Imaging the Neural Systems for Motivated Behavior and their Dysfunction in

Neuropsychiatric Illness

Hans C. Breiter1,2,3, Gregory P. Gasic1,2,3, and Nicholas Makris2,4

1Motivation and Emotion Neuroscience Collaboration, Department of Radiology,

Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA 2Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital,

Massachusetts Institute of Technology, and Harvard Medical School, Boston, MA, USA 3Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School,

Boston, MA, USA 4Center for Morphometric Analysis, Department of Neurology, Massachusetts General

Hospital and Harvard Medical School, Boston, MA, USA

© Breiter, Gasic, & Makris, 2003

Running Title: Systems Neuroscience of Motivated Behavior

Author correspondence:

Hans C. Breiter Email: hbreiter@partners.org

Gregory P. Gasic Email: ggasic@nmr.mgh.harvard.edu

Nickolas Makris Email: nikos@cma.mgh.harvard.edu

Editorial correspondence:

H.C. Breiter; MGH-NMR Center, 2nd Floor; Building #149-2301, Thirteenth St.;

Charlestown, MA 02129-2060; Phone: 617-726-5715; FAX: (617) 726-7422

General Introduction

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The broad questions posed by astrophysics and by motivation neuroscience are

conceptually similar but converse in their focus, one peering out at the universe and the

other gazing into the brain. The former asks, “what is the nature of what we perceive?” and

“how is something created from nothing?” The latter asks, “how do we perceive, control

interpretation of perception, and exercise free will?” In short, motivation neuroscience

asks, “why is there directed action?”

Motivation is the engine that allows organisms to make choices, direct their

behavior, or plan their actions across time. Directed action or motivated behavior can be

defined as goal-directed behavior that optimizes the fitness of an individual organism (or

social group). Input from a number of evaluative processes around potential goal-objects in

the environment, remembered consequences of previous behavior, internal homeostatic and

socially acquired needs, and perceived needs in other organisms form the basis of

motivated behavior. The combined neural systems that produce this directed behavior

constitute the neural basis of what we call motivation (Watts & Swanson, 2002). Central to

these neural systems are multiple subsystems that have evolved to allow an organism to

assign a value to objects in the environment so as to work for “rewards” and avoid

“punishments” (aversive outcomes). They include an extended set of subcortical gray

matter regions [nucleus accumbens (NAc), caudate, putamen, sublenticular extended

amygdala (SLEA) , amygdala, hippocampus, hypothalamus, and thalamus] (Heimer et al.,

1997) and domains of the paralimbic girdle [including the orbitofrontal cortex (GOb),

insula, cingulate cortex, parahippocampus, and temporal pole] (Mesulam, 2000). Other

networks across the prefrontal cortex also appear to be engaged in the evaluative and

decision making components of motivated behavior. Dopaminergic neurons in the

substantia nigra, the retrotuberal field, and the ventral tegmental area (herein jointly

referred to as the ventral tegmentum: VT) modulate a number of these regions (Schultz,

2002). Less is known about the roles of non-dopaminergic neuromodulators

(noradrenergic, serotonergic, cholinergic, steroid hormones, and neuropeptides) that alter

the balance between excitatory and inhibitory synaptic neurotransmission to guide

motivated behavior. Olds and Milner (Olds and Milner, 1954) were the first to implicate a

subset of these regions in reward-mediated behavior. In subsequent decades, pioneering

studies by others contributed to our further understanding of these systems (Everitt, 1978;

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Gallistel, 1978; LeDoux, et al., 1985; Wise, 1978). Over the last ten years, neuroimaging

have allowed the study of these neural processes in humans and have dissected the

contribution of individual brain regions to the processing of motivationally significant

information. Recent structural and functional neuroimaging has implicated a number of

these brain regions in psychiatric disease.

Since Aquinas, Spinoza, and Bentham, a central question has been how the

experience of reward is perceived across categories of stimuli that reinforce behavior, and

how these rewarding stimuli are experienced relative to aversive or painful events

(Aquinas, 1993; Bentham, 1996; Spinoza, 1883). Today, human neuroimaging studies (at

the limits of their current resolution) have provided evidence for a generalized circuitry

processing rewarding and aversive stimuli. Motivationally salient features of monetary

gains and losses, infusions of drugs of abuse, consumption of fruit juice, processing of

beautiful faces or music, experiences of somatosensory pain, and harbingers of aversive

events all activate a common set of distributed neural circuits that process painful and

hedonic stimuli (Aharon, et al., 2001; Becerra, et al., 2001; Berns, et al., 2001; Blood and

Zatorre, 2001; Breiter et al., 1996a; Breiter, et al., 1997; Breiter, et al., 2001; Elliott, et al.,

2003; Knutson, et al., 2001a; O'Doherty, et al., 2002; Phelps, et al., 2001).

Electrophysiological studies of rodents further suggest that distinguishable local circuits

within some of these neural groups activate selectively to distinct categories of reward

input (Carelli, et al., 2000; Carelli, 2002). Additional studies have begun to dissect the

functions processing reward/aversion information into their subcomponent processes

(Breiter and Rosen, 1999; Breiter, et al., 2001).The results of human studies together with

those in phylogenetically lower species (Kelley and Berridge, 2002; LeDoux, 2000;

Robbins and Everitt, 2002) support the existence of an informational backbone (iBM) that

processes reward/aversion information.

Circuits within this iBM have been shown to be affected in several neuropsychiatric

illnesses (Breiter, et al., 1996b; Crespo-Facorro, et al., 2001; Goldstein, et al., 1999;

Hyman and Malenka, 2001; Manji, et al., 2001; Nestler, et al., 2002; Saxena, et al., 2001;

Schneier, et al., 2000; Volkow, et al., 1997a). This body of research suggests that these

illnesses can be characterized by distinct circuit-based alterations. If some of these

alterations prove heritable, they may serve as endophenotypes for future genetic linkage

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studies. Endophenotypes are defined as heritable/familial quantitative traits, which

correlate with individual’s risk of developing a disease but are not a hallmark of disease

progression (Almasy and Blangero, 2001). Along with state-sensitive alterations, circuitry-

based quantitative traits may serve as better indicators of the neural systems biology (Hill,

2001; O’Brien, et al., 2000; Stoll et al., 2001) than diagnostic categories based on statistical

associations of behavioral signs and symptoms currently used for psychiatric diagnosis. A

detailed characterization of neural circuits in affected individuals, their family members,

and family-based matched controls (thereby producing a “systems biology map”) may

enable us to characterize the polymorphisms that combine to produce these circuit-based

alterations.

Recent studies have alluded to a correspondence between events at the molecular

and brain circuitry levels during presentation of motivationally salient stimuli (Barrot, et

al., 2002; Becerra, et al., 2001; Sutton and Breiter, 1994). This correspondence across

different scales of brain function suggests that we are observing similar molecular cascades

in distributed neural networks responsible for an emerging systems behavior (Ben-Shahar,

et al., 2002). To develop these interlinked concepts, this chapter will be organized in five

sections. A general model of motivation and the embedded systems for processing

reward/aversion informtion (i.e., via an iBM) as well as those that give rise to emotion will

be discussed first. The second section will describe one of the widely used approaches in

brain mapping, namely functional magnetic resonance imaging (fMRI), which has provided

important insights into the neural systems in humans involved with emotion and the

processing of reward/aversion information. The third section will describe converging

evidence from human fMRI and other neuroimaging studies, as well as physiological

studies in animals, for a common circuitry processing reward/aversion information and its

component sub-processes. The fourth section will synthesize a body of human

neuroimaging evidence that argues for a dysfunction in components of this reward/aversion

circuitry in neuropsychiatric illnesses. The final section will describe how a dense mapping

of the neural systems responsible for reward/aversion function should allow us to

chracterize the networks of genes responsible for the development and maintenance of

these neural circuits. These integrative neuroscience approaches have the potential to

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redefine our conceptualization of neuropsychiatric illnesses through the use of objective

quantitative measures.

Theoretical Basis for Motivation

In everyday life, humans make choices to guide their behavior, integrating

unconscious and conscious mental processes. These processes involve a systematic

evaluation of: (a) goal-objects in the environment, (b) memories of outcomes from previous

behaviors directed toward goal-objects, (c) internal physiological and mental needs, and (d)

the perceived social needs of other cooperative and competitive organisms. The confluence

of these evaluative processes has classically been referred to as a drive or motivational

state, which is required to explain the intensity and direction of behavior (Breiter and

Rosen, 1999). In the context of evolutionary pressures, or selection of fitness, motivational

states guide the choice of goal objects and activities that will maximize personal fitness

over time.

Motivational states commonly require planning over time, or planning in parallel of

alternative behaviors and choices. Not all drives that control behavior (i.e., curiosity) have

a well-defined temporal relationship to environmental events (Breiter and Rosen, 1999;

Kupferman et al., 2000), but all drives select outcomes to produce variable arousal and

satiation/relief. Motivational states allow an organism to intentionally monitor its needs

over time (as opposed to doing so in a stimulus-response based fashion), and to select

environmental opportunities that fulfill these needs. Multiple motivational states can be

present concurrently (e.g., while camping during winter you become cold, tired, and

hungry). Homeostatic and biological needs related to glucose level, osmolality, oxygen

saturation of hemoglobin, or thermoregulation may thus be arrayed with inter-organism and

social objectives related to issues such as defense, shelter, procreation, hierarchical

ordering, or curiosity. The collective neuronal and physiological processes that mediate

drives based on such needs, and linked intentional activity that meets these needs via

behavioral processes inclusive of decision-making, speech, and imagination, can be

collectively referred to as motivation.

Current thinking about motivational function represents a direct challenge to the

simple stimulus-response model of behaviorism. Behaviorism framed goal-objects in the

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environment as having organizing effects on behavior in “stimulus-response” relationships.

Goal-objects or stimuli that produced repeated approach behaviors or response repetitions

were called “rewards”. As an incentive for approach behavior, a rewarding stimulus could

act either via a memory or via salient sensory properties (i.e., a food odor) of the stimulus.

Rewarding stimuli that reinforce previous behavior, in contrast, would increase the

probability that preceding behavioral responses would be repeated (i.e., drug self-

administration). This behavioral perspective had difficulties with concepts such as target

detection by the brain (Brown et al., 1994). Likewise, its stimulus-response framework did

not allow for symbolic manipulation as described by Chomskian linguistics (Freeman,

2001), or inferential processes (Montague et al., 1995). These deficiencies led to a

conceptual revolt manifested by cognitive neuroscience (Hauser et al., 2002; Kosslyn and

Shin, 1992; Marr, 1982; Miller, 2003), neuro-computation (Hopfield, 1982; Cohen and

Grossberg, 1983; McClelland and Rumelhart, 1985; Churchland and Sejnowski, 1992),

judgment and decision making (Mellers, et al., 1998; Shizgal, 1999), emotion neuroscience

(Davidson, et al., 2002;LeDoux, 1996; Panksepp, 1998), behavioral ecology (Glimcher,

2003; Kreb and Davies, 1997) and a more “pragmatist” perspective framed by nonlinear

dynamics (Freeman and Barrie, 1994; Freeman, 2001) and the effects of nonlinear

processes on system information (Yorke and Yorke, 1979; Grebogi et al., 1983, 1986).

A general schema for motivation functions, which synthesizes these viewpoints, and

is consistent with recent neuro-computational evidence (Dayan et al., 2000; Freeman and

Barrie, 1994; Sutton and Barto, 1981), is illustrated in Figure 1a. In this schema, at least

three fundamental operations are ascribed to motivated behavior (Breiter and Rosen, 1999),

which have precursors in models of animal cognitive physiology function and

communcation theory (Shannon and Weaver, 1949) (Figures 1b and 1c). They include a

number of processes: (a) evaluation of homeostatic and social needs, and selection of

objectives to meet these needs, (b) sensory perception of potential goal-objects that may

meet these objectives, assessment of potential reward/aversion outcomes related to these

goal-objects, and comparison of these assessments against memory of prior outcomes, and

(c) assessment, planning, and execution of action to obtain or avoid these outcomes (Breiter

and Rosen, 1999; Herrnstein, 1971; Herrnstein and Loveland, 1974; Mazur and Vaughan,

1987; Mazur, 1994; Rachlin, et al., 1991; Shizgal, 1997, 1999). As these operations rely on

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intricate feedback loops in their production of a behavioral trajectory, they are not

necessarily sequential but orthogonal to time. In humans, the third operation encompasses a

number of potential actions: (1) modulation of attention-based filtering of perceptual input,

(2) organization of motor output to obtain goal objects, (3) use of cognitive, logical, and

internal imagery systems (and their symbolic output in the form of language) to increase

the range of goal-objects that can be obtained, problems that can be solved, or events that

can be experienced (Kosslyn et al., 1999; Modell, 2003; Shizgal, 1999).

The outputs of this system, intentional behaviors, are a form of communication

(Shannon and Weaver, 1949), and also represent a means for modulating sensory inputs to

the brain, which is distinct from top-down adaptation of input once it is in modality-

specific processing streams (Friston, 2002). The reference to communication theory

(Shannon and Weaver, 1949) is not accidental, as it helped foster the revolt against

behaviorism. Claude Shannon and colleagues devised a schema for understanding

communication in its broadest sense, namely how one mind or mechanism affects another.

Their ideas focused on the technical constraints on communication, and never addressed

the “semantic problem” or the “effectiveness problem” of communication. Integration of

ideas from communication theory with neural systems biology has occurred only recently

in domains such as: sensory representation and memory (Churchland and Sejnowski, 1992;

Cohen and Grossberg, 1983; Hopfield, 1982; Pouget et al., 2003), serial response learning

and novelty assessment (Berns, et al., 1997), reward prediction (Schultz, et al., 1997),

conditional probability computation (Breiter and Rosen, 1999), and nonlinear dynamics

underpinning decision making (Freeman & Barrie, 1994; Freeman, 2001). Recently,

attempts have been made to address the “semantic problem” posed by inter-organism

communication, and what constitutes meaning for a biological system. Two viable

hypotheses, which could be considered as two sides of the same coin, have been advanced.

The first, links meaning in biological systems to the intersection of intentional behaviors

between organisms (Freeman, 2001), whereas the alternative hypothesis frames meaning in

the context of organism optimization of fitness over time and tissue metabolic needs

(Breiter and Rosen, 1999). For the latter, inter-organism communication involves message

sets defined by genomic and epigenomic control of the bioenergetics of metabolism

(Breiter & Gasic, 2004).

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A number of the general operations and processes (Figure 1a) have been the target

of experimental dissection. For example, a set of hypothetical informational sub-processes

appear to be active when an animal seeks and finds a object with motivational salience

(Beauchamp, et al., 2002; Breiter and Rosen, 1999; Damasio, 1999b; Dayan and Balleine,

2002; Freeman and Barrie, 1994; Haxby, et al., 1991; Heckers, et al., 2002; Kunst-Wilson

and Zajonc, 1985; Pfaffmann et al., 1977; Shizgal, 1997, 1999; Zeki, 2001) (Figure 2).

These sub-processes include its: (a) reception from the environment or internal milieu

across multiple channels, (b) representation by transient neuronal activity, (c) evaluation

through sensory modality-specific characteristics such as color and motion, (d)

combination across modality at theoretical convergence zones as potential percepts, (e)

encoding into memory and contrasting it with other stimulus memories, and (f) evaluation

for features (rate, delay, intensity, amount, category) that are relevant to organizing

behavior. Evaluation of features includes the: (a) categorical identification of putative

“rewards” or aversive stimuli, (b) valuation of goal object intensity (i.e., strength) and

amount in the context of potential hedonic deficit states, and (c) extraction of rate and delay

information from the object of worth. These operations allow the computation of a rate

function to model temporal behavior (Gallistel, 1990), and of a probability function for

possible outcomes (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992) (Figure

2). How the output of these valuation and probability (i.e., expectation) sub-processes is

combined remains an area of active discussion and inquiry (Shizgal, 1999).

For the model in Figures 1 and 2, a “reward” is defined as positive value that an

animal attributes to a goal-object, a behavioral act, an internal physical state, or a cue

associated with the same. Rewards with a direct temporal connection to homeostatic

regulation depend heavily on the physiological state of the organism (Aharon, et al., 2001;

Cabanac, 1971). An animal can assign a positive or negative value to the stimulus

depending on their internal state and their previous experience with the stimulus. Although

they are often referred to as “deficit states”, vis-à-vis the physiological needs of the

organism (e.g. hunger, thirst, body temperature), they are not trivial to define in the case of

social rewards (e.g. personal or social aspirations). Social rewards do not always have a

clear relationship to deficit states (Aharon, et al., 2001; Cabanac, 1971; Cabanac, et al.,

1971), and may provide insurance over time for satiating some motivational states, or

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avoiding aversive outcomes (LeDoux, 1996; Adolphs, 2003) (Figure 3). In contrast,

aversive events can be directly defined as deficit states whose reduction could be

considered rewarding (Becerra et al., 2001). Rewarding and aversive outcomes are not just

impacted by these potential deficit states, but depend on an assessment of the value and

probabilities for alternative payoffs that do not occur (i.e., counterfactual comparisons

dependent on memory). A simple example of a counterfactual comparison is feeling that

you were not very fortunate when you and a friend saunter down a sidewalk and

simultaneously find money, but she finds a twenty dollar bill and you find a one dollar bill

(Mellers, et al., 1997) (Figure 2).

Objectives for optimizing fitness (observed in Figures 1 and 2) focus on meeting

short-term homeostatic needs, and projected long-term needs via the insurance provided by

social interaction and planning (Adolphs, 2003). They are represented by multiple

motivational states, whose differing temporal demands produce a complex layering for

competing behavioral incentives. This idea was first recognized by Darwin (Darwin, 1872),

who hypothesized that motivational states form the basis for emotion. He theorized that the

expression of emotion, as via facial expression, represented communication between

organisms of internal motivational states. Experimental evidence supporting the thesis of

internal sources for emotion was suggested by the work of Cannon (Cannon and Britton,

1925) and others (Cain, et al., 2002; Davis and Whalen, 2001; LaBar, et al., 1998; LeDoux,

2000). An alternative theory on emotion from James and Lange (James, 1884; Lange,

1985), posited that sensory inputs regarding bodily function were central to emotional

experience. Experimental data has also supported the James-Lange thesis (Damasio,

1999a). Both of these perspectives on emotional function are represented by processes

within the general schema for motivation in Figure 3. By this view, emotion represents an

interaction between processes for (1) evaluation of potential deficit states, (2) prediction of

future needs, (3) processing of sensory input about the condition of the body and others’

bodies, (4) assessment of the presence of potential goal-objects or aversive events that

might alter particular deficit states, (5) retrieval and updating of memories regarding (a) the

outcome of prior deficit states, (b) social interactions plus conversations, and (c) contexts

with particular goal-objects or aversive events. A view of this sort potentially allows for the

intrapsychic complexity of human psychology (Modell, 2003). It conceptualizes emotion

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within the schema of motivation, potentially permitting linkage to processes that have been

a strong focus of cognitive neuroscience research, and synthesizing the original

perspectives of Darwin and James.

In Figure 3, the processes shown in solid blue represent ones that are supported by

behavioral and neuroscience data. These processes determining input and output to the

organism appear to be more readily observed via experimentation than the processes shown

with light green lines and purple dashes. The processes shown in light green are supported

by emerging behavioral data, but much remains to be known regarding their systems

biology. The processes indicated with purple dashes (also see sub-processes at bottom of

Figure 2), in contrast, have become associated with neural activity in a distributed set of

deep brain regions, suggesting that they are part of an informational backbone for

motivation. Intriguingly, they may represent a generalized circuitry for processing

rewarding and aversive events.

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Figures 1-3

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In Vivo Measurement of Human Brain Activity Using fMRI

The majority of the data gathered over the past 15 years characterizing human

motivational function has been collected via tomographic and non-tomographic brain

imaging techniques. Tomographic techniques that localize signal changes in three

dimensional space include: positron emission tomography (PET), single photon emission

computed tomography (SPECT), magnetic resonance imaging (MRI), and optical imaging

techniques. Non-tomographic techniques include electroencephalography (EEG) and

magnetoencephalography (MEG). Each of these techniques has unique benefits that

warrant its use for specific neuroscience questions (please see Toga and Mazziota, 2002 for

technical discussion of such considerations). Functional MRI (fMRI) has been the most

widely used technique to study motivation in humans. In contrast to studies of normative

reward circuitry function, dysfunction of these systems that contribute to neuropsychiatric

illnesses has not yet been commonly studied with fMRI. Given the ease with which fMRI

acquisitions can be combined with other forms of MRI: 1) high-resolution structural

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scanning for morphometric quantitative anatomy measures (see Figure 4a), 2) arterial spin-

labeling scans for absolute resting perfusion, 3) diffusion tensor imaging for white matter

tractography (see Figure 4b), or 4) spectroscopy for chemical signatures related to neural

integrity, fMRI is likely to become a technique more commonly used for researching

neuropsychiatric illness, and potentially clinical diagnoses. What follows is a brief

overview of the development of fMRI and some of the research into what it actually

samples of brain activity.

There are now a number of different fMRI techniques that can be used for making

movies of focal changes in brain physiology related to one or a set of targeted mental

functions. These techniques can be roughly categorized by their use of contrast agents vs.

non-contrast methodology. Functional MRI with contrast agents was first demonstrated by

Belliveau et al (1991), using echo-planar imaging in combination with the paramagnetic

contrast agent gadolinium, bound to a chelating agent, DTPA. This general methodology

works because the presence of Gd-DTPA within the parenchymal vasculature increases the

decay rate of the MR signal (1/T2) in a regionally specific fashion. This, in turn, changes

the image contrast, and serial measurement of image intensity can be converted to regional

cerebral blood volume. If injections of Gd-DTPA are made during different experimental

conditions, for instance the rest condition of no movement and the targeted condition of

finger apposition, contrasting images acquired during each experimental condition can lead

to a measure of cerebral blood volume change associated with the experimental

perturbation to the system. The cerebral blood volume change can be evaluated

statistically, and overlaid on a structural MRI to illustrate the anatomical localization. This

contrast agent imaging approach generally produces the strongest signal of all fMRI

techniques, and is currently the technique of choice in animal studies.

For human studies, the non-contrast technique has become widely utilized. Its

development followed from the classic work of Pauling and Coryell (1936a, b) on the

diamagnetic versus paramagnetic state of oxyhemoglobin and deoxyhemoglobin,

respectively. Subsequent, work by Thulborn and colleagues (1982) evaluated the in vitro

effect of oxygenation on the MRI signal. Independent, groups led by Ogawa and Turner

extended these observations to note similar changes alter T2-weighted signals in vivo in

mammals. Parallel work by Detre and colleagues (1992) demonstrated how to use T1-

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weighted signals to quantify perfusion. With important modifications, Kwong and

colleagues (1992) synthesized these developments to image oxygenation and flow changes

associated with neural activity. The work of Kwong and colleagues was first presented to

other scientists at the 10th Annual Meeting of the Society of Magnetic Resonance, August,

1991, and rapidly replicated and extended (Bandettini et al., 1992; Kwong et al., 1992;

Ogawa et al., 1992).

The non-contrast technique is sensitive to T2*-weighted signal changes, and

critically depends on the observations of Pauling and Coryell (1936a & b) that the magnetic

properties of hemoglobin change from the oxy- state, which is diamagnetic, to the deoxy-

state, which is paramagnetic. Because of this issue, the non-contrast technique has been

called blood oxygen-level-dependent contrast imaging or BOLD (Ogawa et al., 1992).

Blood oxygen-level-dependent contrast results from a set of effects initiated by changes in

local cellular activity. These effects include alterations in cerebral blood flow (CBF) and

cerebral blood volume (CBV) that in general produce increased oxygen delivery beyond

oxygen utilization, so that there is a relative decrease in local deoxyhemoglobin

concentration. A relative decrease in deoxyhemoglobin concentration results in an increase

in the relaxation time T2*, or apparent T2, leading to an increase in MR signal in brain

regions with increased neural activity.

The mechanistic details behind this general model of non-contrast fMRI continue to

be a topic of active research, specifically around (a) the neural correlates of BOLD signal,

(b) the coupling of neural activity with vascular responses, and (c) factors influencing the

concentration of deoxyhemoglobin. What follows is a synopsis of research on the first and

last of these topics given their relevance to interpretation of fMRI studies of normative

motivation, and altered motivational function in the form of neuropsychiatric illness.

In a number of circumstances, BOLD signal changes have been observed to be

proportional to changes in neuronal spike rates (Rees et al., 2000). But other work

involving stimulation of parallel fibres with neutralizing effects on measured spike rates

has shown circumstances where spike rate and CBF diverge (Mathiesen et al., 1998). Data

has been further presented that local field potentials (LFPs) correlate better with

CBF/BOLD effects than spike rate (Logothetis et al, 2001), suggesting that changes in

BOLD signal reflect incoming synaptic activity and local synaptic processing. This

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relationship between LFPs and CBF/BOLD will vary with local neural architecture in that

this relationship has been observed to be linear during climbing fiber stimulation, and non-

linear with parallel fiber stimulation (Mathiesen et al., 1998).

When neural activity is altered, corresponding effects are observed in CBF, CBV,

and oxygen consumption. The weight accorded to these effects and their impact on

deoxyhemoglobin concentration has been a topic of intense investigation. Early in the

development of fMRI, capillary perfusion studies in the rat brain determined that increases

in blood velocity through capillaries was the main determinant of BOLD effects (as

opposed to capillary recruitment) (Villringer et al., 1994). Subsequent research illustrated

the complexity of the relationship between oxyhemoglobin and deoxyhemoglobin

concentrations (Lindauer et al., 2001), and how this relationship was differentially

impacted by oxygen consumption, CBF, and CBV over time after a controlled

experimental stimulus (Yacoub et al., 1999). Recent work has begun to emphasize the

importance of understanding how brain pathology and medications can alter the relative

weightings of oxygen consumption and CBV effects on BOLD signal, so that they become

a dominant factor (Obrig et al., 2002). For example, altered neurovascular coupling has

been observed with carotid occlusion (Rother et al., 2002), transient global ischemia

(Schmitz et al., 1998), subarachnoid hemorrhage (Dreier et al., 2000), and theophylline or

scopolamine treatment (Dirnagl et al., 1994; Tsukada et al., 1998). To date, altered

neurovascular coupling has not been demonstrated to be a consistent effect of Axis I

neuropsychiatric illnesses, although medications, drugs of abuse, and changes in ventilation

or heart rate will alter global parenchymal perfusion and make focal BOLD measures more

difficult (Gollub et al., 1998). It thus remains a defensible hypothesis among psychiatric

neuroimagers using fMRI that illnesses such as major depressive disorder, generalized

anxiety disorder, obsessive compulsive disorder, addiction, and schizophrenia have

neurovascular coupling mechanisms that are similar to those in healthy controls. It also

remains a defensible hypothesis that studies using rewarding stimuli, aversive stimuli, or

stimuli with strong emotional content will not alter neurovascular coupling.

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Figures 4a, b

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Neural Basis for a General Reward/Aversion System Underlying Motivated Behavior

Animal studies aimed at understanding the neural systems that select rewarding

goal-objects and avoid their obverse have focused on the role of the many projection fields

of the VT dopamine neurons, such as the nucleus accumbens (NAc), sublenticular extended

amygdala (SLEA) of the basal forebrain, amygdala, and hypothalamus, and multiple fields

in the paralimbic girdle (Heimer, et al., 1997; Lindvall and Bjorklund, 1974; Mesulam,

2000; Watts and Swanson, 2002) (Figure 5). Within the last 5 years, human neuroimaging

studies have implicated homologous systems in processing reward/aversion information,

and have begun to dissect the contributions of these individual brain regions.

Amongst the first human neuroimaging studies to visualize neural activity in a

subset of these brain regions during the processing of rewarding stimuli were three studies

using monetary or drug infusion rewards (Berns, et al., 1997; Breiter, et al., 1997; Thut, et

al., 1997). In the double-blind cocaine vs. saline infusion study (Breiter et al., 1997)

multiple projection fields of the VT dopamine neurons sere specifically targetted and

visualized (Figure 6). As the study involved chronic cocaine-dependent subjects, the

results correlating subjective reports of euphoria and craving (i.e., a mono-focused

motivational state) to activation in reward circuitry, could not be separated from neuro-

adaptations to subject drug abuse. Follow-up studies in healthy controls using monetary

reward, social reward in the form of beautiful faces, and thermal aversive stimuli confirmed

the initial findings with cocaine and provided strong evidence for a generalized circuitry

that processes stimuli with motivational salience (Aharon, et al., 2001; Becerra, et al.,

2001; Breiter, et al., 1997; Breiter, 1999; Breiter, et al., 2001). Modeling a game of chance,

the monetary reward study temporally segregated expectancy effects from outcomes in

reward regions (Figure 7a). Incorporating principles from Kahneman and Tversky’s

prospect theory as well as Mellor’s decision affect theory (Kahneman and Tversky, 1979;

Mellers, et al., 1997; Mellers, 2000; Tversky and Kahneman, 1992), this experiment

observed rank ordering of signal responses in a set of brain reward regions that reflected

the differential expectancy conditions. Rank ordering of fMRI signal responses to

differential monetary outcomes was also observed. For an outcome shared across

expectancy conditions, strong effects of expectancy on subsequent outcomes, namely

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“counterfactual comparisons” (Mellers, et al., 1997; Mellers, 2000), were measured.

Demonstration of counterfactual comparisons is necessary if expectancy measures are to be

believed. At the high spatial resolution of this 3T fMRI study, a number of reward regions

were activated by expectancy or outcome effects, whereas a few regions were activated by

differential expectancies, outcomes, and counterfactual comparisons. Later studies of

monetary reward have significantly extended these observations of differential expectancy

responses in some reward regions (Knutson, et al., 2001a) and differential outcome effects

(Elliott, et al., 2003). Other important studies with categorical rewards have shown

segregation of expectancy and outcome effects, but not counterfactual comparison effects

(Berns, et al., 2001; O'Doherty, et al., 2002). This concordance of findings between

different research groups, and overlap across studies within the same research group (e.g.

such as similar expectancy findings in the NAc) for a monetary stimulus (Breiter, et al.,

2001) and retrospectively, for a cocaine infusion (Breiter and Rosen, 1999) (Figure 7b),

suggests that there exists a distributed set of circuits for reward function across different

categories of stimuli that are amendable to dissection.

In contrast to these monetary reward studies, presentation of social stimuli such as

beautiful vs. average faces addressed the issue of valuation (see Figure 2). Incorporating a

keypress paradigm, this study objectively quantified the reinforcement value of each

stimulus by measuring the effort that experimental subjects expended to increase or

decrease their viewing time of each face (Figure 8). In addition, this study suggested that

non-rewarding stimuli might produce a different regional signal profile to rewarding

stimuli (Aharon, et al., 2001), an observation that was further supported by a study using

thermal pain (Becerra, et al., 2001) (Figure 9). Together, these studies in healthy controls

noted that “classic” reward circuitry (including the NAc, SLEA, amygdala, VT, and GOb)

processes both rewarding and aversive stimuli, with salient similarities and differences in

the pattern of regional activation (Figure 10a).

The segregation of neural systems that process aversive stimuli from those that

process rewarding stimuli might be an artificial distinction (Kelley and Berridge, 2002).

Comprised of subcortical gray strctures, the “classic reward system” is activated by

aversive stimuli such as thermal pain, expectancies of bad outcomes, and social stimuli that

are not wanted (Aharon, et al., 2001; Becerra, et al., 2001; Breiter, et al., 2001). Comprised

16

of paralimbic cortical and thalamic structures, “classic pain circuitry” is also activated by

rewarding stimuli (see Figure 10b legend for references). This commonality of activation

patterns produced in healthy humans by stimuli with positive and negative outcomes

(Figure 10b) argues that an extended set of subcortical gray matter and paralimbic cortical

regions processes both rewarding and aversive information, and could be considered a

generalized system (Breiter, 1999).

A general survey of studies presenting rewarding stimuli to humans indicates that

this extended set of reward/aversion regions responds across multiple categories of

rewarding stimuli (Figure 11, see figure legend for references), although individual subsets

of neurons may respond to one type of rewarding stimulus but not another. As some of

these studies (Figure 11) only focused on select brain regions, such as the GOb, anterior

cingulate cortex, or amygdala, or did not have the spatial resolution to observe a subset of

subcortical regions, the relative prevalence of documented brain activity in some brain

regions is overweighted. Although the majority of these studies involved a motor

component for the experimental task, the bulk of activations reported did not involve

regions associated with some aspect of motor control (i.e., dorsal caudate, putamen, globus

pallidus, posterior cingulate gyrus, and thalamus). In addition, experiments using the

passive presentation of social/aesthetic stimuli, appetitive stimuli, and drug stimuli

produced activation patterns in subcortical gray matter and paralimbic cortex that

overlapped those produced by tasks that included motor performance. Evidence

summarized in Figure 11 makes it difficult to argue that “classic” reward circuitry (i.e.,

NAc, SLEA, amygdala, VT, GOb) is uniquely involved with reward functions, as a

proportionate number of activation foci are also reported in the rest of the paralimbic

girdle.

Some minor caveats need to be considered when evaluating the literature around a

generalized circuitry for the assessment of reward/aversion information. First, the majority

of studies surveyed in Figure 11 utilized monetary reward, which is theorized to be a ready

substitute for most other categories of reward (Cabanac, 1992; Mellers, et al., 1998). In the

monetary reward studies, expectancy effects are known to be salient, but most of these

reports did not take expectancy into account, and thus the results reflect a combination of

expectancy and outcome effects (Figures 2 & 7). Second, all of the drugs infused into

17

healthy controls have known global effects along with purported regional effects, making

the association of regional activation to subjective reports of euphoria less certain. Finally,

with the exception of a study using chocolate stimuli, none of these reward studies

controlled for the presence of a deficit state, thus raising the question of how to gauge the

relative reward value of these stimuli. Despite these general concerns, there now appears to

be strong similarity between the conclusion drawn from animal and human studies on the

neural basis for reward/aversion (Aharon, et al., 2001; Becerra, et al., 2001; Breiter, et al.,

2001; Kelley and Berridge, 2002).

These subcortical gray matter and paralimbic cortices, which actively assess

reward/aversion information as part of a theoretical iBM (Figures 2, 3), also appear to be

integrated into other processes for: (1) the selection of objectives for fitness and (2)

behavior. Perceptual inputs from multiple channels are processed through successive stages

in unimodal association regions of the frontal, temporal and parietal cortices. This

processing achieves more complex discrimination of features relevant for organizing

behavior (Figure 2). Channel specific information is conveyed to multimodal areas for

intermodal integration in the neocortical heteromodal association areas. In turn,

information relayed to paralimbic and limbic structures such as the cingulate gyrus, the

insula, the orbitofrontal, frontomedial, parahippocampal and temporopolar cortices, as well

as the amygdala, sublenticular extended amygdala, and hippocampus, is used for feature

extraction and encoding. Feature extraction and integration for probability determination

further involves the orbitofrontal cortex, amygdala, and cingulate gyrus, with the ventral

tegmentum and nucleus accumbens septi. Contingent probability assessments require

extensive working memory and attentional resources, and thus will be integrated with

activity in limbic brainstem structures, and heteromodal frontal, parietal and limbic cortices

in a modality- and domain-specific way. These probability assessments are necessary for

making predictions about future homeostatic needs or potential deficit states, and,

accordingly, be involved in selecting objectives for fitness over time (Figures 1-3). They

can also be important, for prediction of effects of social interactions on homeostatic needs,

and for focussing the majority of behavioral output on social functions. Valuation and

probability computations around possible goal objects and events, their combination as

outcomes, and subsequent counterfactual comparisons are further integrated with

18

information regarding the costs of changing body position in space, potential risks to action

and inaction, and discounted benefits of other consummatory opportunities. Subcortical

gray matter and paralimbic cortices function in concert with multiple corticothalamic

circuits for the determination of physical plans and actual behavior. For instance, the

cingulate gyrus has an extensive involvement with the alteration of attention for

motivational state. Other paralimbic cortices and ventral striatal regions interact with the

supplementary motor and premotor frontal areas in preparation for executive behavior and

directed action appropriate to environmental and internal factors (Mesulam, 1998; Nauta

and Feirtag, 1986; Nieuwnhuys et al., 1988; Pandya and Yeterian, 1985).

The circuits that process reward/aversion information as an iBM, and interact with

other brain regions to produce behavior and to determine objectives optimizing fitness, are

fundamental for normal emotion function, and its malfunction. The systems biology of

reward/aversion assessment, like the systems biology of all other sub-processes (e.g.,

attention, memory, symbolic discourse), represents an interface in the interactions of

genome, epigenome, and environment. Across the interface of all motivation subprocesses,

the interaction of genome, epigenome, and environment determines the set of all possible

behavior. This interface can be sampled in a concentrated fashion to cover or characterize a

particular behavioral function in an individual, resulting in a quantitative representation of

the neural processes, necessary for that behavioral function (e.g., valuation in the context of

reward/aversion assessment of a goal-object). If multiple samplings are obtained in each

individual, covering particular sub-processes, the results can be overlaid and evaluated for

correlations across all the individuals studied. The correlations that are identified define a

complex set of physiological and/or mechanistic inter-relationships. These inter-

relationships can be grouped into functionally related clusters, or systems biology maps

(e.g., in cardiovascular function, one can observe vascular, heart, renal, endocrine, and

morphometric clusters). Within such clusters, or systems biology maps, these physiological

and/or mechanistic relationships can also be defined as quantitative phenotypes, which can

be subdivided into sets of phenotypes with different contingent probability functions for

susceptibility to illness/malfunction or resistance to illness/malfunction.

19

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Figures 5-11

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Implications of a Generalized Reward/Aversion Circuitry for Psychiatric Illness

Traditionally, major psychiatric disorders have been characterized on the basis of

behaviors observed from patients and their subjective reports of symptoms. This

phenomenological description of categorical outward signs, exophenotypes, produced the

nosology of illness characterization that is the American Psychiatric Associations

Diagnostic Statistical Manual (DSM) (APA, 1994). Recently, it has been proposed that

neuroscience approaches may replace current symptom-based characterizations of illness,

or exophenotypes, by developing a unitary basis for psychiatric and neurological illnesses

using a nosology based on genes, molecules, neuronal organelles, and specific neural

systems (Cowan, et al., 2000; Cowan, et al., 2002). Such a nosology, focussed on

descriptions of brain structure and function, would have to consider the impact of time, as

many of these neuropsychiatric diseases appear to have a neurodevelopmental and/or

neurodegenerative component (Breiter, et al., 1994; Lewis and Levitt, 2002; Tager-

Flusberg, 1999). At this point, we shall examine the current evidence for a neural systems

approach, focusing on alterations in reward/aversion function, for characterizing and

distinguishing the major (i.e., Axis I disorders per DSM) psychiatric illnesses.

A growing body of neuroimaging work argues that neuropsychiatric illnesses can be

distinguished by alterations in circuitry function observable with positron emission

tomography, single photon emission computed tomography, magnetoencephalograpy,

magnetic resonance spectroscopy, or functional magnetic resonance imaging (fMRI)

(Buchbinder and Cosgrove, 1998; David, et al., 1994). Furthermore, as morphometric MRI

studies allude to the heritability of structural alterations (Narr, et al., 2002; Thompson, et

al., 2001), a circuitry-based nosology would rely on the identification of endophenotypes

for psychiatric disorders (Almasy and Blangero, 2001; Drevets, 1998; Gershon, et al.,

2001; Lenox, et al., 2002; Manji, et al., 2001). The presence of these endophenotypes

would have implications for characterizing the genetic, molecular, subcellular, and cellular

mechanisms that produce them (Caspi, et al., 2003;Egan, et al., 2001; Hariri, et al., 2002;

20

Hyman, 2002; Manji, et al., 2001). Circuitry-based phenotypes have been hypothesized for

disorders such as depression, in that subtyping of depressed patients appears critical for

reducing variability in the patterns of regional activity observed with functional imaging

(Drevets, 1998, 2001). When structural and functional imaging studies of depression with

large cohort sizes or replicated findings are grouped, as least three putative phenotypes are

observed. Distinct patterns of structural and functional alterations are observed for (a)

recurrent depression with strong familial loading (i.e., familial pure depressive disorder),

(b) primary depression with and without obsessive-compulsive disorder and without

manifested familial connections, and (c) primary and secondary depression in older

subjects studied post-mortem (Botteron, et al., 2002; Bremner, et al., 2000; Bremner, et al.,

2002; Drevets, 1998; Drevets, et al., 1999; Drevets, 2000, 2001; Fava and Kendler, 2000;

Krishnan, et al., 1991; Manji, et al., 2001; Nestler, et al., 2002; Ongur, et al., 1998;

Rajkowska, et al., 1999; Rajkowska, et al., 2001; Sapolsky, 2001; Saxena, et al., 2001)

(Figure 12). A number of neuroimaging studies that compared individuals with

neuropsychiatric disorders to unaffected controls have documented qualitative differences

(presence or absence of a regional signal), and quantitative differences (numeric alterations

in the mean or median signal), that allude to circuit-based phenotypes, which may be

heritable markers, or endophenotypes (Breiter, et al., 1996b; Bush, et al., 1999; Drevets,

2000; Fowler, et al., 1996; Heckers, et al., 2000; Manoach, et al., 2000; Manoach, et al.,

2001; Schneier, et al., 2000).

To date, no project has used a unitary set of experimental paradigms to classify the

major categories of psychiatric illness on the basis of their patterns of circuitry function or

structural differences. By compiling studies that compare patients to unaffected controls for

each of the major categories of neuropsychiatric illnesses on the basis of (a) patterns of

resting brain metabolism, blood flow, or receptor binding (b) functional differences in

responses to normative stimuli (i.e., pictures of emotional faces that are rapidly masked in

an effort to present them subconsciously), (c) volumes of brain structure, or (d) quantifiable

chemical signatures of neuronal integrity, in brain systems that collectively process

reward/aversion information, we can already observe patterns characteristic to each

disorder (Figure 13). In Figure 13, anxiety disorders (1), major depressive disorder (2),

and addiction (3) re displayed along a potential continuum at the top of the figure, whereas

21

behavioral disorders (4) and schizophrenia (5) are placed in the lower part of the figure to

separate them. Anxiety disorder (1) is represented by a compilation of studies on post-

traumatic stress disorder, social phobia, and simple phobia, and includes symptom

provocation studies that did not involve a healthy control group (Rauch, et al., 1997;

Rauch, et al., 2000; Schneier, et al., 2000; Schuff, et al., 2001). Major depression (2)

involved the grouping of studies described for Figure 12 (Botteron, et al., 2002; Bremner,

et al., 2000; Bremner, et al., 2002; Drevets, 1998; Drevets, et al., 1999; Drevets, 2000,

2001; Fava and Kendler, 2000; Krishnan, et al., 1991; Manji, et al., 2001; Nestler, et al.,

2002; Ongur, et al., 1998; Rajkowska, et al., 1999; Rajkowska, et al., 2001; Sapolsky,

2001; Saxena, et al., 2001). Addiction (3) groups multiple stimulant addictions (Childress,

et al., 1999; Franklin, et al., 2002; Garavan, et al., 2000; Goldstein, et al., 2001; Grant, et

al., 1996; Li, et al., 1999; Volkow, et al., 1991; Volkow, et al., 1997a; Volkow, et al., 2000;

Volkow, et al., 2001). Behavioral disorders (4) were grouped following more recent

suggestions that place obsessive-compulsive disorder (OCD) on a continuum with tics

(Tourettes), attention deficit hyperactivity disorder, and other behavioral problems such as

conduct disorder and oppositional behavior, and learning disabilities (Jankovic, 2001). For

Figure 13, studies of OCD were grouped (Baxter, et al., 1987; Baxter, et al., 1988; Breiter,

et al., 1996b; Fitzgerald, et al., 2000; Graybiel and Rauch, 2000; Nordahl, et al., 1989;

Perani, et al., 1995; Sawle, et al., 1991; Saxena, et al., 2001; Swedo, et al., 1989). Lastly,

schizophrenia (5) involved a grouping of studies that used subjects who were not actively

psychotic. As this review focuses on the iBM (involving subcortical gray matter and

paralimbic cortices), the schizophrenia grouping included studies with relevance to

negative symptomatology such as amotivation, avolition, and anhedonia (Andreasen, et al.,

1994; Crespo-Facorro, et al., 2001; Goldstein, et al., 1999; Heckers, et al., 2000; Manoach,

et al., 2000; Manoach, et al., 2001).

Overall, circuitry-based phenotypes for the general categories of anxiety disorder,

major depressive disorder, addiction, behavioral disorders, and schizophrenia reveal

differences between patient and control groups primarily in subcortical gray matter and

paralimbic cortices. These brain regions mediate a number of sub-processes, such as

reward/aversion assessment, that are fundamental to emotional function and the generation

of motivated behavior (Adolphs, 2003; Anderson and Phelps, 2002; Berns, et al., 1997;

22

Breiter and Rosen, 1999; Breiter, et al., 2001; Davis and Whalen, 2001; LeDoux, 2000;

Mesulam, 2000; Robbins and Everitt, 2002; Schultz, 2000; Shizgal, 1999; Watts and

Swanson, 2002; Wise, 2002). Dysfunction of these brain regions has been previously

hypothesized to be responsible for a variety of psychiatric symptoms such as olfactory or

gustatory hallucinations, autonomic discharges, episodic amnesias, depersonalizations,

avolition (or lack of motivation), abulia (or lack of will), anaffectiveness (or affective

flattening), asociality, as well as delusion, hallucinations, thought disorder, and bizarre or

disorganized behavior (Mesulam and Geschwind, 1976; Mesulam and Mufson, 1985).

Such observations do not imply that a unitary nosology based on the circuitry of

one or two motivation sub processes would be complete. There already is strong evidence

that disorders such as schizophrenia include abnormalities in multiple cortical regions and

functional domains outside the circuitry implicated in the sub-process of reward/aversion

assessment (Sawa and Snyder, 2002; Shenton, et al., 2001). The characterization and

distinction of neuropsychiatric illnesses on the basis of abnormalities in circuitry for

reward/aversion assessment represents perhaps one dimension of a multi-dimensional

schema for circuitry-based (or systems-based) characterization of neuropsychiatric

diseases. Other dimensions might include processes shown in Figure 3 for attention and

memory (Donaldson, et al., 2001; Seidman, et al., 1998; Wagner, et al., 1998), or processes

involved with sensory perception (Bonhomme, et al., 2001). At the systems biology

interface between genome, epigenome, and environment, any number of brain sub-

processes involved with motivated behavior may be dysfunctional in concert with that of

reward/aversion assessment, producing neuropsychiatric signs and symptoms (Figure 14).

--------------------------------------------------------------------------------------------------------------

Figures 12-14

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Future Prospects: Implications of Linking Reward/Aversion Circuitry to Gene

Function

If an organism is defined by the interaction of its genome-epigenome with the

environment, then all disease states can be represented as a failure of the organism to adapt

effectively to its environment. One view to building a new nosology of psychiatric disease

23

is via common circuitry alterations, which represent adaptation failures on an immediate

time-scale (Figures 12,13). Other views are based on alterations in genetic, molecular, and

neuronal function, which represent adaptation failures on a broader time-scale. In any given

individual, maladaptive changes can manifest themselves at the genetic, molecular, and

organelle level and are a byproduct of the “capacitors” and “gain-controls” responsible for

species-wide adaptations to a changing environment over time (Beaudet & Jiang, 2002;

Kirschner & Gerhart, 1998; Queitsch et al., 2002; Rutherford & Lindquist, 1998; True &

Lindquist, 2000; Vrana et al., 2000). Given that circuitry and molecular genetic functions

are inter-related, it is likely that systems level descriptors (e.g. the reward/aversion systems

described above) and molecular genetic level descriptors may both be necessary

components for the characterization of all neuropsychiatric illness.

Arguments in favor of future use of molecular genetic level descriptors to

characterize psychiatric illness come from characterizations of less prevalent

neuropsychiatric diseases with established neurodegenerative and neurodevelopmental

etiologies (Breiter et al., 1994; Lewis & Levitt, 2002; Tager-Flusberg, 1999). For instance,

prion diseases and many of the neurodegenerative diseases with patterns of mixed

mendelian and/or non-mendelian inheritance (i.e., Alzheimer’s disease, Parkinson's

disease, Frontotemporal dementias, Huntington's disease) have a strong component of their

etiology from two processes. One process involves the dysfunction and/or cell death of a

subset of brain neurons that express an aberrant gene product, while a second process

involves the non cell-autonomous consequences (e.g., altered homeostasis) of this neuronal

vulnerability. These diseases result from an inability to maintain mutant proteins: (a) in a

properly folded and/or functional state, (b) in their proper subcellular organelles, or (c) at

appropriate steady-state levels to prevent their gain-of-function role (Collinge, 2001;

Cummings & Zoghbi, 2000; Dunah et al., 2002; Gusella et al., 1983; Heppner et al., 2001;

Nicotera, 2001; The Huntington’s Disease Collaborative Research Group, 1993; Okazawa

et al., 2002; Shahbazian et al., 2002; Watase et al., 2002). Ultimately, the energy state of

the cell and/or mitochondrial function may become impaired and normal transport

processes may likewise be affected.

Protection against degenerative disease can be conferred by over-expression of

some members of a family of heat-shock proteins that keep proteins in a folded state, and

24

are up-regulated during cellular stress conditions (Li et al, 2002; Opal & Zoghbi, 2002;

Sherman & Goldberg, 2001). Aging causes these cellular defense proteins to decline,

possibly heralding the onset of neurodegenerative disease whose prevalence increases with

age (Li et al, 2002; Opal & Zoghbi, 2002; Sherman & Goldberg, 2001). Lindquist and

colleagues have proposed that molecular systems that keep proteins in a folded state serve

as "capacitors" for cellular evolution (Kirschner & Gerhart, 1998; Queitsch et al., 2002;

Rutherford & Lindquist, 1998; True & Lindquist, 2000).

Another category of adaptation failure is proposed in the form of heritable

alterations in gene expression that do not rely on alterations in DNA sequence (e.g.

methylation of DNA bases) but on the parental origin of the DNA (epigenetic

modifications such as imprinting). For example, Down's syndrome, Turner's syndrome, and

Praeder-Willi and Angelman Syndromes are neuropsychiatric diseases that can be caused

by alterations in gene dosage and/or imprinting rather than by mutations in the DNA itself

(Beaudet & Jiang, 2002; Nicholls & Knepper, 2001; Sapienza & Hall, 2001; Tager-

Flusberg, 1999). Such observations have led to a “rheostat” model for gene expression,

which acts as a gain-control to allow rapid and reversible attenuation of gene expression

(over generations and during development). Alterations in such a gain-control mechanism

may ultimately explain a spectrum of related neuropsychiatric diseases (Beaudet & Jiang,

2002; Vrana et al., 2000).

The molecules that serve as the putative gain-control (i.e., rheostat) and capacitors

for producing adaptive phenotypic variation function in a substantial and stepwise fashion

rather than an incremental and progressive one. Variations in both systems may be present

in neuropsychiatric diseases such as Rett Syndrome (Amir et al., 1999; Shahbazian et al.,

2002; Watase et al., 2002; Zoghbi, 2001). The molecular genetic basis of such

neuropsychiatric diseases may be the outcome of evolutionary events that strike a delicate

balance between minimizing deleterious mutations while allowing phenotypic variations

that are adaptive to a species in a changing environment (Beaudet & Jiang, 2002; Kirschner

& Gerhart, 1998; Queitsch et al., 2002; Rutherford & Lindquist, 1998; Shahbazian &

Zoghbi, 2002; True & Lindquist, 2000; Vrana et al., 2000).

These molecular genetic variations, which may be adaptive or maladaptive in a

changing environment, produce changes observable at a number of spatio-temporal scales

25

of brain function or levels of organization (Figure 15). The parsimonious description of

scales of brain function and their embedding remains a topic of active discussion (see

Churchland and Sjenowski, 1992; Freeman, 2001). There is also an open question of

whether the dynamic principles governing information processing at one level of

organization are applicable to other levels of organization (i.e., neural scale invariance)

(Sutton & Breiter, 1994). For at least one brain region, the NAc, a qualitative similarity is

noted between reports of transcription factor cAMP response element-binding protein

(CREB) phosphorylation in response to aversive and rewarding stimuli (Barrot et al., 2002;

Pliakas et al., 2001), and the signal representing distributed group function observed with

fMRI to similar aversive and rewarding stimuli (Becerra et al., 2001; Breiter et al., 1997;

Breiter et al., 2001). Reverse engineering how activity is linked across levels of brain

organization will have implications for reductive understanding of health and disease. As a

course example of an integrative neuroscience approach to brain disease, circuitry-based

endophenotypes should be identifiable for any given psychiatric disease, allowing

researchers to constrain future genetic association and linkage studies (Figure 16). Such a

“top-down” approach for integrative neuroscience was utilized to find an EEG-based

endophenotype in individuals susceptible to alcohol dependence, forging an association

with a locus that contains a subunit of the GABAA receptor (Porjesz et al., 2002; Reich et

al., 1999; Williams et al., 1999).

The linkage of systems level measures to molecular genetic level descriptors

assumes that the probability of illness manifestation will be related to (a) the probability

associated with having a specific allele(s) at a particular locus (loci), (b) the probability

of having a particular endophenotype, and (c) the probability of having a particular set of

epigenetic elements (e.g., this might be expressed as P(illness) ≈ P(allele, locus) x

P(EndophenotypeN, t) x P(Epigenome, t)). Epigenetic elements appear to be species

specific (Vrana et al., 2000), and may explain significant differences in phenotypes

between species that otherwise have 99% sequence similarity (Paabo, 2001).

Phylogenetically lower animal species may help to a limited degree in elucidating the

abnormalities at discrete spatio-temporal scales of brain function characterizing human

functional illness.

26

Epigenetic issues may partly explain replication difficulties across gene linkage

studies of psychiatric illness. Another salient challenge may be the use of course clinical

measures and dichotomous behavioral distinctions rather than quantitative markers

(endophenotypes) to cluster subjects for these studies. Activity in a variable number of

distinct, distributed neural groups may yield multiple endophenotypes for an illness, yet

produce indistinguishable symptom/sign clusters. The scale of distributed cell groups

controls behavior, and is biased by genetic/epigenetic function. Weinberger and

colleagues have demonstrated with fMRI that genetic variations in COMT and 5HT

transporter are correlated with fMRI signal changes in human amygdala and prefrontal

cortex, respectively (Egan et al., 2001; Hariri et al., 2002). These types of studies

represent a “bottom-up” approach to complement findings from the “top-down” approach

(Figure 17a, b). In some diseases, such as Huntington’s, a single major disease locus

may be enough to produce the endophenotypes and exophenotypes that characterize the

illness. In contrast, oligogenetic and polygenic diseases (Beaudet et al., 2001), such as

Parkinson’s Disease and most neuropsychiatric illnesses, appear to involve more than one

genetic locus. In such cases, future genome-wide association studies using circuit-based

endophenotypes will have to demonstrate that variant alleles at multiple loci (when

quantitative trait loci become quantitative trait nucleotides) are both necessary and

sufficient to produce the alterations in the functional sub-processes and their mediating

neurocircuitry.

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Figures 15 - 17

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27

Acknowledgements:

We would like to thank the following individuals for helpful comments on the

manuscript: Arthur Beaudet, David Colman, Steven Hyman, Michael Moskowitz, Eric

Nestler, Jerrold Rosenbaum, and Huda Zohgbi. We thank Frederick Sheahan for editorial

assistance and commentary.

Work on this was supported by funding from the National Institute of Drug Abuse

(grants #14118 and #09467), the Office of National Drug Control Policy - Counterdrug

Technology Assessment Center (ONDCP-CTAC), and the Massachusetts General

Hospital Department of Radiology. This work was further supported, in part, by the

National Center for Research Resources (P41RR14075), the Mental Illness and

Neuroscience Discovery (MIND) Institute, and the Division on Addictions, Harvard

Medical School.

28

Figure Legends:

Figure 1: During communication, information (H), as defined by Shannon &

Weaver (Shannon and Weaver, 1949), is received and decoded by processes that allow

incoming information to be linked to the set of communicable messages. Messages,

communicated from the organism, are encoded and transmitted in the form of behavior.

Self-organizing organisms always generate entropy as an outcome, which acts as a force

behind the development of complexity in coding/decoding systems such as the brain, and

their evolution toward greater complexity (Prigogine, 1985). Given the interdependence

of these sets of brain processes on each other, they function as if they were all orthogonal

to time.

Figure 2: The theorized processes of the informational backbone for sensory perception,

memory, and reward/aversion assessment can be extensively dissected into sub-

processes, reflecting one interpretation of research from evolutionary ecology (Glimcher,

2003; Shizgal, 1997) and behavioral finance (Kahneman and Tversky, 1979; Shizgal,

1999; Tversky and Kahneman, 1992). Dashed lines indicate sub-processes theorized to

interact with feedback leading to nonlinear system function (Freeman and Barrie, 1994;

Freeman, 2001), whereas solid lines connect sub-processes as steps in the processing of

information. The postulated early sub-processes are: (1) information reception along

discrete channels and representation (hollow points in cartoon of brain), (2) convergence

of processed informational measures such as detected motion, color, and contrast for

vision (thus the ventral and dorsal processing streams represented by arrows moving to

solid points), and (3) convergence of represented information from distinct receptive

channels for construction of a percept (Beauchamp, et al., 2002; Damasio, 1999b; Dayan

and Balleine, 2002; Haxby, et al., 1991; Zeki, 2001). The postulated later sub-processes

include the extraction of informational features necessary to match putative goal-objects

to internally determined objectives that optimize fitness over time (A). Such

motivationally relevant features include rate, delay, category, amount, and intensity

information that are integrated during computation of probability functions and valuation

functions, along with input regarding proximity and extraction risk (i.e., “cost”

assessments) needed for general cost-benefit analyses (Breiter and Rosen, 1999; Shizgal,

29

1999). Doted lines group features integrated in such computations (Gallistel, 1990) for

the determination of rewarding and aversive outcomes. Memory encoding, updating, and

retrieval functions are necessary for these sub-processes, and the evaluation of

counterfactual comparisons (Mellers, et al., 1997; Mellers, 2000).

Figure 3: The abbreviated schematic of the MIT model is shown in (a) to orient the

description of its partial dissection into processes shown in (b). Solid black compartment

lines in (a) are dashed bold black lines in (b). Compartments and connections in solid

blue represent processes and their interactions for which substantial, though not

complete, neuroscience data has been accumulated. Compartments and interactions in

light green represent ones for which there is behavioral research and beginning

neuroscience data (Adolphs, 2003; Cabanac, 1971; Gallistel, 1990), but substantially less

is known than for processes in solid blue. Processes and interactions signified by purple

dashes represent ones for which a significant body of neuroscience has begun to

accumulate, although we remain far from the level of knowledge currently available for

the processes in solid blue. The informational backbone for motivation (iBM), and the

operation for selection of objectives that optimize fitness over time, are starred to

emphasize a synthetic view that their processes comprise those that constitute the

experience of emotion (Damasio, 1999a; Darwin, 1872; James, 1884; LeDoux, 1996).

Figure 4: (a) Morphometric segmentation of T1-weighted MRI data in coronal sections

using the morphometric methodology of the Center for Morphometrc Analysis (Caviness

et al., 1996; Filipek et al., 1994). A., B., C., and D. are four representative coronal

sections of the human brain in the rostral-caudal dimension showing limbic and

paralimbic structures. The colored sphere shows the color coding scheme applied for the

visualization of the tensors: red stands for the medial-lateral orientation, green indicates

the anterior-posterior orientation, and blue shows the superior-inferior orientation.

Abbreviations: FOC=Frontal Orbital Cortex, FMC=Frontal Medial Cortex,

CGa=Cingulate Gyrus (anterior), CGp=Cingulate Gyrus (posterior), NAc=Nucleus

Accumbens, TP=Temporal Pole, INS=Insula, BF/SLEA=Basal Forebrain/Sublenticular

30

Extended Amygdala, Hip=Hippocampus, PH=Parahippocampal Gyrus, VT=Ventral

Tegmental Area, Tha=Thalamus, Hyp=Hypothalamus, BS=Brain Stem, Amy=Amygdala

(b) Diffusion tensor magnetic resonance (DT-MR) image of limbic fiber

pathways. b) shows the primary eigenvector map (PEM) of a coronal slice at the level of

the anterior commissure, the T2-EPI image of this slice is shown in a). Dotted rectangles

highlight regions that include major limbic pathways such as the fornix, cingulum bundle,

medial forebrain bundle and ventral amygdalofugal projection (in the basal forebrain or

BF). Abbreviations: CB: cingulum bundle; UF: uncinate fasciculus; ac: anterior

commissure; CC: corpus callosum; lv: lateral ventricle; ac: anterior commissure. For

further details on DT-MRI, please see Makris et al. (1997, 2002).

Figure 5: Diagrammatic representation of topologic relationships of brainstem and

parcellated forebrain structures (Makris et al., 1999). Limbic and paralimbic structures (in

yellow) and some of their main connections are shown in a schematic fashion. The

diagram is flanked on the left and right by coronal projection planes (PP1, PP2).The

paracallosal coronal slabs I-IV are distinguished by verticals in the interval, displayed on

the midsagittal plane (PP3) of the hemisphere. The numbers aligned along the top of this

plane correspond to the y axis (anterior-posterior) coordinates of the Talairach

stereotactic system (Talairach, and Tournoux, 1988) for the standard brain used to

develop this system. PP1 corresponds to a composite coronal plane (temporal lobe is

forced to be more anteriorly located) projected at the Talairach coordinate, indicated by

the vertical black arrow in paracallosal slab II. PP2 corresponds to the immediately

posterior callosal coronal plane at Talairach coordinate -40. The projection of these slabs

within the temporal lobe is indicated by the step (PP4). Whereas the amygdalo-

hippocampal junction will have the approximate lateral projection of PP4, the

hippocampus and fornix will actually curve medially and approximate the plane of PP3.

Its representation in PP4 is a schematic emphasis of the anterior-posterior projection of

the structure. The decussation of anterior (ac) and posterior (pc) commissures is indicated

by brown squares at PP3/II and IV respectively. Ventricular system (LV) (black) is

projected topologically within the 3-dimensional representation. Cortex: neocortex

(gray), limbic cortex (yellow). nuclei: thalamus (th), caudate (cau), putamen-pallidum

31

(Ln), amygdala (amy) (pink). White matter: radiata (beige); corpus callosum (cc) (red);

internal capsule (IC). Cortical paralimbic structures: parahippocampal gyrus (PH);

temporal pole (TP); fronto-orbital cortex (FOC); frontomedial cortex (FMC). Gray limbic

structures: limbic brainstem (LB); hypothalamus (Hyp); hippocampus (Hip); amygdala

(Amy); septal area (sept); preoptic area (proa); nucleus accumbens septi (NAc);

sublenticular extended amygdala and basal forebrain (SLEA/BF); insula (INS); habenula

(Hb). Abbreviations: White matter: superior sagittal stratum (Ss), inferior sagittal stratum

(Si), temporal sagittal stratum (St). Limbic fascicles shown in this figure: uncinate

fasciculus (UF); cingulum bundle (CB); dorsal hippocampal commissure (dhc), fornix

(fo), fimbria (fi); medial forebrain bundle (MFB); amygdalofugal projection (AFP).

Figure 6: Figure adapted from Breiter et al. (1997). Nonparametric statistical maps in

pseudocolor (with p-value coding bar), showing functional magnetic resonance imaging

“activation”, are juxtaposed on structural images. Activation represents brain signal

related to blood flow and volume changes that are linked to changes in neural local field

potentials. Images on the left show significant signal change in the nucleus

accumbens/subcallosal cortex (NAc/SCC) and sublenticular extended amygdala (SLEA)

to infusion of cocaine and not saline. These images are brain slices in the same

orientation as the human face, and are 12mm (NAc/SCC) and 0mm (SLEA) anterior to a

brain landmark, the anterior commissure. Signal time courses from the NAc/SCC and

SLEA are graphed in the middle of the figure as percent signal change during the cocaine

pre-infusion and post-infusion intervals (infusion onset shown with a blue line). These

signals were correlated with the average behavioral ratings for rush (euphoria and

physiological experience of initial cocaine effects) and craving (motivational drive to

obtain more cocaine) shown in a graph at the bottom of the figure. The statistical

correlations of the behavioral ratings with the brain signal responses to cocaine are shown

as statistical maps on the right of the figure.

Figure 7: (a) These data illustrate an example of brain mapping efforts to temporally

dissect sub-processes shown in Figure 2, using an experimental design that applied

principles of prospect theory and decision affect theory (Figure adapted from Breiter, et

32

al., 2001; Breiter and Gasic, 2004) . This study employed a single-trial like design, shown

at the bottom with three spinners. The trial sequence started with presentation of one of

these spinners, and continued with an arrow rotating on it. This rotating arrow would

abruptly stop after 6 seconds, at which time the sector upon which it had landed would

flash for 5.5 seconds, indicating the subject had won or lost that amount of money. Given

three spinners, each with three outcomes, this experiment sought to determine which

putative reward/aversion regions in the brain would display differential expectancy

and/or outcome effects. With one outcome ($0) shared across spinners, it could explicitly

also evaluate counterfactual comparison effects (Mellers, et al., 1997; Mellers, 2000).

The evaluation of counterfactual comparison effects is necessary to determine that the

experiment did produce expectancy effects, and the incorporation of expectancy effects is

necessary to be able to interpret any outcome effects. The graphs at top display

differential expectancy effects (left), differential outcome effects (middle), and

counterfactual comparisons (right). The y-axes display normalized fMRI signal, while the

x-axes display time in seconds. All time-courses come from a region of signal change in

the sublenticular extended amygdala (SLEA).

(b) Expectancy of a monetary gain in the upper panel from a study involving a

game of chance in healthy controls (Breiter et al., 2001) and expectancy of a cocaine

infusion in the lower panel from a study of double-blind, randomized, cocaine vs. saline

infusions in cocaine dependent subjects (Breiter et al., 1997; Breiter &Rosen, 1999).

Results are shown in the radiological orientation as pseudocolor statistical maps

juxtaposed on coronal group structural images in gray tone. Note the close anatomic

proximity for NAc signal changes during positive expectancy in the context of

uncertainty for both experiments.

Figure 8: Behavioral and fMRI results regarding the viewing of beautiful vs. average

faces adapted from Aharon et al. (2001). A sample of the four picture types used in these

tasks (from left to right) is shown at top: beautiful female, average female, beautiful male

and average male. In the graph just below these sample face pictures, rating responses are

shown for eight heterosexual males who rated picture attractiveness on a 1-7 scale for a

randomized sequence of these pictures. The responses grouped themselves with tight

33

standard deviations in the four categories illustrated at top. This process was interpreted

as a “liking” response, whereas the keypress procedure (whose results are shown as the

second graph down from the top) was interpreted as a “wanting” response (Berridge,

1996). For the keypress procedure, a separate cohort of 15 heterosexual males performed

a task where picture viewing time was a function of the number of their key-presses.

Within each gender, the faces were always presented in a new random order, with

beautiful and average faces intermixed (Aharon et al., 2001). On the lowest graph,

percent BOLD signal from the NAc for a third cohort of heterosexual males is shown for

each face category relative to a fixation point baseline. Significant by a random effects

analysis, the fMRI results in the NAc were driven by the response to the beautiful female

and the beautiful male faces, and more closely approximated the “wanting” response

rather than the “liking” response. On the right, a pseudocolor statistical map of signal

collected during the beautiful female condition vs. the beautiful male condition (with p-

value coding bar) is juxtaposed on a coronal group structural image in gray tone.

Figure 9: In this figure adapted from Becerra et al. (2001), representative coronal slices

containing “classic” reward circuitry [GOb, SLEA, ventral striatum (VS), ventral

tegmentum/periacqueductal gray area (VT/PAG)] are segregated into early and late

phases of BOLD signal change following the 46°C stimulus (left and middle columns).

As in Figures 6, 7b, and 8, the statistical maps are overlaid in pseudocolor on gray scale

average structural maps. The right column of statistical maps shows the overlap (red) of

early (yellow) and late (blue) phase activation. Time courses of % signal change vs. time

are shown in the column at far right for each structure. To aid anatomic localization, the

anterior-posterior coordinate in mm from the anterior commissure for each slice is shown

in the far left column. For this figure, activated pixels are thresholded at p < 5 x 10-4.

Figure 10: (a) Tabular results from studies at one lab (Aharon, et al., 2001; Becerra, et

al., 2001; Breiter, et al., 1997; Breiter and Rosen, 1999; Breiter, et al., 2001) regarding

the analysis of expectancy, or of outcome, show common and divergent patterns of

activation. Up arrows indicate symbolizes positive signal changes while down arrows

stand for negative signal change. Raised numeric notation signifies more than one focus

34

of signal change in that region, whereas brackets indicate the signal change was

statistically subthreshold for that study. Two separate cocaine infusion studies are listed,

as are positive and negative valuation results for the beautiful faces experiment and the

thermal pain experiment. Bilateral NAc and left GOb are observed in both studies with

expectancy conditions. The right GOb, right NAc, right SLEA, and potentially the left

VT, are observed during the outcome conditions for most of the experiments.

(b) The gray tone structural images on the left in the sagittal orientation, and on

the right in the coronal orientation (+6mm anterior of the anterior commissure), juxtapose

published neuroimaging data in humans from painful stimuli (Becerra, et al., 2001;

Becerra, et al., 1999; Coghill, et al., 1994; Craig, et al., 1996; Davis, et al., 1998a; Davis,

et al., 1998b; Ploghaus, et al., 1999; Rainville, et al., 1997; Rainville, et al., 2001;

Sawamoto, et al., 2000; Talbot, et al., 1991) and from rewarding stimuli (Bartels and

Zeki, 2000; Berns, et al., 1997; Berns, et al., 2001; Blood, et al., 1999; Blood and Zatorre,

2001; Breiter, et al., 1997; Breiter, et al., 2001; Bush, et al., 2002; Elliott, et al., 2000;

Elliott, et al., 2003; Ketter, et al., 1996; Ketter, et al., 2001; Knutson, et al., 2001a;

Knutson, et al., 2001b; Liu, et al., 2000; O'Doherty, et al., 2001b; O'Doherty, et al., 2002;

Small, et al., 2001; Thut, et al., 1997) in three brain regions traditionally reported as

“classic” pain regions (Becerra et al., 2001). These region include the thalamus (Thal -

left image between ac and pc), the cingulate cortex, and the anterior insula (INS - right

image). The cingulate cortex is segmented into four units following the standardized

methods of the MGH Center for Morphometric Analysis (Makris, et al., 1999; Meyer, et

al., 1999); the aCG includes aCG1 and aCG2, while the posterior cingulate is the darkest

segmentation unit. Note the close approximation of reported activation from stimuli of

opposite valance.

Figure 11: The top and bottom rows of images indicate, respectively, the anatomy of

subcortical gray matter regions and paralimbic cortex, and reported localization in these

regions of significant signal change for a number of distinct categories of rewarding

stimuli. The gray tone structural images are coronal slices taken (left to right) +18mm,

+6mm, -6mm, and –21mm relative to the anterior commissure. Abbreviations for

anatomy follow the schema adapted from the Massachusetts General Hospital Center for

35

Morphometric Analysis (Breiter, et al., 1997; Breiter, et al., 2001; Makris, et al., 1999;

Meyer, et al., 1999). In this diagram, subcortical gray matter implicated in the processing

of reward and aversion input include the NAc (nucleus accumbens), Put (putamen), Cau

(caudate), SCC (subcallosal cortex), Amyg (amygdala), SLEA (sublenticular extended

amygdala), Hypo (hypothalamus), GP (globus pallidus), Thal (thalamus), Hipp

(hippocampus), VT (ventral tegmentum). Components of the paralimbic girdle include:

sgaCG (subgenual anterior cingulate gyrus), GOb (orbitofrontal cortex), aCG (anterior

cingulate gyrus), pCG (posterior cingulate gyrus), INS (insula), pHip (parahippocampus),

and TP (temporal pole). The colored symbols on the brain slices show reported activation

surveyed from 26 studies of reward function in healthy controls (Aharon et al., 2001;

Bartels and Zeki, 2000; Berns et al., 1997; Berns et al., 2001; Blood et al., 1999; Blood

and Zatorre, 2001; Breiter and Rosen, 1999; Breiter et al., 2001; Bush et al., 2002;

Delgado et al., 2000; Drevets et al., 2001; Elliott et al., 2000; Elliott et al., 2003; Kahn et

al., 2002; Kampe et al., 2001; Ketter et al., 1996 Knutson et al., 2001; Knutson et al.,

2003; Liu et al., 2000; O’Doherty et al., 2001a; O’Doherty et al., 2001b; O’Doherty et al.,

2002; Small et al., 2001; Thut et al., 1997; Volkow et al., 1995; Volkow et al., 1996;

Volkow et al., 1997b). These include ten studies with monetary reward (five with a

guessing paradigm determining compensation, four with a performance task determining

compensation, and one with a prospect theory based game of chance). Four studies

focused on appetitive reward with fruit juice, chocolate, or pleasant tastes, while five

studies focused on some aspect of social reward (two with beautiful faces, one with

passive viewing of a loved face, and two with music stimuli). Five studies involved

amphetamine or procaine reward, and two studies focused on a probabilistic paradigm.

Figure 12: The same structural scans shown in Figure 11 are displayed here, grouped

two-by-two, and numbered to correspond with the anterior-to-posterior orientation. The

three groupings of brain slices at the bottom of the figure display changes in the structure,

function, or morphology of subcortical gray matter and paralimbic cortices for the

following three groupings of studies. Studies grouped as “putative endophenotype

variation 1” were focused on recurrent depression with strong familiality (i.e., sometimes

referred to as familial pure depressive disorder). Studies grouped as “putative

36

endophenotype variation 2” were focused on primary depression with and without

obsessive-compulsive features and without manifested familial connections. Studies

grouped as “putative endophenotype variation 3” were focused on primary and secondary

depression in older subjects who were studied post-mortem (see text for references).

Regions with differences in resting brain metabolism from healthy baselines are noted

with an “O” symbol, while regions with differences in regional morphology or volume

from healthy baselines are noted with a diamond. Most of these studies were not

performed with a family segregation design, yet they do suggest the potential for

circuitry-based endophenotypes for major depressive disorder. Aggregation of such data

across studies, as done for the four slices shown at the top of the figure, point to a strong

focus on the generalized reward/aversion system for circuitry-based alterations

characterizing major depressive disorder. Such circuitry-based sub-types may aid

treatment planning in the future.

Figure 13: The groupings of structural images in gray tone are the same as in Figure 12.

Groupings (1) – (5) are placed like the spokes of a wheel around a central sagittal slice

showing the approximate location of each coronal slice relative to a yellow rectangle

around brain regions containing the subcortical gray matter and paralimbic cortices

hypothesized to produce reward/aversion functions. Each grouping represents a partial

consolidation of findings from the neuroimaging literature comparing patient groups to

healthy controls on the basis of (a) resting metabolism, blood flow, or receptor binding, (b)

blood flow or metabolic responses to normative stimuli (i.e., pictures of emotional faces

that are rapidly masked in an effort to present them subconsciously to subjects with post-

traumatic stress disorder), (c) structural differences, or (d) magnetic resonance

spectroscopy measures (see text for references). As in Figure 12, regions with functional

differences (a & b above) between subjects and healthy controls are noted with an “O”

symbol, while regions with differences in regional morphology, volume, or spectroscopy

signal from healthy baselines (c & d above) are noted with a diamond. Regions with an

asterisk are noted when a set of studies implicate a difference between patients and healthy

controls for a large region, and a more recent study with significantly better spatial

resolution in healthy controls notes an effect to the same experimental paradigm (i.e.,

37

amphetamine infusions) localized to a specific subregion (i.e., the NAc vs. the basal

ganglia). The clinical groupings used for (1) – (5) are listed above each set of slices and

described in detail in the text. This schema supports the hypothesis that neuropsychiatric

illness may lend itself to objective diagnosis by use of circuitry-based neuroimaging

measures.

Figure 14: (a) This diagram emphasizes the tripartite division of influences that shape an

organism, namely the genome, epigenome, and environment. The set of all possible

behaviors for an organism (i.e., communication) is determined by these three influences,

although the specific sequence of output is not. (b) The internal environment produced by

the genome/epigenome produces the putative spatiotemporal scales of brain function. In

this case, activity at the level of distributed groups of cells, local networks or groups of

cells, and individual neurons modulate the function of the genome/epigenome, and

activity at the level of the genome/epigenome significantly modulates the function of

each of the spatiotemporal scales of function that embed it. The linked spatiotemporal

scales of brain function are again distinct from observed behavior in the outside world

(i.e., exophenotype) and will have a stronger connection, as endophenotypes observable

with neuroimaging and other measurement systems of brain function, with the

genome/epigenome. The scale of distributed groups of cells produces behavior, and

accordingly serves as an interface between the environment and genome/epigenome.

Figure 15: As a rough approximation, brain processes can be analogized to a set of

nested scales of function. The genome/epigenome is nested in cells (neural and glial),

which in turn are nested in neural groups as local circuits, which in turn are nested in sets

of inter-connected groups that are distributed across the brain and modulated by

monoaminergic and hormonal systems. The scale of distributed neural groups, which

produces systems biology, can be sampled using a number of distinct technologies,

including tomographic imaging modalities such as fMRI and PET. Local circuits or

neural groups, comprised of excitatory and inhibitory synapses, axonal and dendro-

dendritic circuits, can be sampled by multicellular recording techniques. The individual

38

cell, with its intracellular signaling and surface receptors, can also be characterized by

measures of local field potential and sequences of action potentials. From the scale of

molecular genetics to that of distributed neural groups, reductionistic explanation of

empirical observation using linkage of measures across scale has to occur both from “top-

down” and “bottom-up” to be self-sufficient. Given the nesting of scales, and the

measurable relationship of information processing at one scale to another, dense sampling

of one scale of brain function will reflect processes at the other scales (see Figure 10).

Figure 16: This schematic illustrates one potential “top-down” approach to integrative

neuroscience, such as might to used for identifying the genes associated with a

susceptibility or resistance to addiction. Overlapping sampling of circuitry processing

reward/aversion input (cartoon in top-left) from families with addiction, could be used to

produce a systems biology map (cartoon top right) that identifies quantitative traits with a

demonstrated familiality, and little alteration with disease progression. These

endophenotypes could then be used in a multipoint genetic linkage analysis to

chromosomal loci (schematic in center/bottom). This “top-down” approach would define

disease susceptibility by continuous quantitative traits measured from systems biology (as

via neuroimaging), and might perform a total genome scan and a multipoint linkage

analysis using a variance component approach (for quantitative and potential qualitative

traits). Analysis of microsatellite repeats and SNP markers could then drive gene

identification.

Figure 17: (a) Attempts at a “top-down” approach to integrative neuroscience have

frequently started from the delineation of behaviorally defined exophenotypes, which are

theoretically related to circuitry-based phenotypes (endophenotypes). Illness category can

stand in for any number of American Psychiatric Associations Diagnostic Statistical

Manual Axis I neuropsychiatric disorders, such as subtypes of major depressive disorder,

or cocaine abuse and dependence. Altered function in a distributed set of neural groups

(referred to in the figure as “circuits”) is symbolized by an asterisk after the circuit

number. This altered function may include diminished or increased circuitry activity, or

substitution of an alternative circuitry to fulfill a functional deficit. There may be a

39

number of altered functions or metric traits, determined by altered circuitry performance,

which determine a particular neuropsychiatric disorder. This is highly likely given the use

of multiple signs and symptoms currently used to define neuropsychiatric exophenotypes

using the American Psychiatric Associations Diagnostic Statistical Manual (APA, 1994).

Given the embedding of scales of brain function, “top-down” approaches starting from

continuous quantitative measures of systems biology, would, with the appropriate subject

sample size, have the potential to identify all polymorphic traits and temporal adaptations

for a behavioral varient. (b) “Bottom-up” approaches evaluate one genetic polymorphism

at a time to determine how it leads to an altered profile of circuitry function.

40

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