. 2011; Crane et al., 2013). Thus, in
order to better clarify the specificity and temporal ordering of effects, the studies in
Chapter 3 examined the acute effects of THC on cognition before and during chronic
THC treatment (1.0-2.0 mg/kg/day, s.c.) and the residual effects of daily THC treatment
(i.e., cognition assessed 22 hrs after daily THC administration) as well as the
persistence of effects during abstinence.
It was found that the acute effects of THC were task-specific and produced
deficits to working memory in a delay-dependent manner, as well as deficits in
compound discrimination learning, and extradimensional set-shifting; tolerance
developed to those effects during chronic treatment. Furthermore, during chronic
treatment, THC produced persistent impairments 22 hours after administration to
working memory when the cognitive load was high but not to discrimination and reversal
learning or attentional set-shifting performance. Impairments to working memory
recovered within 2 weeks of abstinence. The implications of these findings will be
discussed in the following sections.
Task Specificity
One of the major findings of this work involves the task specificity on which THC
produced both its acute and residual effects. This finding is an important contribution to
the overall theme of this dissertation involving the behavioral mechanisms of THC - the
164
notion that the behavioral effects of THC cannot be predicted based on its inherent
pharmacological properties. Consistent with the findings in Chapter 3, other studies in
nonhuman primates have also found task-specific effects of THC on cognitive
performance. For instance, Winsauer et al. (1999) found that THC impaired the
acquisition of a novel stimulus-response chain but not the expression of an already-
learned chain of response. Furthermore, Kangas et al. (2016) found that short-term
memory assessed under the DMS task was most vulnerable to impairment by acute
THC pretreatment in squirrel monkeys, followed by discrimination reversal while simple
discrimination learning was not affected. Moreover, in the only other study in nonhuman
primates that has assessed the residual effects of THC on cognition, Verrico et al.
(2014) found that adolescent rhesus monkeys were impaired by THC (0.2-0.3 mg/kg,
i.v.; 23 hours after drug administration) on a spatial working memory task (i.e. location-
recall) but not an object working memory task (i.e. object-recall). Overall, these findings
suggest cognitive performance may be most susceptible to impairment by THC when the
retention of information over time or the flexible alteration of responses based on
ongoing within-trial contingencies are required.
As discussed in Chapter 3, the basis for THC to affect cognition in a task-specific
manner is unclear but may be related to CB1 receptor function in different brain regions
that mediate such tasks. For example, the control of discrimination reversal performance
appears to be highly localized in the orbital prefrontal cortex (Chudasama, 2011),
whereas object working memory is thought to be controlled by dorsolateral prefrontal
systems (Hampson et al., 2009) and hippocampal mechanisms (Eichenbaum et al.,
1992) while spatial working memory likely involves more frontal-temporal circuitry
(Browning and Gaffan, 2008). As such, CB1 receptors are widely expressed throughout
the brain (Freund et al., 2003); however, recent studies have indicated that CB1 receptor
dynamics (e.g., desensitization and downregulation) differ across brain regions (Lazenka
165
et al., 2013). For example, Sim-Selley et al. (2006) treated mice chronically with THC or
the CB1 full agonist WIN55,212-2 and showed that after cessation of treatment,
cannabinoid-induced decreases in CB1 receptor function persisted for two weeks longer
in the hippocampus compared to the striatum. These findings are consistent with recent
data from positron emission tomography imaging studies in human marijuana users that
showed slower recovery of CB1 receptors in hippocampus than in other brain regions
(Hirvonen et al., 2012). Considering the significant hippocampal mechanism involved in
working memory performance, these data support the task specificity of THC to impair
DMS performance in the present study and in others.
Alternatively, it may be that differences in the relative potencies of THC across
tasks most directly reflect differences in the difficulty of the tasks, i.e., more difficult tasks
may be more vulnerable to cannabinergic drug action, resulting in higher potency and
larger effects. For example, Branch et al. (1980) showed that squirrel monkeys trained to
emit a 5-key response sequence were more sensitive to acute doses of THC than when
emitting a 2-key sequence. In this regard, performances under all tasks used in the
present study required discriminative behavior but differed in complexity. That is, simple
discrimination trials are simple stimulus-response associations, compound discrimination
adds complexity with the presentation of another stimulus dimension, and discrimination
reversal, intradimensional/extradimensional shift is made even more complex due to the
unsignaled shift in contingency. DMS is perhaps the most complex task among the ones
used here because accuracy depends on a conditional discrimination (i.e., S+ and S-
contingencies are conditional on the previously presented sample) and, moreover, a
delay between the presentation of sample and comparison stimuli. Thus, although the
extent to which the potency of THC depends on task complexity may be difficult to
determine a priori, the present results support the view that this factor at least
contributes to the behavioral mechanisms of THC cognition-related studies.
166
Implications for chronic THC exposure to produce tolerance to its acute cognitive
effects
These findings for both the acute and persistent effects of THC on cognitive
function have several important implications. First, as touched on briefly in Chapter 3,
the differential acute effects of THC on cognitive performance as a function of chronic
THC exposure may bear an important implication for establishing thresholds of THC that
can be used to determine impairment for driving or other tasks. Ideally, a crash risk ratio
of THC would be calculated similar to alcohol in which there is a direct comparison
between the level of THC in the blood and impairment for driving. To this end, Colorado
recently enacted a law stipulating a threshold of 5 ng/ml of THC in blood for driving
under the influence, based on other published reports (Hartman and Huestis, 2013;
Walker et al., 2013). Another study indicated that blood levels below 10 ng/ml were not
associated with increased accident risk, and recommended a threshold of 7–10 ng/ml for
driving under the influence (Grotenhermen et al., 2007).
However, based on recent research, what these thresholds don’t take into
account is the level of tolerance chronic THC exposure produces to the acute
intoxicating effects of THC. For instance, one study reported that in occasional (≤1
time/week) cannabis users, smoked cannabis (500 μg/kg) produced significant
impairment of tracking performance, divided attention, and inhibitory control but only
modestly affected inhibitory control in heavy (>4 days/week) users (Ramaekers et al.,
2009). Similarly, in a sample of heavy cannabis users (>4 days/week for more than 2
years), smoked cannabis (700 μg/kg) produced no impairment on tasks measuring
tracking errors or reaction time, and only produced modest effects on a divided attention
task (Schwope et al., 2012). Levels of THC and its major metabolites were measured in
that study, and remained above the legal threshold of 5.0 ng/ml for 2–4 h after cannabis
167
consumption. These findings are also consistent with a study in nonhuman primates
showing that monkeys with a chronic history of THC (1.0 mg/kg/12h for several years)
compared with monkeys intermittently exposed to THC (0.1 mg/kg every 3-4 days)
resulted in greater tolerance to the hypothermic and rate-decreasing effects following
administration of 3.2 mg/kg THC (Ginsburg et al., 2014). THC blood levels were also
measured in this study, however, no differences in blood levels of THC or its metabolites
were present between the groups. These findings are consistent with others showing
that tolerance does not develop to the pharmacokinetics of THC despite tolerance to its
behavioral effects. Interestingly, although maximal THC blood levels occurred 20
minutes after injection for both groups (457.0 and 355.2 ng/ml for the intermittent and
chronic groups, respectively), maximal effects of THC to decrease response rates and to
produce hypothermia in the intermittent group did not occur until 120 minutes after
injection, after THC blood levels had substantially decreased. By 12 hours and 24 hours,
effects on temperature and response rate was not different from control levels although
blood THC levels for both groups were approximately 20 ng/ml, well above the legal
threshold of 5 ng/ml. Taken together, these studies indicate that blood levels of THC do
not necessarily reflect the behavioral effects of the drug when drug history is not taken
into account.
The findings in Chapter 3 further add to this literature by using a within-subject
study design to show that chronic THC exposure produced tolerance to the acute
cognitive impairing effects of THC. Moreover, tolerance to the cognitive effects of THC
such as working memory, that involves attentional processes and the active processing
and manipulation of information, may more closely parallel the demanding task of driving
a vehicle. Although the experiments in Chapter 3 were not designed to directly address a
blood threshold that generally reflects impairment to the acute effects of THC, these
findings add to the body of literature demonstrating that a chronic history of THC results
168
in tolerance to its acute behavioral effects, even when that behavior is complex and
under strict stimulus control. It can be speculated based on previous studies in humans,
however, that THC blood levels were above the legal threshold of 5 ng/ml following
acute administration. For instance, i.v. administration of 5.0 mg THC (approximately 0.07
mg/kg) in a group of frequent cannabis users produced an average blood level of 20
ng/ml at 30 minutes post administration, the same time that cognitive assessment
occurred in Chapter 3. Furthermore, THC levels declined rapidly to about 3 ng/ml at 4
hours post injection. These data offer a good comparison because the same route of
administration was used and the dose range is within the range (0.03-0.056 mg/kg) to
which tolerance developed during chronic treatment in Chapter 3. Taken together, these
data suggest that the current threshold of 5 ng/ml may be too low in many cases and
that drug history needs to be a critical component for determining impairment.
Pre-existing vs. drug-induced cognitive impairment
The studies in Chapter 3 also have implications regarding the extent to which
cognitive impairments are caused by chronic drug use itself. For instance, while there
may be a causal relationship between chronic drug use and cognitive impairment,
individuals with innate cognitive deficits may also be more vulnerable to initiating drug
use and/or becoming drug dependent (Wagner et al., 2012). In order to address this
issue, the studies in Chapter 3 used a within-subjects study design that allowed for the
assessment of THC on cognition from initial to chronic exposure and during abstinence.
Thus, it was demonstrated that the residual effects of THC to impair cognitive function
were a consequence of the drug exposure itself as opposed to innate differences in
baseline cognitive functioning. Such an assessment is more difficult in studies with
human subjects as group designs comparing marijuana users with healthy control
subjects are typically used. As a result, there is always the looming question of whether
169
cognitive deficits associated with chronic cannabis use predated the initiation of drug use
and were instead, a factor that rendered individuals more vulnerable to chronic use in
the first place. Some studies, however, compare heavy cannabis users against a control
group of infrequent users rather than a control group of nonusers (e.g., Pope et al.,
1996; Whitlow et al., 2004). To some extent, this approach controls for some
confounding factors associated with using a healthy control group; however, with each
subject serving as its own control, the present results unequivocally demonstrate that
THC-induced cognitive deficits were from the drug itself and not a result of baseline
differences between control groups.
The implications of these findings bears particular importance in terms of the
feasibility of cognitive enhancement as a treatment approach. For instance, if innate
cognitive impairment contributed to the initiation of cannabis use then cognitive
rehabilitation as a treatment strategy may be more challenging because of a potential
ceiling effect in cognitive capabilities. However, the present study shows that cognitive
deficits may be produced by the drug itself and in that case, there may be more room for
cognitive enhancement to be an effective treatment option because baseline function
would be the endpoint as opposed to the starting point.
Of course, in a treatment context it may be very difficult to discern whether
cognitive impairments were from the drug itself or a trait variable that contributed to the
initiation of compulsive use. One strategy would be to compare cognitive performance at
the time of treatment to scholastic measures that have been taken prior to the initiation
of use such as test scores, IQ tests, behavioral assessments etc. in order to obtain some
sort of within subject comparison. However, the practicality of having access to the
necessary resources for such an approach is uncertain. Nevertheless, if cognitive
enhancement increases general functioning, it has the potential to influence substance
use outcomes regardless of whether substance abuse arises secondary to cognitive
170
impairment or vice versa. In reality, cognitive impairments in most cannabis users are
likely a combination of pre-existing vulnerability factors, long-term drug effects,
withdrawal effects, and acute drug effects; however, a better understanding of these
factors will provide clinicians with a better understanding of when a treatment works and
will allow clinicians to maximize the effectiveness of cognition-based treatments.
Working memory as a treatment target for cannabis use disorder
Furthermore, the results in Chapter 3 show that working memory was the only
cognitive domain examined that showed impairment 22 hrs after THC administration
during chronic treatment with deficits persisting until two weeks of abstinence. Therefore,
these findings suggest that working memory, in particular, may represent a
neurocognitive domain that may be predictive of treatment outcome or one that is
particularly sensitive to therapeutic interventions in cannabis dependent patients.
Several studies have suggested that working memory function is linked to
inhibitory control in that high working memory demand or impairments to working
memory function may facilitate drug craving or relapse during abstinence (Chambers et
al., 2009). For instance, Hester and Garavan (2004) showed that under high working
memory demand, cocaine users have reduced response inhibition, as measured by a
Go-No/Go task, compared to healthy controls. Poorer working memory performance has
also been shown to be predictive of relapse in abstinent smokers (Patterson et al.,
2010). Surprisingly, the impact of cognitive function on treatment outcomes for cannabis
use disorder has not been well-studied. The only such study to date evaluated cognitive
functioning in 20 marijuana-dependent patients at treatment entry and its relation to
retention and drug use outcome in motivational enhancement therapy and cognitive
behavioral therapy (Aharanovich et al. 2008). It was found that cognitive impairments in
abstract reasoning, spatial processing, and processing accuracy were predictive of poor
171
treatment retention but not rates of abstinence. There was no association between
performance on a memory task and treatment retention or outcome, although this study
was limited by small sample size and it is unclear whether the particular task was
sensitive enough to detect impairments. However, given the evidence of working
memory and other cognitive functions to predict the success of behavioral treatment
programs in cocaine and cigarette users, future studies are likely to find cognitive
functions that are predictive of treatment outcomes in marijuana users as well. The
selection of validated cognitive tests that include high levels of cognitive demand that
would be most sensitive to detecting impairments (i.e. in order to avoid false negatives)
will be a crucial step.
In addition to the prognostic significance of working memory, the basic research
in Chapter 3 also has implications for working memory to serve as a cognitive domain
that could be targeted to improve the effectiveness of treatment in cannabis abusers.
Working memory has been recognized as one dimension of inhibitory control so in
theory, therapeutic approaches aimed at improving working memory could also help
restore impairment within inhibitory control functions, which are important for overriding
pre-potent responses, such as drug-taking behavior in response to drug cues (Sarter et
al., 2006). A growing amount of empirical evidence also supports working memory as a
treatment target for addiction (Klingberg, 2010). For instance, it has been shown that
working-memory training in alcoholics reduced alcohol use for more than one month
after training (Houben et al., 2011). Consistent with the association between working
memory and inhibitory control, it was also found that training had an effect on alcohol
use for those with strong automatic preferences for alcohol, indicating working memory
training may increase control over the underlying automatic processes that drive alcohol
use. Furthermore, Bickel et al. (2011) showed that working memory training improved
delay discounting amongst stimulant users, suggesting that such training may lead to a
172
greater ability to attend to future consequences and thus reduce impulsive decision
making that could contribute to relapse during abstinence. Cognitive rehabilitation
approaches aimed at targeting working memory specifically have not yet been evaluated
among patients with cannabis use disorder. However, the results in Chapter 3
demonstrating that chronic THC exposure produces persistent effects within this domain
combined with promising evidence in alcoholics and stimulant abusers indicate that the
incorporation of working memory training into behavioral treatments should be explored.
THC-induced alteration to brain activity independent of cognitive performance
Although working memory was the only cognitive domain impaired during chronic
THC treatment, the lack of impairment during performance within the other domains (i.e.,
discrimination learning, behavioral flexibility, executive function) should not be
completely disregarded in terms of clinical significance. First, as discussed previously,
the complexity of the tasks assessing these domains of cognition may not have been
sensitive enough to detect impairments during chronic THC treatment. Second,
functional neuroimaging studies showed that despite similar performance on certain
cognitive measures, the underlying neural mechanisms mediating such performance
may still be affected (Batalla et al., 2013). These findings indicate that neuroadaptations
may occur during chronic THC exposure in which additional brain regions are recruited
in order to maintain normal cognitive functioning and compensate for the THC-induced
deficits.
As it relates to neuroadaptations in human marijuana users, Chang et al. (2006)
used fMRI to compare a visual attention task in current and abstinent cannabis users
with healthy controls. Despite all groups showing normal task performance, both active
and abstinent chronic cannabis users demonstrated decreased activation in the right
prefrontal, medial and dorsal parietal cortices and medial cerebellar regions but greater
173
activation in several smaller regions throughout the frontal, posterior parietal, occipital
and cerebellum. The authors suggested that their findings may reflect a compensatory
role for these regions in mitigating the effects of abnormal attentional and visual
processing following chronic cannabis exposure. Furthermore, in a group of abstinent
cannabis users, Tapert et al. (2007) compared the activation pattern on a go/no-go task
during fMRI with seventeen healthy subjects. Despite similar level of task performance,
cannabis users showed greater activation during inhibitory trials in the right dorsolateral
prefrontal, bilateral medial frontal, bilateral inferior and superior parietal lobules and right
occipital gyrus compared to the healthy subjects. During the non-inhibitory trials,
differences were located in right prefrontal, insular and parietal cortices, with cannabis
users showing greater activation in these areas compared to the controls. Similar
findings have also been observed in adults (Hester et al., 2009), and suggest a greater
neurocognitive effort during the task in cannabis users, even after the abstinence period.
In this regard, the brain seems able to achieve some degree of reorganization
during chronic THC exposure by activating brain regions not usually needed to perform
the cognitive task in response to an impaired ability of the normally engaged task
network. Thus, it is possible that despite control-level performance during tasks other
than DMS in Chapter 3, THC-induced alterations in the primary regions controlling
performance on these tasks were present but were compensated for by the recruitment
of additional regions. However, it is not clear how long such compensatory mechanisms
can maintain normal functioning and whether impairments over time may become
apparent. The impact of these subtle brain alterations on treatment outcome remains to
be determined.
Age of onset
It is also important to acknowledge that the cognition-related studies in Chapter 3
174
were conducted in adult monkeys. Cannabis use, however, is often initiated in
adolescence, and recent data show that a decline in perceived risk of marijuana has
been accompanied by a simultaneous increase in rates of use among adolescents
(Johnston et al., 2014). Adolescence is a critical period of neurodevelopment, especially
for frontal regions (Giedd et al., 1999; Gogtay et al., 2004), with synaptic pruning and
refinement leading to the development of complex cognitive domains such as working
memory, executive functions, decision making and emotional/motivational processes
(Yurglun-Todd, 2007). Thus, chronic cannabis use during this time may disrupt normal
neuromaturation (Bava and Tapert, 2010), and in turn, lead to long-lasting impairments
in neurocognitive functioning more so than if use was initiated as an adult.
Indeed, studies that have examined the relationship between age of onset of
regular cannabis use and cognitive function have reported long-lasting alterations on a
range of tasks. Ehrenreich and colleagues (1999) reported that early onset cannabis use
(prior to age 16) predicted significantly longer reaction times on a task of visual scanning
and attention, while those who began use at age 16 or later performed similarly to
controls. Pope and colleagues (2003) analyzed cognitive data from long-term heavy
cannabis users following a 28-day period of abstinence, and compared early onset users
(prior to age 17) with late onset users (use at age 17 or later). After adjusting for age,
gender, ethnicity and family background, late onset cannabis users were no different
from control subjects; however, early onset users demonstrated reduced performance
relative to control subjects on measures of verbal learning, fluency and overall verbal
intelligence quotient. Consistent with these data with human subjects, repeated THC
administration in adolescent rats was shown to produce more profound effects on
working memory using a novel object test than in adult rats (Cha et al., 2006; Quinn et
al., 2008).
Given these data, it is possible that if the same study as in Chapter 3 were
175
conducted in adolescent monkeys, dramatically different results would have been
generated. In fact, the largest reduction in working memory performance during chronic
THC treatment in Chapter 3 was approximately 25% of baseline performance. Although
this was a significant decrease that persisted for several weeks, a 25% reduction in
baseline performance does not appear all that substantial. This effect also occurred at
the long delay, which was individualized to produce < 60% accuracy (chance accuracy
was 25-33% depending on the number of distractors), therefore, it is possible that a floor
effect prevented any further decreases in performance. Nevertheless, it would be
hypothesized that persistent THC-induced cognitive impairment would be even greater
in adolescent monkeys (e.g., impairments on other tasks and/or further decrease in DMS
percent accuracy at the long delay as well as at other delays) than what was seen in
adult monkeys in the present study. These results would support current evidence
showing the marijuana-induced cognitive impairments are generally more persistent
when use was initiated during adolescent compared to adulthood (Chang et al. 2006;
Jager et al. 2006; Fisk and Montgomery 2008; Becker et al. 2010; Grant et al. 2011;
Gruber et al. 2012;). Future studies should continue to characterize the specific domains
of cognitive function that are impaired from chronic THC exposure in adolescents and
the time course of effects. The persistence of cognitive impairments in adolescent
cannabis users supports the need to address these impairments early in treatment and
continually during abstinence.
Relationships between behavioral endpoints
One advantage of this dissertation was that the studies across both chapters
were conducted within-subject, that is, the majority of the same monkeys used for the
experiments in Chapter 2 were also used for the ones in Chapter 3. Thus, it was
possible to compare the potency and efficacy of THC across various behavioral
176
endpoints (i.e., food-maintained responding, hypothermia, self-administration, and
cognitive performance) in order to provide some insight on the behavioral mechanisms
of THC. Although differences in THC pretreatment times and the inability to calculate an
ED50 for some effects made potency comparisons across tasks difficult, examination of
the smallest dose of THC that produced a significant deficit (or the highest dose that had
no effect) offers some indication of relative potency. In this regard, the rank order of
THC’s potency in the behavioral measures within this dissertation was: working memory
< FR10 food-maintained responding = extradimensional set shifting < compound
discrimination < simple discrimination = reversal learning = intradimensional set-shifting
= hypothermia.
The fact that THC was more potent to decrease rates of responding under the
FR 10 schedule of food presentation than to decrease body temperature is consistent
with the pattern of effects seen in other studies with rodents (e.g. De Vry et al., 2004;
Singh et al., 2011). It should be noted that for some subjects, no hypothermic effects
were seen up to doses that abolished all operant behavior therefore higher doses were
not tested because that was not the primary endpoint. However, it is likely that
hypothermic effects would have become apparent if higher doses were tested, based on
previous studies (McMahon, 2005; Taffe, 2012). Thus, the difference in potency for
producing the two effects might reflect differential binding to multiple receptor subtypes
that differentially mediate the two effects. However, the CB1 receptor antagonist
rimonabant antagonized the rate-decreasing and hypothermic effects of THC (Giuffrida
and McMahon 2010; Singh et al., 2011), albeit antagonism of rate-decreasing effects
was less orderly and of a lesser magnitude than antagonism of hypothermia. Less
orderly antagonism of rate-decreasing effects could reflect actions of rimonabant at a
subset of brain cannabinoid receptors that are more prominently involved in rate-
177
decreasing effects than hypothermic effects; however, mechanisms underlying the
effects of rimonabant are not known.
Interestingly, a study in rhesus monkeys reported the opposite relationship than
observed in this dissertation regarding the potency of THC to alter these behavioral
effects, i.e., THC was more potent to decrease body temperature than to decrease food-
maintained responding (McMahon, 2005). In that study, however, an FR10, FR10
multiple schedule of food presentation and stimulus-shock termination was used to study
the effects of THC on operant behavior. Therefore, additional variables including
schedule of reinforcement, environmental context and the maintaining event should also
be considered with regard to the potency of THC to produce rate-decreasing effects on
operant behavior. Moreover, rate dependency has also been shown to be a determinant
of THC’s potency to affect operant behavior (McMillan, 1977; Brady and Balster, 1980).
Supporting this notion, the overall rate of responding in the rhesus monkeys used for this
dissertation under the FR10 schedule of food presentation was negatively related to the
potency of THC to produce rate-decreasing effects under that schedule (Figure 3).
178
The higher potency of THC to disrupt working memory performance compared to
hypothermia and other unconditioned behaviors is consistent with at least one other
study in rodents that included these endpoints (Varvel et al., 2001). As discussed above,
if both THC-induced deficits to cognition and the other unconditioned behaviors are
mediated by CB1 receptors, then the differences in potency might be due to a difference
in the magnitude of CB1 receptor stimulation (i.e., the efficacy requirement) required for
the effects (e.g., hypothermia > working memory). This would imply that the receptor
Figure 3. Rate-dependent effects of THC on food-
maintained responding (r = -0.85, p < 0.05). Abscissa: ED50 (mg/kg) of THC to decrease food-maintained responding under an FR10 schedule of food presentation. Ordinate: Mean overall rate of responding (responses/second) under an FR10 schedule of food presentation. Unpublished data from John, Martin, and Nader, 2016.
0.001 0.01 0.1 1
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ED50 (mg/kg)
Overa
ll R
esp
on
se R
ate
(resp
on
ses/s
ec)
179
reserve level is higher for the CB1 receptor population in the brain mediating the effects
of cannabinoids on working memory than for the CB1 receptor population mediating the
hypothermic, rate-decreasing effects, and other cognitive domains assessed in this
study. Collectively, these data indicate that the potency of THC is determined by multiple
factors potentially involving pharmacological and behavioral mechanisms. In particular,
these potency relationships across various behavioral endpoints have implications for
establishing legal limits for THC in the blood when driving; however, it remains to be
determined which behavioral endpoints are most predictive of driving performance. For
instance, if the effects of THC on working memory performance are most predictive of its
effects on driving, then a relatively lower THC blood limit might be the most suitable. On
the other hand, if compound discrimination and reversal learning are more predictive,
lower blood levels could result in false positives with regard to driving impairment.
Moreover, as discussed previously, the potency of THC to impair behavioral
performance differs as function of drug history, which should be considered along with
differential levels of tolerance between behavioral endpoints.
Conclusions
Overall, the research conducted in this dissertation provided insight on the
behavioral mechanisms underlying the reinforcing, unconditioned, and cognitive effects
of THC. These data indicate that the behavioral effects of THC cannot be predicted
solely on its pharmacological mechanism of action. Instead, a complex set of
environmental variables must be considered including behavioral sensitivity, drug
history, and the contingencies governing behavior. The adoption of a more
comprehensive perspective that includes the interaction of environmental variables with
neuropharmacology will be vital for the development of the most effective approaches to
treat substance abuse problems.
180
REFERENCES
Agrawal A, Wetherill L, Dick DM, Xuei X, Hinrichs A, Hesselbrock V, Kramer J,
Nurnberger Jr. JI, Schuckit M, Bierut LJ, Edenberg HJ, Foroud T (2009)
Evidence for association between polymorphisms in the cannabinoid receptor 1
(CNR1) gene and cannabis dependence. Am. J. Med. Genet. B Neuropsychiatr.
Genet. 150B: 736-740.
Aharonovich E, Nunes EV, Hasin D (2003) Cognitive impairment, retention and
abstinence among cocaine abusers in cognitive- behavioral treatment. Drug
Alcohol Depend. 71: 207–211.
Aharonovich E, Hasin DS, Brooks AC, Liu X, Bisaga A, Nunes EV (2006) Cognitive
deficits predict low treatment retention in cocaine dependent patients. Drug
Alcohol Depend 81: 313-322.
Aharonovich E, Brooks AC, Nunes EV, et al. (2008) Cognitive deficits in marijuana
users: effects on motivational enhancement therapy plus cognitive behavioral
therapy treatment outcome. Drug Alcohol Depend. 95:279–83.
Arria AM, Garnier-Dykstra LM, Caldeira KM, Vincent KB, Winick AR, O'Grady KE
(2013). Drug use patterns and continuous enrollment in college: Results from a
longitudinal study. Journal of Studies on Alcohol and Drugs, 74, 71–83.
Aryana A, Williams MA (2007). Marijuana as a trigger of cardiovascular events:
Speculation or scientific certainty? International Journal of Cardiology, 118: 141–
144.
Ator NA (2002) Relation between discriminative and reinforcing effects of midazolam,
pentobarbital, chlordiazepoxide, zolpidem, and imidazenil in baboons.
Psychopharmacology 163: 477–487.
Ator NA and Griffiths RR (1983) Nicotine self-administration in baboons. Pharmacol
181
Biochem Behav 19: 993-1003.
Ator NA, Griffiths RR (2003) Principles of drug abuse liability assessment in laboratory
animals. Drug and Alcohol Dependence. 70: S55–S72.
Balster BL and Schuster CR (1973) Fixed-interval schedule of cocaine reinforcement:
effect of dose and infusion duration. Journal of the Experimental Analysis of
Behavior, 20: 119-129.
Batalla A, Bhattacharyya S, Yücel M, Fusar-Poli P, Crippa JA, Nogué S, Torrens M,
Pujol J, Farré M, Martin-Santos R (2013) Structural and functional imaging
studies in chronic cannabis users: a systematic review of adolescent and adult
findings. PLoS One. 8: e55821.
Bava S, Tapert SF (2010) Adolescent brain development and the risk for alcohol and
other drug problems. Neuropsychol Rev. 20: 398-413.
Beardsley PM, Balster RL (1993) The effects of delay of reinforcement and dose on the
self-administration of cocaine and procaine in rhesus monkeys. Drug Alcohol
Depend. 34: 37-43.
Becker B, Wagner D, Gouzoulis-Mayfrank E, Spuentrup E, Daumann J (2010). The
impact of early-onset cannabis use on functional brain correlates of working
memory. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 34:
837–845.
Bergamaschi M, Queiroz R, Chagas M, Oliveira D, Martinis B, Kapczinski F et al (2011).
Cannabidiol reduces the anxiety induced by simulated public speaking in
treatment-naïve social phobia patients. Neuropsychopharmacology 36: 1219–
1226.
Bhattacharyya S, Morrison PD, Fusar-Poli P, Martin-Santos R, Borgwardt S, Winton-
Brown T et al (2010). Opposite effects of Δ-9-tetrahydrocannabinol and
cannabidiol on human brain function and psychopathology.
182
Neuropsychopharmacology 35: 764–774.
Bickel W, Yi R, Landes R, et al. (2011) Remember the future: working memory training
decreases delay discounting among stimulant addicts. Biol Psychiatry. 69: 260–5.
Bolla KI, Brown K, Eldreth D, Tate K, Cadet JL (2002): Dose-related neurocognitive
effects of marijuana use. Neurology. 59: 1337-1343.
Borgen LA, Davis WM (1974) Cannabidiol interaction with delta-9-tetrahydrocannabinol.
Res. Commun. Chem. Pathol. Pharmacol. 7: 663–670.
Brady KT, Balster RL (1980) The effects of delta 9-tetrahydrocannabinol alone and in
combination with cannabidiol on fixed-interval performance in rhesus monkeys.
Psychopharmacology (Berl). 72: 21-6.
Branch MN, Dearing ME, and Lee DM (1980) Acute and chronic effects of Δ9-
tetrahydrocannabinol on complex behavior of squirrel monkeys.
Psychopharmacology (Berl) 71: 247–256.
Brook JS, Kessler RC, and Cohen P (1999). The onset of marijuana use from
preadolescence and early adolescence to young adulthood. Development and
Psychopathology, 11: 901–914.
Brook JS, Zhang C, and Brook DW (2011). Developmental trajectories of marijuana use
from adolescence to adulthood: Personal predictors. Archives of Pediatrics &
Adolescent Medicine. 165: 55–60.
Browning PG, Gaffan D (2008) Impairment in object-in-place scene learning after
uncinate fascicle section in macaque monkeys. Behav Neurosci. 122: 477-82.
Carbuto M, Sewell RA, Williams A, Forselius-Bielen K, Braley G, Elander J, Pittman B,
Schnakenberg A, Bhakta S, Perry E, Ranganathan M, D'Souza DC; Yale THC
Study Group (2012) The safety of studies with intravenous Δ⁹-
tetrahydrocannabinol in humans, with case histories. Psychopharmacology
183
(Berl). 219: 885-896.
Carroll KM, Kiluk BD, Nich C, Babuscio TA, Brewer JA, Potenza MN, et al. (2011)
Cognitive function and treatment response in a randomized clinical trial of
computer-based training in cognitive-behavioral therapy. Substance use and
misuse 46: 23–34.
Carter LP, Griffiths RR (2009) Principles of laboratory assessment of drug abuse liability
and implications for clinical development. Drug and Alcohol Dependence.
105(supplement 1): S14–S25.
Cha YM, White AM, Kuhn CM, Wilson WA, Swartzwelder HS (2006). Differential effects
of delta9-THC on learning in adolescent and adult rats. Pharmacol Biochem
Behav 83: 448–455.
Chait LD (1989) Delta-9-tetrahydrocannabinol content and human marijuana self-
administration. Psychopharmacology (Berl). 98: 51–55.
Chait LD, Burke KA (1994) Preference for high- versus low-potency marijuana.
Pharmacol Biochem Behav. 49: 643–647.
Chait LD, Zacny JP (1992) Reinforcing and subjective effects of oral delta 9-THC and
smoked marijuana in humans. Psychopharmacology (Berl) 107: 255 – 62.
Chambers CD, Garavan H, Bellgrove MA 2009. Insights into the neural basis of
response inhibition from cognitive and clinical neuroscience. Neurosci. Bio-
behav. Rev. 33: 631-646.
Chang L, Yakupov R, Cloak C, Ernst T (2006) Marijuana use is associated with a
reorganized visual-attention network and cerebellar hypoactivation. Brain 129:
1096–1112.
Chudasama Y (2011) Animal models of prefrontal-executive function. Behav Neurosci
125: 327–343.
184
Crane NA, Schuster RM, Fusar-Poli P, et al. (2013) Effects of cannabis on
neurocognitive functioning: recent advances, neurodevelopmental influences,
and sex differences. Neuropsychol Rev. 23: 117–137.
Crean, RD., Crane, NA, and Mason, BJ (2011) An evidence based review of acute and
long-term effects of cannabis Use on executive cognitive functions. Journal of
Addiction Medicine. 5: 1–8.
Crippa J, Derenusson GN, Ferrari TB, Wichert-Ana L, Dura F, Marti N-Santos RO et al
(2011) Neural basis of anxiolytic effects of cannabidiol (CBD) in generalized
social anxiety disorder-a preliminary report. J Psychopharmacol 25: 121–130.
De Vry J, Jentzsch KR, Kuhl E, Eckel G (2004). Behavioral effects of cannabinoids show
differential sensitivity to cannabinoid receptor blockade and tolerance
development. Behav Pharmacol 15: 1–12.
Deneau GA and lnoki R(1967) Nicotine self-administration in monkeys. Ann NY Acad Sci
142: 227-279.
D'Souza DC, Perry E, MacDougall L, Ammerman Y, Cooper T, Wu YT, Braley G,
Gueorguieva R, Krystal JH (2004) The psychotomimetic effects of intravenous
delta-9-tetrahydrocannabinol in healthy individuals: implications for psychosis.
Neuropsychopharmacology. 29:1558-1572.
Ehrenreich H, Rinn T, Kunert HJ, Moeller MR, Poser W, Schilling L, et al. (1999).
Specific attentional dysfunction in adults following early start of cannabis use.
Psychopharmacology, 142: 295–301.
Eichenbaum H, Otto T, and Cohen NJ (1992) The hippocampus—what does it do?
Behav Neural Biol 57: 2–36.
ElSohly M, Wachtel S, Wit H (2003). Cannabis versus THC: response to Russo and
McPartland. Psychopharmacology 165: 433–434.
Elsohly MA, Slade D (2005) Chemical constituents of marijuana: the complex mixture of
185
natural cannabinoids. Life Sci. 78: 539-48.
Falk JL (1961) Production of polydipsia in normal rats by an intermittent food schedule.
Science 133: 195-196.
Falk JL (1964) Studies on schedule-induced polydipsia. In: Thirst. Proceedings of the
First International Symposium on Thirst in the Regulation of Body Water, edited
by M. J. Wayner. Oxford, England: Pergamon Press.
Falk JL (1966) Schedule-induced polydipsia as a function of fixed interval length. J Exp
Anal Behav. 9: 37–38.
Falk, JL (1969). Conditions producing psychogenic polydipsia in animals. Ann. N. Y.
Acad. Sci. 157: 569-593.
Falk JL, Samson HH, Tang M (1972) Chronic ingestion techniques for the production of
physical dependence on ethanol, in Alcohol Intoxication and Withdrawal
(GrossMM ed) 197–211. Plenum, New York.
Fisk JE, and Montgomery C (2008). Real-world memory and executive processes in
cannabis users and non-users. Journal of Psychopharmacology, 22: 727–736.
Ford MM (2014) Applications of schedule-induced polydipsia in rodents for the study of
an excessive ethanol intake phenotype. Alcohol. 48: 265-76.
Freund TF, Katona I, and Piomelli D (2003) Role of endogenous cannabinoids in
synaptic signaling. Physiol Rev 83: 1017–1066.
Ghozland S, Mathews H, Simonin F, Filliol D, Kieffer BL, Maldonado R (2002)
Motivational effects of cannabinoids are mediated by μ- and k-opioid receptors. J
Neurosci. 22:1146–1154.
Goldberg SR, Spealman RDand Goldberg DM (1981) Persistent behavior at high rates
maintained by intravenous self-administration of nicotine. Science. 214: 573-575.
Goldberg SR and Spealman RD (1982) Maintenance and suppression of behavior by
intravenous nicotine injections in squirrel monkeys. Fed Proc 41: 216-220.
186
Goldberg SR and Henningfield JE (1988) Reinforcing effects of nicotine in humans and
experimental animals responding under intermittent schedules of i.v. drug
injection. Pharmacol Biochem Behav.1: 227-234.
Grant KA and Johanson CE (1988) The nature of the scheduled reinforcer and
adjunctive drinking in nondeprived rhesus monkeys. Pharmacol Biochem Behav
29: 295–301.
Grant JD, Scherrer, JF, Lyons MJ, Tsuang M, True WR, Bucholz KK (2005) Subjective
reactions to cocaine and marijuana are associated with abuse and dependence.
Addict. Behav., 30: 1574–1586
Grant KA, Leng X, Green HL, Szeliga KT, Rogers LS, Gonzales SW (2008) Drinking
typography established by scheduled induction predicts chronic heavy drinking in
a monkey model of ethanol self-administration. Alcohol Clin Exp 32: 1824-1838.
Grant JE, Chamberlain SR, Schreiber L, and Odlaug BL (2011). Neuropsychological
deficits associated with cannabis use in young adults. Drug and Alcohol
Dependence, 121: 159–162.
Grech, A, Van Os J, Jones PB, Lewis SW, and Murray RM (2005) Cannabis use and
outcome of recent onset psychosis. European Psychiatry, 20: 349–353.
Grotenhermen F, Leson G, Berghaus G, Drummer OH, Krüger HP, Longo M, Moskowitz
H, Perrine B, Ramaekers JG, Smiley A, Tunbridge R, (2007) Developing limits for
driving under cannabis. Addiction 102: 1910–1917.
Gruber SA, Sagar KA, Dahlgren MK, Racine M, and Lukas SE (2012) Age of onset of
marijuana use and executive function. Psychology of Addictive Behaviors, 26:
496–506.
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., et
al. (1999) Brain development during childhood and adolescence: a longitudinal
MRI study. Nature Neuroscience, 2: 861–863.
187
Giuffrida A, McMahon LR (2010) In vivo pharmacology of endocannabinoids and their
metabolic inhibitors: therapeutic implications in Parkinson’s disease and abuse
liability. Prostaglandins Other Lipid Mediat 91: 90–103.
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., et al.
(2004) Dynamic mapping of human cortical development during childhood
through early adulthood. Proceed- ings of the National Academy of Sciences of
the United States of America 101: 8174–8179.
Gorriti MA, Ferrer B, del Arco I, Bermúdez-Silva FJ, de Diego Y, Fernandez-Espejo E,
Navarro M, Rodríguez de Fonseca F (2005) Acute delta9-tetrahydrocannabinol
exposure facilitates quinpirole-induced hyperlocomotion. Pharmacol Biochem
Behav 81:71-77.
Hall, W. (2009) The adverse health effects of cannabis use: What are they, and what are
their implications for policy? The International Journal on Drug Policy 20: 458–
466.
Hampson RE, España RA, Rogers GA, Porrino LJ, Deadwyler SA (2009) Mechanisms
underlying cognitive enhancement and reversal of cognitive deficits in nonhuman
primates by the ampakine CX717. Psychopharmacology (Berl) 202: 355-69.
Haney M, Comer SD, Ward AS, Foltin RW, Fischman MW (1997) Factors influencing
marijuana self-administration by humans. Behav Pharmacol. 8: 101–112.
Hanson, H. M,, C. A. Ivester and B. R. Morton (1979) Nicotine self-administration in rats.
In: Cigarette Smoking as a Dependence Process, edited by N. A. Krasnegor.
National Institute on Drug Abuse Research Monograph Series No. 23. U.S.
Department of Health, Education and Welfare, Washington, DC: U.S.
Government Printing Office. 70-90.
Hardwick S, King LA (2008) Home Office Cannabis Potency Study. Home Office
188
Scientific Development Branch: St Albans. Report.
Hart CL, Haney M, Vosburg SK, Comer SD, Foltin RW (2005) Reinforcing effects of oral
Delta9-THC in male marijuana smokers in a laboratory choice procedure.
Psychopharmacology (Berl). 181: 237-243.
Hartman RL, Huestis MA( 2013) Cannabis effects on driving skills. Clin. Chem. 59: 478–
492.
Hashibe M, Straif K, Tashkin DP, Morgenstern H, Greenland S, and Zhang ZF (2005)
Epidemiologic review of marijuana use and cancer risk. Alcohol, 35: 265–275.
Hasin DS, Saha TD, Kerridge BT, Goldstein RB, Chou SP, Zhang H et al (2015)
Prevalence of marijuana use disorders in the United States between 2001–2002
and 2012–2013. JAMA Psychiatry 72: 1–9
Hello NK (1973) A review of methods to induce alcohol addiction in animals. Pharmacol
Biochem Behav. 1: 89-101.
Hester R, Garavan H (2004) Executive dysfunction in cocaine addiction: evidence for
discordant frontal, cingulate, and cerebellar activity. J. Neurosci. 24: 11017-
11022.
Hester R, Nestor L, Garavan H (2009) Impaired error awareness and anterior cingulate
cortex hypoactivity in chronic cannabis users. Neuropsychopharmacology 34:
2450–2458.
Hill KP (2015) Medical Marijuana for Treatment of Chronic Pain and Other Medical and
Psychiatric Problems: A Clinical Review. JAMA. 313: 2474-2483.
Hingson R, Heeren T, Winter M, and Wechsler H (2005). Magnitude of alcohol-related
mortality and morbidity among U.S. college students ages 18–24: Changes from
1998 to 2001. Annual Review of Public Health, 26: 259–279.
Hirvonen J, Goodwin RS, Li CT, Terry GE, Zoghbi SS, Morse C, Pike VW, Volkow ND,
189
Huestis MA, and Innis RB (2012) Reversible and regionally selective
downregulation of brain cannabinoid CB1 receptors in chronic daily cannabis
smokers. Mol Psychiatry 17: 642–649.
Hoffmeister F (1988) A comparison of the stimulus effects of codeine in rhesus monkeys
under the contingencies of a two lever discrimination task and a cross self-
administration paradigm: tests of generalization to pentazocine, buprenorphine,
tilidine, and different doses of codeine. Psychopharmacology 94: 315–320.
Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S (1981) Do
plasma concentrations of delta 9-tetrahydrocannabinol reflect the degree of
intoxication? J Clin Pharmacol. 21: 171S-177S.
Hollister LE (1986) Health aspects of cannabis. Pharmacol Rev 38: 1–20
Holtzman SG (1985) Drug discrimination studies. Drug Alcohol Depend 14: 263–282.
Hopfer CJ, Young SE, Purcell S, Crowley TJ, Stallings, MC, Corley RP, Rhee SH,
Smolen A, Krauter K, Hewitt JK, Ehringer MA, (2006) Cannabis receptor
haplotype associated with fewer cannabis dependence symptoms in
adolescents. Am. J. Med. Genet. B Neuropsychiatr. Genet. 141B: 895-901.
Houben K, Wiers RW, Jansen A (2011) Getting a grip on drinking behavior: training
working memory to reduce alcohol abuse. Psychol Sci. 22: 968–975.
Hurd YL, Michaelides M, Miller ML, Jutras-Aswad D (2014) Trajectory of adolescent
cannabis use on addiction vulnerability. Neuropharmacology. 76 Pt B: 416-424.
Jager G, Kahn RS, Van Den Brink W, Van Ree JM, and Ramsey NF (2006). Long-term
effects of frequent cannabis use on working memory and attention: an fMRI
study. Psychopharmacology. 185: 358–368.
Johnson LA, Johnson RL, Portier RB (2013) Current “legal highs.” J Emerg Med 44:
1108–1115.
190
Johnston LD, et al (2014). Monitoring the Future: National Survey Results on Drug Use,
1975 2014-Overview, Key Findings on Adolescent Drug Use. (Institute for Social
Research, University of Michigan, Ann Arbor) Available at
http://monitoringthefuture.org//pubs/monographs/mtf-vol1_2014.pdf.
Kangas BD, Leonard MZ, Shukla VG, Alapafuja SO, Nikas SP, Makriyannis A, Bergman
J (2016) Comparisons of Δ9-Tetrahydrocannabinol and Anandamide on a Battery
of Cognition-Related Behavior in Nonhuman Primates. J Pharmacol Exp Ther.
357: 125-133.
Karniol IG, Carlini EA (1973) Pharmacological interaction between cannabidiol and delta
9-tetrahydrocannabinol. Psychopharmacologia. 33: 53-70.
Karniol IG, Shirakawa I, Kasinski N, Pfeferman A, Carlini EA, (1974) Cannabidiol
interferes with the effects of delta 9
-tetrahydrocannabinol in man. Eur. J.
Pharmacol. 28: 172–177.
Kato S, Wakasa Y, Yanagita T (1987) Relationship between minimum reinforcing doses
and injection speed in cocaine and pentobarbital self-administration in crab-
eating monkeys. Pharmacol Biochem Behav. 28: 407-410.
Kelleher RT and Morse WH (1968) Determinants of the specificity of the behavioral
effects of drugs. Ergeb Physiol Biol Chem Exp Pharmakol. 60: 1-56.
Kelly TH, Foltin RW, Emurian CS, Fischman MW (1994a) Effects of delta 9-THC on
marijuana smoking, dose choice, and verbal report of drug liking. J Exp Anal
Behav. 61: 203–211.
Kelly TH, Foltin RW, Mayr MT, Fischman MW (1994b) Effects of delta 9-
tetrahydrocannabinol and social context on marijuana self-administration by
humans. Pharmacol Biochem Behav. 49: 763–768.
Kelly TH, Foltin RW, Emurian CS, Fischman MW (1997) Are choice and self-
administration of marijuana related to delta 9-THC content? Exp Clin
191
Psychopharmacol. 5: 74–82.
Klingberg T (2010) Training and plasticity of working memory. Trends Cogn Sci. 14:
317–324.
Lang, WJ, Latiff AA, McQueen A, and Singer G (1977) Self-administration of nicotine
with and without a food delivery schedule. Pharmacol Biochem Behav. 7: 65-
70.
Lazenka MF, Selley DE, and Sim-Selley LJ (2013) Brain regional differences in CB1
receptor adaptation and regulation of transcription. Life Sci 92: 446–452.
Lepore M, Vorel SR, Lowinson J, Gardner EL (1995) Conditioned place preference
induced by Δ9-tetrahydrocannabinol: comparison with cocaine, morphine, and
food reward. Life Sci 56: 2073–2080.
Leweke FM, Piomelli D, Pahlisch F, Muhl D, Gerth CW, Hoyer C et al (2012).
Cannabidiol enhances anandamide signaling and alleviates psychotic symptoms
of schizophrenia. Transl Psychiatry 2: E94.
Lyons MJ, Toomey R, Meyer JM, Green AI, Eisen SA, Goldberg J, True WR, and
Tsuang MT (1997). How do genes influence marijuana use? The role of
subjective effects. Addiction, 92: 409–417
Lynskey M, and Hall W (2000). The effects of adolescent cannabis use on educational
attainment: A review. Addiction, 95: 1621–1630.
Mansbach RS, Nicholson KL, Martin BR, Balster RL (1994) Failure of Δ9-
tetrahydrocannabinol and CP 55,940 to maintain intravenous self-administration
under a fixed-interval schedule in rhesus monkeys. Behav Pharmacol 5: 219–
225.
Manwell LA, Charchoglyan A, Brewer D, Matthews BA, Heipel H, Mallet PE (2014) A
vapourized Δ(9)-tetrahydrocannabinol (Δ(9)-THC) delivery system part I:
192
development and validation of a pulmonary cannabinoid route of exposure for
experimental pharmacology studies in rodents. J Pharmacol Toxicol Methods.
70:120-127.
Martelle JL, Nader MA (2009) A within-subject assessment of the discriminative stimulus
and reinforcing effects of self-administered cocaine in rhesus monkeys.
Psychopharmacology (Berl). 203: 343-353.
Martelle SE, Nader SH, Czoty PW, John WS, Duke AN, Garg PK, Garg S, Newman AH,
Nader MA (2014) Further characterization of quinpirole-elicited yawning as a
model of dopamine D3 receptor activation in male and female monkeys. J
Pharmacol Exp Ther. 350: 205-211.
McMahon LR, Amin MR, France CP (2005) SR 141716A differentially attenuates the
behavioral effects of delta9-THC in rhesus monkeys. Behav Pharmacol. 16: 363-
372.
McMahon LR (2006) Characterization of cannabinoid agonists and apparent pA2
analysis of cannabinoid antagonists in rhesus monkeys discriminating Delta9-
tetrahydrocannabinol. J Pharmacol Exp Ther. 319: 1211-1218.
McMahon LR (2011) Chronic Δ⁹-tetrahydrocannabinol treatment in rhesus monkeys:
differential tolerance and cross-tolerance among cannabinoids Br J Pharmacol.
162: 1060-1073.
McMahon LR (2016) Enhanced discriminative stimulus effects of Δ(9)-THC in the
presence of cannabidiol and 8-OH-DPAT in rhesus monkeys. Drug Alcohol
Depend. 165: 87-93.
McMillan DE (1977) Behavioral pharmacology of the tetrahydro- cannabinols. In:
Thompson T, Dews PB (eds) Advances in behavioral pharmacology, vol I.
Academic Press, New York, p 1.
Mello NK. A review of methods to induce alcohol addiction in animals (1973) Pharmacol
193
Biochem Behav. 1: 89-101.
Mello NK and Negus SS (1996) Preclinical evaluation of pharmacotherapies for
treatment of cocaine and opioid abuse using drug self-administration procedures.
Neuropsychopharmacology.14: 375-424.
Mendelson JH, Mello NK (1984) Reinforcing properties of oral delta 9-
tetrahydrocannabinol, smoked marijuana, and nabilone: influence of previous
marijuana use. Psychopharmacology (Berl) 83: 351–356.
Meisch RA (1977) Ethanol self-administration, infrahuman studies. Advances in
Behavioral Pharmacology. 1: 36–71.
Mittleman MA, Lewis RA, Maclure M, Sherwood JB, and Muller JE (2001) Triggering
myocardial infarction by marijuana. Circulation. 103: 2805–2809.
Moreno M, Lopez-Moreno JA, Rodríguez de Fonseca F, Navarro M (2005)
Behavioural effects of quinpirole following withdrawal of chronic treatment with
the CB1
agonist, HU-210, in rats. Behav Pharmacol. 16: 441-446.
Moskowitz H, Fiorentino D (2000) A Review of the Literature on the Effects of Low
Doses of Alcohol on Driving-Related Skills. National Highway Traffic Safety
Administration, Washington, DC.
Mumford GK, Holtzman SG (1991) Qualitative differences in the discriminative stimulus
effects of low and high doses of caffeine in the rat. J Pharmacol Exp Ther 258:
857–865.
Naef M, Russmann S, Petersen-Felix S, Brenneisen R (2004) Development and
pharmacokinetic characterization of pulmonal and intravenous delta-9-
tetrahydrocannabinol (THC) in humans. J Pharm Sci. 93: 1176-1184.
Nava F, Carta G, Gessa GL (2000) Permissive role of dopamine D(2) receptors in the
hypothermia induced by delta(9)-tetrahydrocannabinol in rats. Pharmacol
194
Biochem Behav. 66: 183-187.
Nelson ME, Bryant SM, Aks SE (2014) Emerging drugs of abuse. Emerg Med Clin North
Am 32: 1–28.
Nguyen JD, Aarde SM, Vandewater SA, Grant Y, Stouffer DG, Parsons LH, Cole M,
Taffe MA (2016) Inhaled delivery of Δ(9)-tetrahydrocannabinol (THC) to rats by
e-cigarette vapor technology. Neuropharmacology. 109: 112-20.
Niesink RJ, van Laar MW (2013). Does cannabidiol protect against adverse
psychological effects of THC? Front Psychiatry 4: 130.
Nordstrom BR, Levin FR (2007) Treatment of cannabis use disorders: A review of the
literature. Am J Addict. 16: 331–342.
Ohlsson A, Lindgren JE, Wahlen A, Agurell S, Hollister LE, Gillespie HK. Plasma delta-9
tetrahydrocannabinol concentrations and clinical effects after oral and
intravenous administration and smoking. Clin Pharmacol Ther 1980;28: 409–416.
Patterson F, Jepson C, Strasser AA, Loughead J, Perkins KA, Gur RC, Frey JM, Siegel
S, Lerman C, (2009) Varenicline improves mood and cognition during smoking
abstinence. Biol. Psychiatry 65: 144-149.
Patterson F, Jepson C, Loughead J, Perkins K, Strasser AA, Siegel S, Frey J, Gur R,
Lerman C, (2010) Working memory deficits predict short-term smoking
resumption following brief abstinence. Drug Alcohol Depend. 106: 61-64.
Polen MR, Sidney S, Tekawa IS, Sadler M, and Friedman GD (1993). Health care use
by frequent marijuana smokers who do not smoke tobacco. The Western Journal
of Medicine, 158: 596–601.
Pope HG Jr, Gruber AJ, Yurgelun-Todd D (1995) The residual neuropsychological
effects of cannabis: The current status of research. Drug Alcohol Depend. 38:
25–34.
195
Pope HG, Jr, and Yurgelun-Todd D (1996). The residual cognitive effects of heavy
marijuana use in college students. Journal of the American Medical Association,
275: 521–527.
Pope HG, Gruber Jr AJ, Hudson JI, Cohane G, Huestis MA, and Yurgelun-Todd, D.
(2003) Early-onset cannabis use and cognitive deficits: what is the nature of the
association? Drug and Alcohol Dependence, 69: 303–310.
Potter DJ, Peter C, Brown MB (2008). Potency of D9–THC and other cannabinoids in
cannabis in England in 2005: implications for psychoactivity and pharmacology. J
Forensic Sci 53: 90–94.
Prescott WR, Gold LH, Martin BR (1992) Evidence for separate neuronal mechanisms
for the discriminative stimulus and catalepsy induced by delta 9-THC in the rat.
Psychopharmacology (Berl). 107:117-124.
Quinn HR, Matsumoto I, Callaghan PD, Long LE, Arnold JC, Gunasekaran N et al.
(2008). Adolescent rats find repeated Delta(9)-THC less aversive than adult rats
but display greater residual cognitive deficits and changes in hippocampal protein
expression following exposure. Neuropsychopharmacology 33: 1113–1126.
Ramaekers JG, Kauert G, Theunissen, EL, Toennes SW, Moeller MR (2009)
Neurocognitive performance during acute THC intoxication in heavy and
occasional cannabis users. J. Psychopharmacol. Oxford 23: 266–277.
Rezapour T, DeVito EE, Sofuoglu M, Ekhtiari H (2016) Perspectives on neurocognitive
rehabilitation as an adjunct treatment for addictive disorders: From cognitive
improvement to relapse prevention. Prog Brain Res 224: 345-369.
Rosenbaum CD, Carreiro SP, Babu KM (2012) Here today, gone tomorrow, and back
again? A review of herbal marijuana alternatives (K2, Spice), synthetic
cathinones (bath salts), kratom, Salvia divinorum, methoxetamine, and
196
piperazines. J Med Toxicol 8: 15–32.
Samson HH, Falk JL (1975) Pattern of daily blood ethanol elevation and the
development of physical dependence. Pharmacol Biochem Behav 3: 1119–1123.
Sarter M, Bruno JP, Parikh V, Martinez V, Kozak R, Richards JB, (2006) Forebrain
dopaminergic-cholinergic interactions, attentional effort, psychostimulant
addiction and schizophrenia. EXS. 98: 65-86.
Scherrer JF, Grant JD, Duncan AE, Sartor CE, Haber JR, Jacob T, Bucholz KK (2009)
Subjective effects to cannabis are associated with use, abuse and dependence
after adjusting for genetic and environmental influences. Drug Alcohol Depend.
105: 76-82.
Schuster CR, Balster RL (1977) The discriminative stimulus properties of drugs. In:
Thompson T, Dews PB (eds) Advances in behavioral pharmacology. Academic,
New York. 85–138.
Schuster CR, Johanson CE (1988) Relationship between the discriminative stimulus
properties and subjective effects of drugs. Psychopharmacol. 4: 161-175.
Schwope DM, Bosker WM, Ramaekers JG, Gorelick DA, Huestis MA (2012)
Psychomotor performance, subjective and physiological effects and whole blood
9-tetrahydrocannabinol concentrations in heavy, chronic cannabis smokers
following acute smoked cannabis. J. Anal. Toxicol. 36: 405–412.
Shelton KL, Young JE, Grant KA (2001) A multiple schedule model of limited access
drinking in the cynomolgus macaque. Behav Pharmacol 12: 559–573.
Sim-Selley LJ, Schechter NS, Rorrer WK, Dalton GD, Hernandez J, Martin BR, and
Selley DE (2006) Prolonged recovery rate of CB1 receptor adaptation after
cessation of long-term cannabinoid administration. Mol Pharmacol 70: 986–996.
197
Singh H, Schulze DR, McMahon LR (2011) Tolerance and cross-tolerance to
cannabinoids in mice: schedule-controlled responding and hypothermia.
Psychopharmacology (Berl). 215: 665-675.
Simms JA, Steensland P, Medina B, Abernathy KE, Chandler LJ, Wise R, et al (2008)
Intermittent access to 20% ethanol induces high ethanol consumption in Long-
Evans and Wistar rats. Alcoholism: Clinical and Experimental Research, 32:
1816–1823.
Singer G, Oei TP, Wallace M (1982) Schedule-induced self-injection of drugs. Neurosci
Biobehav Rev. 6: 77-83.
Sim-Selley LJ, Schechter NS, Rorrer WK, Dalton GD, Hernandez J, Martin BR, and
Selley DE (2006) Prolonged recovery rate of CB1 receptor adaptation after
cessation of long-term cannabinoid administration. Mol Pharmacol 70: 986–996.
Slifer BL (1983) Schedule-induction of nicotine self-administration. Pharmacol Biochem
Behav. 19: 1005–1009.
Slifer BL, Balster RL (1985) Intravenous self-administration of nicotine: with and without
schedule-induction. Pharmacol Biochem Behav. 1: 61-69.
Sofuoglu M, DeVito EE, Waters AJ, and Carroll KM (2013): Cognitive Enhancement as a
Treatment for Drug Addictions. Neuropharmacology 64: 452-463.
Solowij N (1995) Do cognitive impairments recover following cessation of cannabis use?
Life Sciences 56: 2119-2126.
Solowij N, Michie PT, Fox AM (1995): Differential impairments of selective attention due
to frequency and duration of cannabis use. Biol Psychiatry 37:731-739.
Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. (2002)
Cognitive functioning of long-term heavy cannabis users seeking treatment.
JAMA 287:1123-1131.
198
Stewart JL and McMahon LR (2010) Rimonabant-induced Δ9-THC withdrawal in rhesus
monkeys: discriminative stimulus effects and other withdrawal signs. J
Pharmacol Exp Ther. 334: 347-356.
Stretch R, Gerber GJ, Lane E (1976) Cocaine self-injection behaviour under schedules
of delayed reinforcement in monkeys. Can J Physiol Pharmacol. 54: 632-638.
Slifer BL (1983) Schedule-induction of nicotine self-administration Pharmacol Biochem
Behav 19: 1005-1009.
Spealman RD and Goldberg SR (1973) Drug self-administration by laboratory animals:
Control by schedules of reinforcement. Annu Rev Pharmaeol Toxieol 18: 313-
339.
Spealman RD and Goldberg SR (1982) Maintenance of schedule-controlled behavior by
intravenous injections of nicotine in squirrel monkeys. J Pharmacol Exp Ther.
223: 402- 408.
Taffe MA (2012) Δ9-Tetrahydrocannabinol attenuates MDMA-induced hyperthermia in
rhesus monkeys. Neuroscience. 201: 125-133.
Takahashi RN, Singer G (1979) Self-administration of delta 9-tetrahydrocannabinol by
rats. Pharmacol Biochem Behav. 11: 737-740.
Takahashi RN, Karniol IG (1975) Pharmacologic interaction between cannabinol and
delta 9-tetrahydrocannabinol. Psychopharmacologia. 41: 277–284.
Tanda G, Munzar P, Goldberg SR (2000) Self-administration behavior is maintained by
the psychoactive ingredient of marijuana in squirrel monkeys. Nat Neurosci. 3:
1073–1074.
Tanda G (2016) Preclinical studies on the reinforcing effects of cannabinoids. A tribute to
the scientific research of Dr. Steve Goldberg. Psychopharmacology (Berl). 233:
1845-1866.
199
Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA, et al. (2007)
Functional MRI of inhibitory processing in abstinent adolescent marijuana users.
Psychopharmacology (Berl) 194: 173–183.
Tashkin DP (1990) Pulmonary complications of smoked substance abuse. The Western
Journal of Medicine, 152: 525–530.
Terry P, Witkin JM, Katz JL (1994) Pharmacological characterization of the novel
discriminative stimulus effects of a low dose of cocaine. J Pharmacol Exp Ther
270: 1041–1048.
Tyndale RF, Payne JI, Gerber AL, Sipe JC (2007) The fatty acid amide hydrolase C385A
(P129T) missense variant in cannabis users: studies of drug use and
dependence in Caucasians. Am. J. Med. Genet. B Neuropsychiatr. Genet. 144B:
660-666.
Valjent E, Maldonado R (2000) A behavioural model to reveal place preference to Δ9-
tetrahydrocannabinol in mice. Psychopharmacology 147: 436–438.
Varvel SA, Hamm RJ, Martin BR, Lichtman AH (2001) Differential effects of delta 9-THC
on spatial reference and working memory in mice. Psychopharmacology (Berl).
157: 142-150.
Verrico CD, Gu H, Peterson ML, Sampson AR, Lewis DA (2014) Repeated Δ9-
tetrahydrocannabinol exposure in adolescent monkeys: persistent effects
selective for spatial working memory. Am J Psychiatry. 171: 416-25.
Vivian JA, Green HL, Young JE, Majerksy LS, Thomas BW, Shively CA, Tobin JR,
Nader MA, Grant KA (2001) Induction and maintenance of ethanol self-
administration in cynomolgus monkeys (Macaca fascicularis): long-term
characterization of sex and individual differences. Alcohol Clin Exp Res 25:
1087–1097.
200
Wagner M, Schulze-Rauschenbach S, Petrovsky N, Brinkmeyer J, von der Goltz C,
Gründer G, Spreckelmeyer KN, Wienker T, Diaz-Lacava A, Mobascher A,
Dahmen N, Clepce M, Thuerauf N, Kiefer F, de Millas JW, Gallinat J, Winterer G
(2013) Neurocognitive impairments in non-deprived smokers--results from a
population-based multi-center study on smoking-related behavior. Addict Biol.
18:752-761.
Walker S, Fields R, King S, (2013) A bill for an act concerning penalties for persons who
drive while under the influence of alcohol or drugs and in connection therewith
making an appropriation. House Bill 114. Colorado State Assembly.
Ward AS, Comer SD, Haney M, Foltin RW, Fischman MW (1997) The effects of a
monetary alternative on marijuana self-administration. Behav Pharmacol. 8: 275-
286.
Wayner MJ, Greenberg I (1972) Effects of hypothalamic stimulation, acclimation and
periodic withdrawal on ethanol consumption. Physiol Behav 9: 737–740.
Winsauer PJ, Lambert P, and Moerschbaecher JM (1999) Cannabinoid ligands and their
effects on learning and performance in rhesus monkeys. Behav Pharmacol 10:
497–511.
Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM, et al.
(2004): Long-term heavy marijuana users make costly decisions on a gambling
task. Drug Alcohol Depend 76: 107-111.
Wise RA (1973) Voluntary ethanol intake in rats following exposure to ethanol on various
schedules. Psychopharmacologia 29: 203–210.
Woolverton WL and Nader MA (1990) Experimental evaluation of the reinforcing effects
of drugs. in Modern Methods in Pharmacology: Testing and Evaluation of Drugs
of Abuse. (Adler MW and Cowans A eds) 165-192, Wiley-Liss, Inc., New York.
201
Wright MJ, Vandewater SA, Taffe MA (2013) Cannabidiol attenuates deficits of
visuospatial associative memory induced by Δ9-tetrahydrocannabinol. Br. J.
Pharmacol. 170: 1365–1373.
Young AM, Masaki MA, Geula C (1992) Discriminative stimulus effects of morphine:
effects of training dose on agonist and antagonist effects of mu opioids. J
Pharmacol Exp Ther 261:246–257.
Yurgelun-Todd D (2007) Emotional and cognitive changes during adolescence. Curr
Opin Neurobiol. 17: 251-257.
Zacny JP, de Wit H (1991) Effects of food deprivation on subjective effects and self-
administration of marijuana in humans. Psychol Rep. 68: 1263–1274.
Zhang ZF, Morgenstern H, Spitz MR, et al. (1999) Marijuana use and increased risk of
squamous cell carcinoma of the head and neck. Cancer Epidemiology,
Biomarkers & Prevention. 8: 1071–1078.
202
SCHOLASTIC VITAE
NAME: WILLIAM SCOTT JOHN CURRENT TITLE: Ph.D. Candidate ADDRESS: Department of Physiology & Pharmacology Wake Forest University School of Medicine Medical Center Boulevard, NRC 547 Winston-Salem, NC 27157-1083 Telephone: (336) 713-7166 Telefax: (336) 713-7180 E-mail: [email protected]
PERSONAL INFORMATION:
BIRTHPLACE: Mount Airy, NC, USA DATE OF BIRTH: February 11, 1988 MARITAL STATUS: Married: Joanna Elizabeth John
EDUCATION:
2012 - Present Wake Forest University School of Medicine (July, 2016 graduation) Ph.D. Integrative Physiology & Pharmacology
Dissertation: Behavioral mechanisms of Δ9-THC
in nonhuman primates Advisor: Michael A. Nader, Ph.D.
2006 - 2010 University of North Carolina at Greensboro B.S. Biology Graduated with Departmental Honors RESEARCH EXPERIENCE:
Jan. 2013 – Present Graduate Research Michael A. Nader, Ph.D. Department of Physiology & Pharmacology Wake Forest University School of Medicine Aug. 2012 – Dec. 2012 Graduate Research Rotation Christos Constantinidis, Ph.D. Department of Neurobiology & Anatomy Wake Forest University School of Medicine 2011 – 2012 Laboratory Technician II Larry Liao, Ph.D. Duke Human Vaccine Institute
Duke University Medical Center
203
2010 – 2011 Research Assistant Keith Erikson, Ph.D. Department of Nutrition University of North Carolina at Greensboro PROFESSIONAL MEMBERSHIPS:
2014 – Present International Study Group Investigating Drugs as Reinforcers (ISGIDAR)
2014-Present Western North Carolina Chapter of the Society
for Neuroscience
2013-Present The American Society for Pharmacology and Experimental Therapeutics (ASPET)
2008-2010 TriBeta National Biological Honor Society HONORS, AWARDS, AND FELLOWSHIPS:
2016 NIDA Director’s Travel Award to The College on Problems of Drug Dependence (CPDD)
2016 Ruth L. Kirschstein National Research Service
Award (NIDA)
2016 Predoctoral Travel Award Behavioral Pharmacology Society Meeting
2016 Graduate Student Travel Award ASPET Annual Meeting at Experimental
Biology
2015 Graduate Student Travel Award, Behavior, Biology and Chemistry: Translational Research in Addiction Annual Meeting
2014 Institutional Training Fellowship (T32)
Collaborative Research on Addiction initiative at NIH (CRAN)
2014 William L. Woolverton Memorial ISGIDAR Travel Award
2014, 2015 Alumni Student Travel Award WFU Graduate School of Arts and Sciences
204
2014 Graduate Student Travel Award, Behavior, Biology and Chemistry: Translational Research in Addiction Annual Meeting
2012-2013 Graduate Fellowship Department of Integrative Physiology and
Pharmacology, Wake Forest University School of Medicine
2008-2010 TriBeta National Biological Honor Society 2006-2010 NCAA Div. I Scholarship Student Athlete INSTITUTIONAL SERVICE:
2014-Present Matching Matriculates and Return Students (MMARS) program
GUEST REVIEWER:
Addiction Biology Biological Psychiatry Drug and Alcohol Dependence Human Brain Mapping Journal of Neuroscience Methods Neuropsychopharmacology Pharmacology, Biochemistry and Behavior
TEACHING EXPERIENCE: Wake Forest School of Medicine Fall 2015 Animal Models of Human Disease (COMD 706)
BIBLIOGRAPHY:
JOURNAL ARTICLES: Keck T, John WS, Nader MA, Newman AH (2015). Identifying medication targets
for psychostimulant addiction: Unraveling the dopamine D3 receptor hypothesis. Journal of Medicinal Chemistry: 58: 5361-80. John WS, Newman AH, Nader MA (2015). Differential effects of acute and
repeated dopamine D3 receptor antagonism in cocaine and methamphetamine self-administration in rhesus monkeys. Neuropharmacology: 92: 34-43.
John WS, Banala AK, Newman AH, Nader MA (2014). Effects of buspirone and
the dopamine D3 receptor compound PG 619 on cocaine and methamphetamine self-administration using the drug-food choice paradigm. Psychopharmacology: 232: 1279-89.
205
Martelle SE, Nader SH, Czoty PW, John WS, Duke AN, Garg PK, Garg S, and
Nader MA (2014). Further characterization of quinpirole-elicited yawning as a model of dopamine D3 receptor activation in male and female monkeys. J Pharmacol Exp Ther 350: 205-11. John, WS and Nader, MA (2016). Effects of acute ethanol administration on
cocaine self-administration under a second-order schedule of reinforcement. Under review. John, WS, Martin, TJ, Sai, KKS, Nader, SH, and Nader, MA (2016). Chronic Δ9-
THC administration in nonhuman primates: Effects on cognition, sleep, and dopamine D2 receptor availability. Under review. John, WS, Martin, TJ, and Nader, MA (2016). Determinants of cannabinoid self-
administration in Old-World monkeys. Under Review.
John, WS, Newman, AH, Hopkins, C, Nader, MA. Effects of dopamine D2, D3,
and D4 selective antagonists on cocaine self-administration in rhesus monkeys using a food-drug choice paradigm. In preparation. John, WS and Nader, MA. Cannabinoid modulation of cocaine-food choice in
rhesus monkeys. In preparation. Czoty PW, John WS, Newman AH, Nader MA. Ethanol/cocaine interactions:
conditioned and unconditioned behaviors in rhesus monkeys. In preparation. BOOK CHAPTERS: Nader MA, Lile JA, John WS, Czoty PW (2015). Nonhuman primate models of
abuse liability. In GF Weinbauer, F Vogel (Eds) “Primate Biologics Research at a Crossroads.” Waxmann Publishing Co: Muenster, Germany: 37-59.
ABSTRACTS:
John, WS and Nader, MA (2016). Characterization of the behavioral phenotypes
underlying the reinforcing effects of cannabinoid receptor agonists in rhesus monkeys. Experimental Biology (American Society for Pharmacology & Experimental Therapeutics). San Diego, CA. John, WS and Nader MA (2016). Effects of chronic Δ9-THC self-administration on cognition and the reinforcing effects of Δ9-THC in nonhuman primates. Wake
Forest University Graduate School Research Day. Winston-Salem, NC.
Czoty PW, John WS, Nader MA, Newman AH, Epperly P (2015).
Ethanol/cocaine interactions: conditioned and unconditioned behaviors in rhesus monkeys. American College of Neuropsychopharmacology. Hollywood, FL.
John, WS, Newman AH, Nader MA (2015). Individual differences in the efficacy
of dopamine D3 receptor antagonism in monkey models of psychostimulant
206
abuse. Wake Forest University Neuroscience Graduate School Research Day . Winston-Salem, NC.
John WJ, Newman AH, Nader MA (2015). Differential effects of the dopamine D3 receptor antagonist PG01037 on cocaine and methamphetamine self-administration in rhesus monkeys. Experimental Biology (American Society for Pharmacology & Experimental Therapeutics). Boston, MA.
John WS and Nader MA (2015). Self-administration of the cannabinoid agonist
CP 55,940 in Old-World monkeys. Behavior, Biology and Chemistry: Translational Research in Addiction. San Antonio, Texas.
Boateng, CA, Okunola-Bakare O, Keck TM, Burzynski C, Rais R, Slusher B, Donthamsetti P, Javitch JA, John WS, Czoty PW, Nader MA, Newman AH
(2014). The development of novel dopamine D3 receptor selective partial agonists as potential medications to treat psychostimulant abuse. Society for Neuroscience. Washington, D.C. John, WS, Newman AH, Nader MA (2014). Dopamine D2/D3 receptor modulation of food-drug choice in rhesus monkeys. Wake Forest University Neuroscience Graduate School Research Day. Winston-Salem, NC.
John WS, Newman AH, Nader MA (2014). Effects of buspirone and the
dopamine D2/D3 receptor antagonist PG 01037 on food-drug choice in rhesus monkeys. Behavior, Biology and Chemistry: Translational Research in Addiction. San Antonio, Texas.
John, WS, Newman AH, Nader MA (2014). Dopamine D2/D3 receptor modulation
of food-drug choice in rhesus monkeys. Wake Forest University Graduate School Research Day. Winston-Salem, NC.
John WS, Newman AH, Nader MA (2014). Dopamine D2/D3 receptor modulation
of food-drug choice in rhesus monkeys. The College on Problems of Drug Dependence. San Juan, Puerto Rico.
Martelle SE, Nader SH, Czoty PW, John WS, Newman AH, Nader MA (2014).
Dopamine D3 receptor availability: sex differences and effects of chronic drug exposure. The College on Problems of Drug Dependence. San Juan, Puerto Rico.
John WJ, Green HL, Newman AH, Nader MA (2014). Effects of selective dopamine D3 receptor compounds in nonhuman primate models of cocaine and methamphetamine abuse. Experimental Biology (American Society for Pharmacology & Experimental Therapeutics). San Diego, CA.
INVITED PRESENTATIONS: John, WS and Nader, MA (2016). Characterization of the behavioral phenotypes underlying the reinforcing effects of cannabinoid receptor agonists in rhesus monkeys. The College on Problems of Drug Dependence. Palm Springs, CA.
207
John WS and Nader, MA (2016). Behavioral determinants of cannabinoid
receptor agonist self-administration in nonhuman primates. Behavioral Pharmacology Society Meeting, San Diego, CA. John WS (2015). Behavioral and pharmacological determinants of cannabinoid
reinforcement and effects on cognition in rhesus monkeys. Student Seminar Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC. John WS, Nader MA (2015). Behavioral mechanisms of cannabinoid
reinforcement: Towards a model of Δ9-THC self-administration in rhesus monkeys. Wake Forest University-Emory Annual Lab Exchange. Atlanta, GA. John WS (2015). Role of Kappa Opioid Receptors in Stress. Student Seminar
Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC.
John WS (2014). Differential effects of acute and repeated dopamine D3 receptor antagonism on food-drug choice: A comparison between cocaine and methamphetamine. Wake Forest University-Emory Annual Lab Exchange. Winston-Salem, NC.
John WS (2014). Effects of buspirone and the dopamine D3 receptor partial
agonist PG 619 on cocaine and methamphetamine self-administration in monkeys. ISGIDAR, San Juan, Peurto Rico. John WS (2014). Behavioral effects of dopamine D2/D3 compounds in nonhuman
primate models of cocaine and methamphetamine abuse. Student Seminar Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC. John WS (2013). Effects of dopamine D2/D3 receptor ligands on cocaine and
methamphetamine self-administration, reinstatement, and discrimination in monkeys. Wake Forest University-Emory Annual Lab Exchange. Atlanta, GA.
John WS (2013). Effects of dopamine D2/D3 receptor ligands on cocaine and
methamphetamine self-administration in monkeys. Student Seminar Series, Dept. Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC.
CURRENT RESEARCH SUPPORT:
F31 DA041825 03/1/16 – Present Mentor: Michael Nader, Ph.D. Ruth L. Kirschstein National Research Service Award (NIDA) Behavioral and pharmacological determinants of cannabinoid reinforcement and effects on cognition in rhesus monkeys
Role: PI
208
COMPLETED RESEARCH SUPPORT:
T32 AA007565 07/01/2014 – 2/29/16 Mentor: Michael A. Nader, Ph.D. Institutional Training Fellowship (T32), Collaborative Research on Addiction Initiative at NIH (CRAN) supplement (NIAAA) Ethanol/cocaine interactions in nonhuman primates
Role: Graduate student