Self-perception and Experiential Schemata in the Addicted Brain

Rex Cannon Æ Joel Lubar Æ Debora Baldwin

� Springer Science+Business Media, LLC 2008

Abstract This study investigated neurophysiological

differences between recovering substance abusers (RSA)

and controls while electroencephalogram (EEG) was con-

tinuously recorded during completion of a new assessment

instrument. The participants consisted of 56 total subjects;

28 RSA and 28 non-clinical controls (C). The participants

completed the self-perception and experiential schemata

assessment (SPESA) and source localization was compared

utilizing standardized low-resolution electromagnetic

tomography (sLORETA). The data show significant dif-

ferences between groups during both the assessment

condition and baselines. A pattern of alpha activity as

estimated by sLORETA was shown in the right amygdala,

uncus, hippocampus, BA37, insular cortex and orbito-

frontal regions during the SPESA condition. This activity

possibly reflects a circuit related to negative perceptions of

self formed in specific neural pathways. These pathways

may be responsive to the alpha activity induced by many

substances by bringing the brain into synchrony if only for

a short time. In effect this may represent the euphoria

described by substance abusers.

Keywords Addiction � Self-perception �Neurophysiological assessment � EEG biofeedback �LORETA

Introduction

Substance use disorders (SUD) continue to exact sub-

stantial costs on society, families and the individual alike

and are the most frequently diagnosed disorders in the

Diagnostic and Statistical Manual of Mental Disorders

(DSM-IV-TR) (APA 2000). SUD continue to create chal-

lenges for both researchers and clinicians due to the high

incidence of recidivism (relapse) and the many components

involved; including, biological, neurophysiological, socio-

economic, cultural and psychological elements. It is

suggested that heritability plays an important role in SUD

(Blum et al. 2007); however, to what extent environment,

perception and synaptic permanency or plasticity influence

the course of genetic adaptation or development of mal-

adaptive self-image requires further investigation.

To date many of the most utilized assessment instru-

ments for SUD focus on information relating to substance

abuse or personality mechanisms (Alterman et al. 2007;

Bryer et al. 1990; Cannon et al. 1990; Gallucci 1997;

McDermott et al. 1996; Rouse et al. 1999; Stein et al.

1999); however, no current instrument provides neuro-

physiological evidence of the construct being measured.

Assessment construction involves careful consideration of

the theoretical construct to be measured and operational

definitions of components to be scaled with the consider-

ation that the final version is dependent on how well the

final data complement and confirm the theoretical construct

(Cronbach and Meehl 1955). It is important that the

question of self identity be addressed before any type of

social (Matto et al. 2007) or family identity can be oper-

ationalized with meaning. Evidence demonstrates that

attachment and interactions between parents and child play

a significant role in normal development; alternatively,

impaired parental bonding appears to be a major risk factor

R. Cannon (&) � J. Lubar � D. Baldwin

Brain Research and Neuropsychology Laboratory,

Department of Psychology, University of Tennessee

at Knoxville, Austin Peay Bldg, Suite 312, Knoxville,

TN 37996, USA

e-mail: rcannon2@utk.edu

123

Appl Psychophysiol Biofeedback

DOI 10.1007/s10484-008-9067-9

for development of mental illness, substance abuse and

possible substance dependence later in life (Canetti et al.

1997; Newcomb and Felix-Ortiz 1992; Petraitis et al. 1995;

Brook et al. 1989). Negative experiences in early child-

hood such as abuse and neglect are shown to be involved in

the development of obesity, cardiovascular diseases, and

other behavioral and psychological problems (McEwen

2000). It is within this context that this study and the

Self-Perception and Experiential Schemata Assessment

(SPESA) were formulated.

The SPESA is designed for sensitivity to negative,

average or positive perceptions of self, experiences and self-

in-experience in three life domains; childhood, adolescence

and adulthood. There are a total of 45 items, each with four

possible responses. The items are scored (2, 1, -1, -2). The

items are similar in content in dissimilar order for each

domain of development. The SPESA takes less than 10 min

to administer and 10 min to score. It provides important

insight into the perceptual, visceral, affective and cognitive

processes that may preclude the actual physical or psycho-

logical substance abuse or dependence. This instrument

divulges perceptual information regarding the endogenous

and exogenous experiences of the individual; including,

physical, sexual or emotional abuse, self-efficacy, self-

image, view of self in relation to family and peers, in

addition to perceptions of alienation and inadequacy. This

type of information is often obtained late in the typical

treatment regimen, if at all. Perception of self is an important

component for formulating a comprehensive understanding

of the client in order to produce effective treatment planning

and continued care monitoring. We utilize an operational

definition for the term SUD to apply to the misuse and abuse

of all mood altering chemicals with the hypothesis that

dissimilar substances may create different biological and

neurological consequences; however, there is some dis-

cernable neurophysiological commonality among the

population of recovering substance abusers (RSA) in this

study regardless of substance abused or drug of choice.

A recent review (Sokhadze et al. 2008) provides com-

prehensive information relating to EEG, event related

potentials (ERP) and EEG-biofeedback (neurofeedback) in

addiction research and treatment. EEG differences between

alcoholics and controls are shown in beta activity in frontal

and central regions of the cortex with beta 1 activity (12–

16 Hz) showing increased amplitude globally (Rangasw-

amy et al. 2002). Lower frontal alpha and slow-beta

coherence is shown in alcohol-dependent males and

females (Kaplan et al. 1985); in addition, individuals with

a family history of alcoholism show increased alpha

activity in posterior regions after alcohol consumption and

rate it more difficult to resist further drinking than controls

(Kaplan et al. 1988). Males at risk for alcoholism show

increased low-alpha EEG activity (7.5–10 Hz) after

ingesting alcohol as compared to males at low risk (Cohen

et al. 1993).

Michael et al. (1993) found higher central alpha and

slow-beta coherence in frontal and parietal electrodes in

relatives of alcoholics and lower parietal alpha and slow-

beta coherence in males with alcohol dependence. Notably,

other findings indicate that morphine, alcohol and mari-

juana increase alpha 2 power in the spectral EEG and relate

this to the euphoric state produced by the drugs (Lukas

1991, 1993; Lukas et al. 1995). Winterer and colleagues

(2003a, b) describe higher alpha and slow-beta coherence

in left-temporal regions and higher slow-beta coherence in

right temporal and frontal electrode pairs in alcohol-

dependent males and females. Moderate-to-heavy alcohol

consumption is associated with differences in synchroni-

zation of brain activity during rest (baseline) and mental

rehearsal with heavy drinkers displaying a loss of hemi-

spheric asymmetry of EEG synchronization in the alpha

and low-beta band. Contrarily, moderate to heavy drinking

males showed lower fast-beta band synchronization (De

Bruin et al. 2004, 2006). Elevated alpha power amplitude

is suggested to be a potential threat indicator for the

development of alcoholism and men with fathers having

alcohol use disorders are more likely to have high-voltage

alpha than men with unaffected fathers at baseline or after

receiving placebo (Ehlers and Schuckit 1988, 1990, 1991;

Ehlers et al. 1989). Based on the available research the

alpha and beta frequency domains appear to play a com-

plex role in addiction and may prove important in

formulating theories of neurophysiological antecedents to

SUD and the associated psychological and behavioral

processes.

Standardized low-resolution electromagnetic tomogra-

phy (sLORETA) is an inverse solution for estimating

cortical electrical current density originating from scalp

electrodes utilizing optimal smoothing in order to estimate

a direct 3D solution for the electrical activity distribution.

This method computes distributed electrical activity within

the cerebral volume, which is discretized and mapped onto

a dense grid array containing sources of electrical activity

at each point in the 3D grid. This methodology produces a

low error solution for source generators and provides sta-

tistical maps modeling distribution currents of brain

activity utilizing realistic electrode coordinates for a three-

concentric-shell spherical head model co-registered on a

standardized MRI atlas. This allows a reasonable approx-

imation of anatomical labeling within the neocortical

volume, including the anterior cingulate (AC) and hippo-

campus. LORETA and the standardized version are

accessible as freeware for research purposes and LORETA

is the only inverse solution developed for real-time neu-

rofeedback use. It is also demonstrated to estimate current

density sources efficiently with 19 electrodes (Congedo

Appl Psychophysiol Biofeedback

123

2003, 2006; Congedo et al. 2004; Lancaster et al. 1997,

2000; Lehmann et al. 2001, 2005, 2006; Pascual-Marqui

et al. 1994, 1999, 2002a, b, Pascual-Marqui 1999, 2002;

Talairach and Tournoux 1988; Towle et al. 1993).

SUD are multifaceted disorders that involve numerous

learning, social, experiential and cognitive components.

Smythies (1966) postulates that conditioned reflexes and

learning occur in the amygdala, the reticular formation and

other subcortical and limbic regions. EEG and functional

magnetic resonance imaging (fMRI) research demonstrat-

ing activity increase in the AC, amygdala, hippocampus,

uncinate gyrus, orbitofrontal (OFC) and inferior frontal

lobes during self-reference, reward acquisition, affective

tasks, decision making, emotional processing, autobio-

graphical memory and addictive disorders indicate a

complex role for these regions in numerous executive and

cognitive functions (Cabeza et al. 2004; Chambers et al.

2001; Dayas et al. 2007; De Witte 2004; del Olmo et al.

2006; Fossati et al. 2003, 2004; Gusnard 2005).

Social anxiety and other interpersonal difficulties are

posited to be increased risk factors for development of

SUD (Buckner et al. 2008). Individuals that have experi-

enced negative life events are more susceptible to alcohol

abuse (Brennan and Moos 1990). Moreover, individuals

that report childhood sexual abuse or other forms of

physical and emotional abuse are more likely to develop

SUD (Fergusson et al. 2008). Likewise, alcohol and other

drugs are suggested to buffer the stress and anxiety of life

events and temporarily improve self-esteem and lower

anxiety and depression levels (Josephs and Steele 1990).

Individuals with low self-esteem, negative self-efficacy,

negative self-image and a perceived lack of power or

control are more prone to use alcohol (Brett et al. 1990;

Bandura 1977; Holahan and Moos 1987). Furthermore,

data demonstrates deficits in executive functioning,

including, decision making and inhibitory processes among

the addicted population regardless of substance abused

(Lyvers and Yakimoff 2003; Wiers et al. 2005; De Bellis

et al. 2000; Grant and Dawson 1997; Tapert et al. 2004).

This concept is of primary interest concerning the behav-

ioral, social, impulsivity and decision making problems

that often plague addicted persons even after substantial

periods of abstinence.

We define experiential schemata (ES) as a neurologic

progression in human development involving a funda-

mental self-organization process. This process is based in

the formulation of concepts of self originating in percep-

tions of self (endogenous) formed through interactions with

others and the environment (exogenous). These encoded

schemata become the foundation for prevailing emotions,

motivations, attitudes, and attributions relating to self and

self-in-the-world that are maintained, reinforced and

entrenched in neural coding mechanisms formed through

dendritic arborization over the lifespan. The aforemen-

tioned behaviors and attributions have a possible etiology

in specific neurophysiological pathways involving regions

that are known to be involved in emotion, cognition, self-

reference and sensory processing and these pathways are

the direct result of dendritic pruning that occurs in early

development and progresses throughout adolescence into

adulthood. In normal development ES involving experi-

ences, behaviors, learning and organization of self are

engrained or reinforced in neural circuits with much of the

acquired information being necessary for social function-

ing, and overall survival in most circumstances. The

drawback to this process is that it can be extremely difficult

to introduce new concepts relating to self—identity to an

individual as well as novel learning material. There is an

extended period of time between infancy and adulthood in

which the environment contributes to the formation of

neural circuits through learning and experience (Anderton

2002; Gogate et al. 2001; Hyman and Malenka 2001;

Johnson 2001; Luczak 2006; van Pelt 1997).

The SPESA was developed with the intent to decrease

the gap that exists in relating neurologic functions with

perceptual, behavioral and psychological components or

experiential schemata. Based upon critical concepts from a

variety disciplines contributing to addiction research, we

propose that a common neurophysiological pattern exists in

RSA when evaluating self and self-in-experience that is

significantly different from non-clinical controls. Of par-

ticular interest to this study are the possible frequency

specific differences in cortical regions during both base-

lines and experimental condition electroencephalograms

(EEG). The cortical and subcortical regions identified in

addiction research offer reasonable insight into the linear,

often concretized cognitive patterns exhibited by addicted

individuals, in addition to the behavioral and decision

making difficulties exhibited by this population even after a

substantial period of abstinence from all mood altering

chemicals.

Methods

Participants

This study was conducted with a total of 56 participants.

Twenty-eight were in each group, RSA and controls (C).

The RSA group consisted of volunteers obtained from

regional Alcoholics and Narcotics Anonymous meetings.

All RSA members have completed at least one inpatient

treatment (28 days or longer) for chemical dependency and

received an Axis I diagnosis for chemical dependency from

medical professionals in the Veterans Administration or at

local chemical dependency treatment centers. The majority

Appl Psychophysiol Biofeedback

123

of the RSA group was classified with polysubstance, crack

cocaine and alcohol use disorders. The C group consisted

of participants obtained from the University of Tennessee

Human Research Participation pool who received extra

course credit for their participation. Exclusion criteria

consisted of previous head trauma, neurological or neuro-

vascular disease, or recent drug or alcohol use (2 weeks).

Because of the high comorbidity rate in SUD populations,

especially with respect to depression and anxiety, partici-

pants were excluded from the study only if they exhibited

psychosis, delusions or hallucinations. These were coded

and included in the analysis. All subjects read, signed and

agreed to protocol approved by the University Institutional

Review Board. The RSA groups’ mean time of abstinence

was calculated in days (384.11), range (14–1,825), SD

(459.46). Table 1 contains the demographic information

for the experimental groups.

Data Collection

Participants were prepared for EEG recording using a

measure of the distance between the nasion and inion to

determine the appropriate cap size for recording (Electro-

cap, Inc; Blom and Anneveldt 1982). The head was

measured and marked prior to EEG recording. The ears and

forehead were cleaned for recording with a mild abrasive

gel (Nuprep) to remove any oil and dirt from the skin. After

fitting the caps, each electrode site was injected with

electrogel and prepared so that impedances between indi-

vidual electrodes and each ear were \6 KX. The EEG

recording was conducted using the 19-leads standard

international 10/20 system (FP1, FP2, F3, F4, Fz, F7, F8,

C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1 and O2). The

data were collected and stored with a band pass set at 0.5–

64.0 Hz at a rate of 256 samples per second using the

Truscan acquisition system (Deymed Diagnostics). We

utilized linked ears and ground reference with 9 mm tin

cups. The Electrocap was also referenced at FPz. All

recordings were carried out in a comfortably lit, sound

attenuated room in the Neuropsychology and Brain

Research Laboratory at the University of Tennessee,

Knoxville. Lighting and temperature were held constant for

the duration of the experiment. Each session required

approximately 60 min to complete. Three-minute eyes

opened and eyes-closed baselines were collected for brain

imaging comparison. Subjects were then recorded while

completing the SPESA. The participant responses to each

item were marked within the EEG record. In addition, the

participants in this study provided a written record of their

mental processes employed during the procedures and also

completed a paper version of the SPESA. Table 2 shows

actual items for each domain in the SPESA.

Data Pre-processing

We extracted the item responses from the EEG record, with

4s on each side of the response marker. We then processed

the subsequent EEG condition and baseline data with

particular attention given to the frontal and temporal leads.

All episodic eye blinks, eye movements, teeth clenching,

jaw tension, body movements and possible EKG (Elec-

trocardiogram) were removed from the EEG stream.

Fourier cross-spectral matrices were computed and aver-

aged over 75% overlapping four-second artifact-free

epochs, which resulted in one cross-spectral matrix for

each subject and for each discrete frequency. The average

common reference is computed prior to the sLORETA

estimations.

Data Analysis

In order to assess the electrophysiological differences

between experimental conditions over the entire neo-cor-

tex, we conducted all voxel-by-voxel t-tests setting the

threshold to abs (t) = 4.0 utilizing a multiple hypothesis

testing procedure for the within subjects experimental

design (NovatechEEG), which adopts multiple compari-

sons procedures based on the t-max test using the step-

down version of the procedure with 5,000 random data

permutations. Randomization and Permutation tests are

Table 1 Demographics for EEG groups

Group Total l age SD M F RH LH

Addict 28 37.89 13.33 21 7 26 2

Control 28 21.21 5.07 14 14 27 1

Table 2 Items from the SPESA for each life domain

6: In childhood, with my family I felt

A: Like I belonged

B: I wanted to belong

C: I did not want to belong

D: Like I did not belong

27: In my teenage years, regarding my family, I felt

A: Like I belonged

B: Like I did not belong

C: I wanted to belong

D: I did not want to belong

37: In my adult relationships with family, I feel

A: Like I belong

B: Like I do not belong

C: I want to belong

D: I do not want to belong

Appl Psychophysiol Biofeedback

123

demonstrated to produce significant control of the family

wise error rate (Type I error) in multiple hypothesis testing

(Edgington 1987; Good 1993, 2005) and are frequently

utilized in neuroimaging studies (Blair and Karniski 1994;

Holmes et al. 1996; Westfall and Young 1993). Current

density for total relative power was extracted. The statis-

tical analysis was conducted using the Multiple Hypothesis

Testing (MhyT3!) software (Nova Tech EEG, Inc.). The

statistical differences within conditions were corrected for

all multiple comparisons using non-parametric analysis.

All data were normalized, log transformed and smoothed

with a 21 mm moving average filter (MAF 3-D) before

entering statistical analysis. The specific contrast between

conditions was entered into one sample t-test correspond-

ing to a corrected p \ .01. This method of analysis has

been utilized in studies by our lab and others (Cannon et al.

2004, 2007, 2008; Congedo et al. 2004; Papageorgiou

et al. 2007; Sherlin et al. 2006; Zumsteg et al. 2006). All

voxels were examined and statistical data were transformed

into sLORETA images. We analyzed the EEG data uti-

lizing the following frequency domains: Delta (0.5–

3.5 Hz); Theta (3.5–7.5 Hz); Alpha 1 (7.5–10.0 Hz); Alpha

2 (10.0–12.0 Hz); Beta 1 (12.0–18.0 Hz); Beta 2 (18.0–

21.0 Hz); Beta 3 (21.0–24.0 Hz); Beta 4 (24.0–28.0 Hz)

and Beta 5 (28.0–32.0 Hz). We also compared the obtained

assessment scores for significance using independent t-tests

assuming equal variance using SPSS 15.

Results

In the following subsections we describe the results for the

voxel by voxel comparisons between RSA and C groups

for the EEG SPESA, eyes-closed and eyes-opened base-

lines and for the obtained SPESA scores. The sLORETA

images are the result of voxel by voxel t-tests with a t-max

threshold set at (4.0). The images from left to right are

horizontal, saggital and axial views of the brain. The red in

the image indicates increased current source density

a B .01. The blue in the image indicates decreased current

source density a B .01. In the subsequent section we

address the results for the comparisons between the

obtained scores between groups, and the influence of

endorsed items within the assessment on the index scores.

SPESA Condition

Figures 1 and 2 show the significant differences between

RSA and C during the SPESA phase of the experiment.

Figure 1 shows the differences for the alpha 1 frequency

(7.5–10.0 Hz). The region (voxel) of maximum increase in

current density as estimated by sLORETA is at Talairach

coordinates (X = 4, Y = -11, Z = -6) Brodmann area

(BA) 37, parahippocampal gyrus, limbic lobe. Figure 2

shows the differences in the alpha 2 frequency. The region

(voxel) of maximum increase in current density as esti-

mated by sLORETA is at Talairach coordinates (X = 39,

Y = 3, Z = 15) BA 13 in the insular cortex of the right

temporal lobe. These are the only differences between the

groups shown by the statistical program for the SPESA

condition.

Eyes Opened and Eyes-Closed sLORETA Comparisons

Table 3 shows the results for the comparison between RSA

and C for the eyes-opened baseline recordings. In the table

from left to right are the frequency domain, the Talairach x,

y and z coordinates, Brodmann Area (BA), anatomical

label and hemisphere-location. Maximum increase in cur-

rent source density did not meet statistical criteria

(t [ 4.0), thus only voxels of maximal decrease in current

source density are shown in the table. Table 4 shows the

results for the comparison between RA and C for the eyes-

closed baselines. In the table from left to right are the

frequency domain and maximum/minimum voxel identi-

fied by sLORETA, Talairach coordinates, BA, anatomical

label and hemisphere-location.

Comparisons for Obtained Assessment Scores

Table 5 shows the results for the comparison for the

obtained SPESA scores between RSA and C. The table

contains subsections comparing coded responses A =

score comparisons between groups. The results show that

the mean scores for the RSA group differ from C in neg-

ative fashion at significant levels. Section B = (Abuse)

shows the results for those that endorsed abuse compared

to those that did not. Those that endorsed these items rate

all domains more negatively. Section C = (MD) those

reporting prior depression diagnosis. Those subjects

endorsing this item tend to rate all categories more nega-

tively. Those reporting a family history of use D = (FHU)

rate items more negatively except in the childhood domain;

those endorsing items relative to forlornness E = (F) (we

consider this to mean forsaken, despaired, wretched,

hopeless, abandoned, deprived, deserted and pitiful) rate all

domains more negatively; similarly, those endorsing items

of inadequacy F = (INA) (we consider this to mean

insufficient, defective or deficient, failing or lacking, not

measuring up, less than and useless) rate all domains more

negatively. For these categories 44.6% report a history of

abuse; 32.1% report a prior diagnosis of depression; 69%

report a history of use in family of origin; 33.9% report

positive for items relating to forlornness; and 50% report

positive for items relating to inadequacy. The reliability

analysis for this group of 56 show inter-item correlations

Appl Psychophysiol Biofeedback

123

(ICC) between domains tested (child/adol = .798), (child/

adult = .516), (adol/adult = .590). The results of the two-

way random effects model with a consistency definition

provide an intra-class correlation coefficient of .825 for

average measures.

Discussion and Conclusion

This is the first study of its kind to evaluate EEG patterns of

self-perception and experiential schemata in a group of

RSA as well as controls. The significant differences

between groups in the SPESA condition may provide

insight into a very probable neural pathway which stands to

be an idiosyncratic neurologic anomaly for the RSA pop-

ulation in this study, in addition to a possible antecedent to

SUD. The excess alpha activity in SUD when processing

perception of self and self-in experience may reflect a state

of desynchronization (or an idling fear and evaluator

response guided by maladaptive ES) within the individual,

given that alpha is generated in the thalamus (Lorincz et al.

2008) and is known to be involved in both attention and

memory processes (Cannon et al. in press-a). This alpha

excess possibly places demands on resources otherwise

employed for the homeostatic functioning of the individ-

ual; including, autonomic, perceptual, attentional, social,

cognitive and sensory processes. Notably, research dem-

onstrates the use of certain chemicals produces widespread

alpha power increases in the cortex (see introduction),

thereby, at least for this study group and their reports of

‘using’ and ‘drinking’ thought patterns, bringing the brain

into synchrony, if only for a very short period of time. We

Fig. 1 Shows the maximum

increase in current source

density between addict and

controls during the completion

of the SPESA in the alpha 1

frequency. The maximum

region of increase is (X = 4,

Y = -11, Z = -6) Brodmann

area 37, Parahippocampal

Gyrus, Limbic Lobe

Fig. 2 Shows the maximum increase in current source density

between addict and control during the completion of the SPESA. The

maximum region of increase in the alpha 2 frequency is at (X = 39,

Y = 3, Z = 15) Brodmann area 13, Insula, Sub-lobar. There is also

increased current density in the alpha 2 frequency in BA 13, BA 47,

BA 34 and BA 37, as well as the amygdaloid complex; including the

uncus, amygdala and hippocampus

Table 3 Eyes-opened comparison between RA and C

Eyes-opened resting condition

Frequency domain Talairach x, y, z, coordinates Brodmann area Anatomical label Hemisphere/location

Delta -38, 3, 36 9 Pre-central gyrus Left frontal lobe

Theta -45, -4, 43 6 Pre-central gyrus Left frontal lobe

Alpha 2 39, -81, 29 19 Superior occipital gyrus Right occipital lobe

Note: All voxels listed meet statistical criteria with t [ 4.0 and are significant B.01

Appl Psychophysiol Biofeedback

123

believe this to be the euphoria addicted individuals speak

of so fondly and is one possible reason for the difficulty in

treating these disorders in addition to the high relapse rates.

The excess alpha activity during the task is possibly

attributable to ES and the associated emotions relating to

internal and external conflict and confusion distinguishing

past from present and the brain’s reaction to re-experi-

encing the past. The regions shown active in the RSA

group are shown to be involved in learning and numerous

cognitive and affective processes. The hippocampus is

active during the processing of emotion, stress, autobio-

graphical memory, learning and long-term potentiation

(LTP) of stress (Diamond et al. 2007; Frey et al. 2007;

Joels et al. 2007). This function of LTP as it applies to

learning, memory and emotional consolidation lends sup-

port to our concept of ES, such that experiential

information is encoded with the internal perceptions and

emotions associated with the experience. The amygdala has

extensive projections throughout the brain and is active

during studies of appetite, sex, aggressivity, reward, deci-

sion making, aversion, emotion, memory (Chen et al.

2007; Cooke et al. 2007; Dardou et al. 2007; de Ruiter

et al. 2007; Evans et al. 2007; Friederich et al. 2007) and

addictive disorders (Adinoff 2004; Ahmed et al. 2005;

Andrzejewski et al. 2005; Bechara and Damasio 2002;

Cardinal et al. 2004; Carelli et al. 2003; Childress et al.

1999; Di Chiara et al. 1999). These limbic structures in

combination with thalamo-cortical-brain stem interactions

in combination with OFC, AC and insular cortices are

implicated in normal brain functions as well as psychiatric

disorders; including, schizophrenia (Lacerda et al. 2007;

Mata et al. 2008; McNamara et al. 2007; Shad et al. 2006;

Toro et al. 2006), depression, obsessive-compulsive and

anxiety disorders (Cardoner et al. 2007; Corya et al. 2006;

Drevets 2007; Toro and Deakin 2005) and more impor-

tantly to this research, personality (Fahim et al. 2007; New

Table 4 Eyes-closed

comparison between RA and C

Note: All voxels listed meet

statistical criteria with t [ 4.0

and are significant B.01

Eyes-closed resting condition

Frequency

domain

Talairach x, y, z,

coordinates

Brodmann

area

Anatomical

label

Hemisphere/

location

Delta

Max 60, -39, -27 20 Inferior temporal gyrus Right temporal lobe

Min -38, 3, 29 6 Pre-central gyrus Left frontal lobe

Theta

Max 25, 10, -34 38 Uncus Right limbic lobe

Min -38, 3, 29 6 Pre-central gyrus Left frontal lobe

Alpha 1

Max -17, 24, -20 47 Inferior frontal gyrus Left frontal lobe

Min -45, -60, 50 40 Inferior parietal lobe Left parietal lobe

Alpha 2

Max -10, 45, 1 32 Anterior cingulate Left limbic lobe

Min 46, -32, 22 13 Insula Right temporal lobe

Beta 1

Max -38, -18, -34 20 Inferior temporal gyrus Left temporal lobe

Min -45, -88, 8 19 Middle occipital gyrus Left occipital lobe

Beta 2

Max -24, -18, -20 28 Parahippocampal gyrus Left limbic lobe

Min 39, 3, 43 6 Middle frontal gyrus Right frontal lobe

Beta 3

Max -17, -11, -13 37 Parahippocampal gyrus Left limbic lobe

Min 39, 17, 29 9 Middle frontal gyrus Right frontal lobe

Beta 4

Max -24, -11, -13 AmC Amygdaloid Complex Right limbic lobe

Min 46, 38, 29 46 Middle frontal gyrus Right frontal lobe

Beta 5

Max -31, -11, -41 20 Uncus Right limbic lobe

Min 46, 31, 36 9 Middle frontal gyrus Right frontal lobe

Appl Psychophysiol Biofeedback

123

et al. 2007) and SUD (Goldstein et al. 2007a, b; Verdejo-

Garcia et al. 2007; Winstanley et al. 2007). The AC, OFC

and insular cortices are shown to be involved in decision

making, reward, learning, emotion and social processing

and are shown active during autobiographical memory and

processing of self; moreover, these regions have also been

shown to be susceptible to learning and self-regulation

using LORETA neurofeedback (Adinoff et al. 2006; Bush

et al. 2000; Cannon et al. 2006, 2007, 2008; Congedo

2003; Congedo et al. 2004; Devinsky et al. 1995; Ersche

et al. 2006; Eshel et al. 2007; Fleck et al. 2006; Frank and

Claus 2006; Krain et al. 2006; O’Doherty, 2007; Pais-

Vieira et al. 2007; Roesch et al. 2007; Sakagami and

Watanabe 2007; Verdejo-Garcia et al. 2007; Wallis 2007;

Windmann et al. 2006; Winstanley et al. 2007).

Levesque and colleagues (2004) conducted research

exploring emotional regulation in children and found that

sad film excerpts elicit activation of similar regions as this

study; namely, BA 9 and 10 in the left prefrontal cortex, as

well as the OFC, BA 11, right AC and BA 47 in right

frontal regions. Studies have proposed that emotional self-

regulation involves interactions of the mid-frontal AC with

the amygdala (Posner and Rothbart 1998) and PFC damage

in adults resembles normal reactions in children (Fox et al.

2001). The obtained data in the SPESA condition implicate

these limbic and frontal regions in emotion, perception of

self, attention to internal and external states and self-reg-

ulation in conjunction with the AC and OFC. Similar fMRI

studies of negative self-reference elicit activity in similar

posterior and central regions as this study (Bjork et al.

2007). The relationship between the amygdala and these

specific regions may in fact be a regulatory loop through

which ES relating to perception of self and self-in-experi-

ence and the associated emotional, memory, attentional and

decision making processes are functionally integrated

(Walton et al. 2007; Weiskopf et al. 2003). Efferent con-

nections from the amygdala project to extensive regions of

the brain, including the hypothalamus, basal forebrain,

etorhinal cortex, ventral striatum, cingulate gyrus, orbito-

frontal cortex, hippocampus, uncus and brainstem auto-

nomic centers (Davidson et al. 2000) that are also involved

in the acoustic startle reflex (Grillon 2007; Rosen et al.

1991). These amygdaloid efferents are suggested to per-

form an important role in early developmental sensory

processing and learning (Smythies 1966) in conjunction

with occipital, temporal and insular cortices from which

the amygdala projects and receives inputs (Malin et al.

2007). In essence self-organization, self-perception, social

and familial identity and self-definition may begin to form

very early in the developmental process.

Decision making processes and the ability to form a

resistance to drugs, i.e. the ability to say no, involves

Table 5 Comparison of the means for the obtained assessment scoresfor each category measured by the assessment and the effects on thetotal score and each subtest score, within the table are the comparisonbetween addict and control scores (A) and comparisons for coding ofitems measured by the assessment and their effects on the total scoreand index scores (B) = (AB) abuse, (C) = (MD) major depression;(D) = (FHU) family history of use; (E) = (FOR) forlornness anddespair; (F) = (IA) inadequacy. In each of the tables are the index,degrees of freedom, obtained t and probability of t

A

Score df t p

Total 54 -9.440 **

Child 54 -6.136 **

Adol 54 -6.474 **

Adult 54 -8.253 **

B

AB df t p

Total 54 -4.339 **

Child 54 -2.658 **

Adol 54 -3.909 **

Adult 54 -4.503 **

C

MD df t p

Total 54 -4.390 **

Child 54 -3.244 **

Adol 54 -4.346 **

Adult 54 -3.566 **

D

FHU df t p

Total 54 -2.447 *

Child 54 -1.711

Adol 54 -2.429 *

Adult 54 -2.241 *

E

FOR df t p

Total 54 -5.258 **

Child 54 -3.034 **

Adol 54 -4.232 **

Adult 54 -6.056 **

F

IA df t p

Total 54 -6.753 **

Child 54 -4.013 **

Adol 54 -4.448 **

Adult 54 -8.370 **

* significant at B.05; ** significant at B.01

Appl Psychophysiol Biofeedback

123

numerous brain regions; including, the insular, somato-

sensory, OFC, AC and dorsolateral prefrontal cortices, as

well as the amygdala, hippocampus and specific thalamic

nuclei (Everitt and Robbins 2005). Damasio (1994) dis-

cusses the continuous monitoring of the body by the brain

as specific content and images are processed, exacting not

only changes in brain electrical activity but also chemical

reactions. Thus, as the brain communicates and orches-

trates the affective state of the individual in response to

these contents and images relating to self and self-in-

experience; it is plausible that a large scale feedback loop

is formed involving not only perceptual processes but rel-

ative autonomic functioning. This process possibly

reinforces the addicted person to become habituated to an

aroused cortical state (i.e. increased alpha/beta activity)

and when there is a shift to ‘normalcy’ it is errantly per-

ceived as abnormal thereby increasing the desire or need

for a substance to return to the aroused (or perceived

normal) state.

A diagnosis of substance dependence requires that an

individual display an inability to control his or her drug-

seeking/using behavior despite adverse consequences.

These characteristics can be described as perseverative and

impulsive or possibly under the control of drug-associated

cues; additionally, these patterns of behavior are similar to

individuals with orbitofrontal lesions (Verdejo-Garcia

et al. 2006). This is possibly attributed to changes pro-

duced by drugs of abuse; however, it may also be that the

OFC, AC and the identified regions in the amygdaloid

complex have an attribute of synaptic permanency and are

less susceptible to plasticity effects and novel learning

relative to self and self-in-experience. There is a growing

body of fMRI literature investigating cortical regions

involved in reflective self-awareness in both normal and

clinical groups. Studies show metabolic increases during

these tasks in the AC, mesofrontal, precuneus, middle

frontal, temporal and parietal regions (Bjork et al. 2007,

2008; Gusnard et al. 2001; Gusnard 2005).

The EOB comparisons (Table 3) indicate the RSA show

significant deficits in delta, theta and upper alpha band in

left dorsolateral prefrontal cortex (BA 9 and 6) and right

occipital-parietal lobe (BA 19). BA 19 is considered a

tertiary visual processing region in conjunction with BA

18. It is shown to be active during confrontation naming

tasks (Abrahams et al. 2003) and decreased in activity

when subjects make decisions about favorite brands of

consumer goods (Deppe et al. 2005). BA 19 is active

during unimodal visual motion stimulation (Deutschlander

et al. 2002), stereodepth perceptions (Fortin et al. 2002),

‘‘theory of mind’’ tasks (Goel et al. 1995), and in memory

retrieval (Osipova et al. 2006). Two points of interest

regarding this region in relation to addiction are its inter-

actions with the hippocampus and right AC (Cannon et al.

in press-b). Connections within these regions have been

demonstrated in the study of the organization of pyramidal

cells in the hippocampus (Casanova et al. 2007). Neuro-

feedback techniques for SUD have shown positive results

in this region of the cortex with substantial improvement in

program retention and long-term abstinence rates (Peniston

and Kulkosky 1989, 1990, 1991; Scott and Kaiser 1998;

Scott et al. 2002, 2005). However, whole head EEG was

not monitored in these studies. The regions in the left

prefrontal cortex are shown to be involved in working

memory, disinhibition and cognitive processes (Cabeza

et al. 2003; Cannon et al. 2007, 2008; du Boisgueheneuc

et al. 2006) and in imitation behaviors (Heiser et al. 2003).

Studies show that mental state considerations explicitly

reflecting on aspects of one’s own mental state or attribu-

tions made about the mental states of others employ these

dorsal AC and dorsal medial prefrontal regions (Gusnard

et al. 2001; Gusnard 2005). Similarly, imaging studies

targeting retrieval of personal or episodic memories

involving verbal and nonverbal material (Cabeza and Ny-

berg 2000; Cabeza et al. 2003, 2004) implicate this same

prefrontal region. The deficits as estimated by sLORETA

may offer insight into the decrements in decision making

and executive processing exhibited by addicted persons, in

addition to difficulty making judgments and interpretations

specific to social context.

The eyes-closed baseline comparisons (Table 4) show a

greater number of differences between RSA and C in all

frequencies. For the RSA group the majority of regions

showing increased current source density are shown in the

left hemisphere, while the majority of deficits are shown in

the right hemisphere. The increase in activity in the left

amygdaloid complex; including BA 20, the uncus and other

limbic regions may very well indicate a disruption in the

default mode of brain function (Harrison et al. 2008). The

central core of this default mode is suggested to utilize

more resources than a functional task and involves regions

in posterior medial and parietal regions. The RSA group

showing significantly more activity during ECB might

demonstrate valuable resources being compromised or

exhausted from normal functional processes. Similarly, it

offers support to the idea of increased dopamine production

within limbic regions in addicted populations (Blum et al.

2007; Kohnke et al. 2003) as increased dopamine produc-

tion may be reflected in excitatory frequency domains. The

prefrontal and inferior frontal lobes in the right hemisphere

are shown to be active during facial recognition and

affective memory recall especially concerning the evalua-

tion of negative emotions (Cannon et al. 2004; Kim and

Hamann, 2007; Seger et al. 2004; Tsao and Livingstone

2008). The decrease in higher frequencies in right frontal

regions in the RSA possibly elucidates many of the social

and attentional difficulties attributable to this group as

Appl Psychophysiol Biofeedback

123

these regions are also shown to be involved in executive

attention (Cannon et al. 2007, in press-a).

The comparison between the obtained scores for the

assessment show significant differences between groups for

all indexes. The five items all appear to exact negative

effects on all index scores, except for family history of use

in the childhood index. These items compose two exoge-

nous factors and three endogenous factors which appear to

exact a significant effect on systems regulating perception

of self, experience and self-in-experience. Endorsement of

emotional, physical or sexual abuse produces effects on the

index scores in negative directions. Similarly, a prior

diagnosis of depression appears to influence the index

scores in a negative direction. Family history of use

appears to produce negative effects on the total, adolescent

and adult index scores; however, it produces no effects for

the childhood index score. Forlornness and inadequacy

both produce negative effects on all index scores. Thus the

overall trend is negative for the RSA group and this group

reports significantly more instances (real or imagined) of

abuse, neglect, inadequacy and forlornness than controls.

The SPESA comparisons reflect the neurophysiology of

this negative perceptual process as well as the emotional

content of this process. The SPESA is demonstrated to be a

reliable and consistent measure of ES and self-perception

over the lifespan.

Our future research directions involve increasing the

sample size to [200; obtaining a sample of adolescents

with SUD, and developing spatial specific neurofeedback

protocols designed to focus on the regions identified in this

study (Cannon et al. in press-b). We have developed an

innovative treatment model combining LORETA neuro-

feedback, standardized group therapy, substance abuse

assignments, social-self reintegration and specialized case

management (in progress). In our discussion we must take

into consideration the limitations of the methods we uti-

lized. When we refer to the respective regions identified in

the sLORETA comparisons, we are referring to regions

delimited by a number of discrete brain volume elements

(voxels) which may be displaced as much as 3–4 cm. from

the identified maximum or minimum; however, a recent

work has addressed this issue in depth (Congedo 2006).

Similarly, the RSA group consisted of an older and more

diverse population, with many having numerous years of

substance abuse issues and the associated legal, relation-

ship and personal consequences. The control group is

younger and the groups differ in many socioeconomic and

cultural contexts to some degree.

This is the first study of its kind to investigate EEG

frequency domain patterns and sLORETA source locali-

zation of specific brain regions involved in the perception

of self and self-in-experience. Additionally, the SPESA is

the first assessment instrument of its kind to demonstrate

neurologic patterns elicited by its content. The possibility

that substance abuse interacts with specific brain regions in

specific frequencies for specific time intervals appears to be

a valid concept, noting the paradox that the resulting self-

destruction and self-deprecation to achieve a desired state

or to change or alter an undesired state transcends imme-

diate comprehension. Recent advances in the EEG-

biofeedback (neurofeedback) method (Cannon et al. 2006,

2007, 2008; Congedo 2003, 2006; Congedo et al. 2004)

offer the possibility for the individual to influence the

identified frequency anomalies in limbic and cortical

regions which in turn may influence decreases in prob-

lematic behaviors associated with SUD even after years of

continued abstinence. Many of the individuals in the RSA

group with 3 or more years of continuous abstinence report

a consistent effort to intervene on their initial reactions to

external cues and seek additional outside interpretations

from counselors, peers or family members for suggestions

in how to deal with life events, rather than go on their first

instinct. Addiction is of particular interest to our research

with spatial specific neurofeedback training and we plan to

implement studies designed to employ this methodology

for training individuals to increase low-beta activity in the

right AC which may decrease this identified alpha activity

in limbic regions as well as increase delta, theta and alpha

in the left frontal and right occipital regions. The AC and

OFC may be primary regions of interest for determining if

it is possible for individuals to learn to increase or decrease

activity in these limbic regions (Cannon et al. in press-b).

This research study is an important step in the development

of treatment protocols that afford evidence based neuro-

physiological outcome measures, in addition to providing

important information about the specific functional rela-

tionship of these cortical regions in addictive disorders.

Acknowledgements The authors express sincere gratitude to the

subjects of this study for their effort and contribution to science. We

would like to thank NovatechEEG for the use of their software;

Deymed Diagnostics for the use of their Truscan Acquisition System

and Dr. Robert Thatcher for his contribution of Neuroguide to our lab

for research purposes. We also thank Dr. Greg Reynolds for his

editorial assistance and thorough review of the manuscript.

References

Abrahams, S., Goldstein, L. H., Simmons, A., Brammer, M. J.,

Williams, S. C., Giampietro, V. P., et al. (2003). Functional

magnetic resonance imaging of verbal fluency and confrontation

naming using compressed image acquisition to permit overt

responses. Human Brain Mapping, 20(1), 29–40. doi:

10.1002/hbm.10126.

Adinoff, B. (2004). Neurobiologic processes in drug reward and

addiction. Harvard Review of Psychiatry, 12(6), 305–320. doi:

10.1080/10673220490910844.

Adinoff, B., Williams, M., Best, S., Harris, T., Chandler, P., &

Devous, M. (2006). Sex differences in medial and lateral

Appl Psychophysiol Biofeedback

123

orbitofrontal cortex hypoperfusion in cocaine-dependent men

and women. Gender Medicine: Official Journal of the Partner-ship for Gender-Specific Medicine at Columbia University, 3(3),

206–222. doi:10.1016/S1550-8579(06)80209-3.

Ahmed, S. H., Lutjens, R., van der Stap, L. D., Lekic, D., Romano-

Spica, V., Morales, M., et al. (2005). Gene expression evidence

for remodeling of lateral hypothalamic circuitry in cocaine

addiction. Proceedings of the National Academy of Sciences ofthe United States of America, 102(32), 11533–11538. doi:

10.1073/pnas.0504438102.

Alterman, A., Cacciola, J., Habing, B., & Lynch, K. (2007). Addiction

severity index recent and lifetime summary indexes based om

nonparametric item response theory models. PsychologicalAssessment, 19(1), 119–132. doi:10.1037/1040-3590.19.1.119.

American Psychiatric Association. (2000). Diagnostic and statisticalmanual of mental disorders (4th ed.). [DSM-IV-TR], Text

revision. Washington, DC: Author.

Anderton, B. (2002). Ageing of the brain. Mechanisms of Ageingand Development, 123, 811–817. doi:10.1016/S0047-6374(01)

00426-2.

Andrzejewski, M. E., Spencer, R., & Kelley, A. (2005). Instrumental

learning, but not performance, requires dopamine D1-receptor

activation in the amygdala. Neuroscience, 135(2), 335–345. doi:

10.1016/j.neuroscience.2005.06.038.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of

behavioral change. Psychological Review, 84(2), 191–215. doi:

10.1037/0033-295X.84.2.191.

Bechara, A., & Damasio, H. (2002). Decision-making and addiction

(part I): Impaired activation of somatic states in substance

dependent individuals when pondering decisions with negative

future consequences. Neuropsychologia, 40(10), 1675–1689.

doi:10.1016/S0028-3932(02)00015-5.

Bjork, J. M., Momenan, R., Smith, A. R., & Hommer, D. W. (2008).

Reduced posterior mesofrontal cortex activation by risky rewards

in substance-dependent males. Drug and Alcohol Dependence,95, 115–128. doi:10.1016/j.drugalcdep.2007.12.014.

Bjork, J. M., Smith, A. R., Danube, C. L., & Hommer, D. W. (2007).

Developmental differences in posterior mesofrontal cortex

recruitment by risky rewards. The Journal of Neuroscience,27(18), 4839–4849. doi:10.1523/JNEUROSCI.5469-06.2007.

Blair, R. C., & Karniski, W. (1994). Distribution-free statistical

analyses of surface and volumetric maps. In R. W. Thatcher, M.

Hallett, E. R. John & M. Huerta (Eds.), Functional neuroimaging:Technical foundations (pp. 114–118). San Diego, CA: Academic.

Blom, J. L., & Anneveldt, M. (1982). An electrode cap tested.

Electroencephalography and Clinical Neurophysiology, 54,

591–594. doi:10.1016/0013-4694(82)90046-3.

Blum, K., Chen, T., Meshkin, B., Waite, R., Downs, B., Blum, S. H.,

et al. (2007). Manipulation of catechol-O-methyl-transferase

(COMT) activity to influence the attenuation of substance seeking

behavior, a subtype of reward deficiency syndrome (RDS), is

dependent upon gene polymorphisms: A hypothesis. MedicalHypotheses, 69, 1054–1060. doi:10.1016/j.mehy.2006.12.062.

Brennan, P. L., & Moos, R. H. (1990). Life stressors, social resources

and late-life problem drinking. Psychology and Aging, 5(4),

491–501. doi:10.1037/0882-7974.5.4.491.Brett, J. F., Brief, A. P., Burke, M. J., George, J. M., & Webster, J. (1990).

Negative affectivity and the reporting of stressful life events.

Health Psychology, 9(1), 57–69. doi:10.1037/0278-6133.9.1.57.

Brook, J. S., Whiteman, M., Gordon, A. S., & Cohen, P. (1989).

Changes in drug involvement: A longitudinal study of childhood

and adolescent determinants. Psychological Reports, 65(3 Pt 1),

707–726.

Bryer, J., Martines, K., & Dignan, M. (1990). Millon clinical

multiaxial inventory alcohol and drug abuse scales and the

identification of substance-abuse patients. Psychological

Assessment: A Journal of Counseling and Clinical Psychology,2(4), 438–441.

Buckner, J. D., Schmidt, N. B., Lang, A. R., Small, J. W., Schlauch,

R. C., & Lewinsohn, P. M. (2008). Specificity of social anxiety

disorder as a risk factor for alcohol and cannabis dependence.

Journal of Psychiatric Research, 42(3), 230–239. doi:10.1016/

j.jpsychires.2007.01.002.

Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional

influences in anterior cingulate cortex. Trends in CognitiveSciences, 4, 215–222. Review. doi:10.1016/S1364-6613(00)

01483-2.

Cabeza, R., Dolcos, F., Prince, S., Rice, H., Weissman, D., & Nyberg,

L. (2003). Attention-related activity during episodic memory

retrieval: A cross-function fMRI study. Neuropsychologia, 41,

390–399. doi:10.1016/S0028-3932(02)00170-7.

Cabeza, R., & Nyberg, L. (2000). Imaging cognition II. Journal ofCognitive Neuroscience, 12(1), 1–47. doi:10.1162/08989290

051137585.

Cabeza, R., Prince, S., Daselaar, S., Greenberg, D., Budde, M.,

Dolcos, F., et al. (2004). Brain activity during episodic retrieval

of autobiographical and laboratory events: An fMRI study using

a novel photo paradigm. Journal of Cognitive Neuroscience,16(9), 1583–1594. doi:10.1162/0898929042568578.

Canetti, L., Bachar, E., Galili-Weisstub, E., De Nour, A., & Shalev,

A. (1997). Parental bonding and mental health in adolescence.

Adolescence, 32(126), 381–394.

Cannon, D., Bell, W., Fowler, R., Penk, W., & Finkelstein, A. (1990).

MMPI differences between alcoholics and drug abusers: Effect

of age and race. Psychological Assessment: A Journal ofCounseling and Clinical Psychology, 2(1), 51–55.

Cannon, R., Congedo, M., & Lubar. J. (in press-a). Differentiating at

network of executive attention: LORETA neurofeedback in

anterior cingulate and dorsolateral prefrontal cortices: Mapping a

network of executive attention. The International Journal ofNeuroscience.

Cannon, R., Lubar, J., Clements, J. G., Harvey, E., & Baldwin, D.

(2008). Practical joking and cingulate cortex: A standardized low

resolution electromagnetic tomography (sLORETA) investiga-

tion of practical joking in the cerebral volume. Journal ofNeurotherapy, 11(4), 51–63.

Cannon, R., Lubar, J., Congedo, M., Thornton, K., Hutchens, T., &

Towler, K. (2007). The effects of neurofeedback in the cognitive

division of the anterior cingulate gyrus. The InternationalJournal of Neuroscience, 117(3), 337–357. doi:10.1080/00207

450500514003.

Cannon, R., Lubar, J., Gerke, A., Thornton, K., Hutchens, T., &

McCammon, V. (2006). Topographical coherence and absolute

power changes resulting from LORETA neurofeedback in the

anterior cingulate gyrus. Journal of Neurotherapy, 10(1), 5–31.

doi:10.1300/J184v10n01_02.

Cannon, R., Lubar, J., Sokhadze, E., & Baldwin, D. (in press-b).

LORETA neurofeedback for addiction and the possible neuro-

physiology of psychological processes influenced: A case study

and region of interest (ROI) analysis of LNFB in right anterior

cingulate cortex (ACC). Journal of Neurotherapy.Cannon, R., Lubar, J., Thornton, K., Wilson, S., & Congedo, M.

(2004). Limbic beta activation and LORETA: Can hippocampal

and related limbic activity be recorded and changes visualized in

an affective memory condition? Journal of Neurotherapy, 8(4),

5–24. doi:10.1300/J184v08n04_02.

Cardinal, R. N., Winstanley, C. A., Robbins, T. W., & Everitt, B. J.

(2004). Limbic corticostriatal systems and delayed reinforce-

ment. Annals of the New York Academy of Sciences, 1021, 33–

50. doi:10.1196/annals.1308.004.

Cardoner, N., Soriano-Mas, C., Pujol, J., Alonso, P., Harrison, B.,

et al. (2007). Brain structural correlates of depressive

Appl Psychophysiol Biofeedback

123

comorbidity in obsessive-compulsive disorder. NeuroImage,38(3), 413–421. doi:10.1016/j.neuroimage.2007.07.039.

Carelli, R. M., Williams, J., & Hollander, J. (2003). Basolateral

amygdala neurons encode cocaine self-administration and

cocaine-associated cues. The Journal of Neuroscience, 23(23),

8204–8211.

Casanova, M. F., Switala, A., & Trippe, J. (2007). A comparison

study of the vertical bias of pyramidal cells in the hippocampus

and neocortex. Developmental Neuroscience, 29(1–2), 193–200.

doi:10.1159/000096223.

Chambers, R. A., Krystal, J. H., & Self, D. W. (2001). A neurobio-

logical basis for substance abuse comorbidity in schizophrenia.

Biological Psychiatry, 50(2), 71–83. doi:10.1016/S0006-3223

(01)01134-9.

Chen, W., Tenney, J., Kulkarni, P., & King, J. (2007). Imaging

unconditioned fear response with manganese-enhanced MRI

(MEMRI). NeuroImage, 37(1), 221–229. doi:10.1016/j.

neuroimage.2007.05.001.

Childress, A. R., Mozley, P. D., McElgin, W., Fitzgerald, J., Reivich,

M., & O’Brien, C. P. (1999). Limbic activation during cue-

induced cocaine craving. The American Journal of Psychiatry,156(1), 11–18.

Cohen, H., Porjesz, B., & Begleiter, H. (1993). The effects of ethanol

on EEG activity in males at risk for alcoholism. Electroenceph-alography and Clinical Neurophysiology, 86, 368–376. doi:

10.1016/0013-4694(93)90132-F.

Congedo, M. (2003). Tomographic neurofeedback: A new techniquefor the self-regulation of brain electrical activity. An unpub-

lished dissertation, University of Tennessee, Knoxville.

Congedo, M. (2006). Subspace projection filters for real-time brain

electromagnetic imaging. IEEE Transactions on Bio-MedicalEngineering, 53(8), 1624–1634. doi:10.1109/TBME.2006.878055.

Congedo, M., Lubar, J., & Joffe, D. (2004). Low-resolution electro-

magnetic tomography neurofeedback. IEEE Transactions onNeuronal Systems and Rehabilitation Engineering, 12(4), 387–

397. doi:10.1109/TNSRE.2004.840492.

Cooke, B., Jordan, C., & Breedlove, S. (2007). Pubertal growth of the

medial amygdala delayed by short photoperiods in the Siberian

hamster, Phodopus sungorus. Hormones and Behavior, 52(3),

283–288. doi:10.1016/j.yhbeh.2007.04.008.

Corya, S. A., Williamson, D., Sanger, T., Briggs, S., Case, M., &

Tollefson, G. (2006). A randomized, double-blind comparison of

olanzapine/fluoxetine combination, olanzapine, fluoxetine, and

venlafaxine in treatment-resistant depression. Depression andAnxiety, 23(6), 364–372. doi:10.1002/da.20130.

Cronbach, L., & Meehl, P. (1955). Construct validity in psychological

tests. Psychological Bulletin, 56, 81–105.

Damasio, A. (1994). Descartes error: Emotion, reason and the humanbrain (pp. 127–169). New York: Avon Books Inc.

Dardou, D., Datiche, F., & Catterelli, M. (2007). Does taste or odor

activate the same brain networks after retrieval of taste

potentiated odor aversion? Neurobiology of Learning andMemory, 88(2), 186–197. doi:10.1016/j.nlm.2007.04.002.

Davidson, R., Jackson, D., & Kalin, N. (2000). Emotion, plasticity,

context, and regulation: Perspectives from affective neurosci-

ence. Psychological Bulletin, 126, 890–909. doi:10.1037/0033-

2909.126.6.890.

Dayas, C., Liu, X., Simms, J., & Weiss, F. (2007). Distinct patterns of

neural activation associated with ethanol seeking: Effects of

naltrexone. Biological Psychiatry, 61(8), 979–989. doi:

10.1016/j.biopsych.2006.07.034.

De Bellis, M. D., Clark, D. B., Beers, S. R., Soloff, P. H., Boring, A.

M., Hall, J., et al. (2000). Hippocampal volume in adolescent-

onset alcohol use disorders. The American Journal of Psychiatry,157(5), 737–744. doi:10.1176/appi.ajp.157.5.737.

De Bruin, E. A., Bijl, S., Stam, C. J., Bocker, B. E., Kenemans, J., &

Verbaten, M. N. (2004). Abnormal EEG synchronisation in

heavily drinking students. Clinical Neurophysiology, 115, 2048–

2055. doi:10.1016/j.clinph.2004.04.010.

De Bruin, E. A., Stam, C. J., Bijl, S., Verbaten, M. N., & Kenemans,

J. L. (2006). Moderate-to-heavy alcohol intake is associated with

differences in synchronization of brain activity during rest and

mental rehearsal. International Journal of Psychophysiology,60(3), 304–314. doi:10.1016/j.ijpsycho.2005.07.007.

del Olmo, N., Miguens, M., Higuera-Matas, A., Torres, I., Garcia-

Lecumberri, C., Solis, J. M., et al. (2006). Enhancement of

hippocampal long-term potentiation induced by cocaine self-

administration is maintained during the extinction of this behavior.

Brain Research, 1116(1), 120–126. doi:10.1016/j.brainres.2006.

07.001.

Deppe, M., Schwindt, W., Kugel, H., Plassmann, H., & Kenning, P.

(2005). Nonlinear responses within the medial prefrontal cortex

reveal when specific implicit information influences economic

decision making. Journal of Neuroimaging, 15(2), 171–182. doi:

10.1177/1051228405275074.

de Ruiter, M., Veltman, D., Phaf, R., & van Dycj, R. (2007). Negative

words enhance recognition in nonclinical high dissociators:

An fMRI study. NeuroImage, 37(1), 323–334. doi:10.1016/

j.neuroimage.2007.04.064.

Deutschlander, A., Bense, S., Stephan, T., Schwaiger, M., Brandt, T.,

& Dietrich, M. (2002). Sensory system interactions during

simultaneous vestibular and visual stimulation in PET. HumanBrain Mapping, 16(2), 92–103. doi:10.1002/hbm.10030.

Devinsky, O., Morrell, M., & Vogt, B. (1995). Review article:

Contributions of anterior cingulate cortex to behaviour. Brain,118, 279–306. doi:10.1093/brain/118.1.279.

De Witte, P. (2004). Imbalance between neuroexcitatory and neuroin-

hibitory amino acids causes craving for ethanol. AddictiveBehaviors, 29(7), 1325–1339. doi:10.1016/j.addbeh.2004.06.020.

Di Chiara, G., Tanda, G., Bassareo, V., Pontieri, F., Acquas, E., Fenu,

S., et al. (1999). Drug addiction as a disorder of associative

learning. Role of nucleus accumbens shell/extended amygdala

dopamine. Annals of the New York Academy of Sciences, 877,

461–485. doi:10.1111/j.1749-6632.1999.tb09283.x.

Diamond, D., Campbell, A., Park, C., Halonen, J., & Zoladz, P. (2007).

The temporal dynamics model of emotional memory processing:

A synthesis on the neurobiological basis of stress-induced

amnesia, flashbulb and traumatic memories, and the Yerkes-

Dodson law. Neural Plasticity, 2007, 1–33. doi:10.1155/NP .

Drevets, W. C. (2007). Orbitofrontal cortex function and structure in

depression. Annals of the New York Academy of Sciences, 1121,

499–527. doi:10.1196/annals.1401.029.

du Boisgueheneuc, F., Levy, R., Volle, E., Seassau, M., Duffau, H.,

Kinkingnehun, S., et al. (2006). Functions of the left superior

frontal gyrus in humans: A lesion study. Brain, 129(Pt 12),

3315–3328. doi:10.1093/brain/awl244.

Edgington, E. S. (1987). Randomization tests (2nd ed., pp. 74–97).

New York: Marcel Dekker, Inc.

Ehlers, C. L., & Schuckit, M. A. (1988). EEG response to ethanol in

sons of alcoholics. Psychopharmacology Bulletin, 24, 434–437.

Ehlers, C. L., & Schuckit, M. A. (1990). EEG fast frequency activity

in the sons of alcoholics. Biological Psychiatry, 27, 631–641.

doi:10.1016/0006-3223(90)90531-6.

Ehlers, C. L., & Schuckit, M. A. (1991). Evaluation of EEG alpha

activity in sons of alcoholics. Neuropsychopharmacology, 4,

199–205.

Ehlers, C. L., Wall, T. L., & Schuckit, M. A. (1989). EEG spectral

characteristics following ethanol administration in young men.

Electroencephalography and Clinical Neurophysiology, 7, 179–

187. doi:10.1016/0013-4694(89)90118-1.

Appl Psychophysiol Biofeedback

123

Ersche, K., Fletcher, P., Roiser, J., Fryer, T., London, M., Robbins, T.

W., et al. (2006). Differences in orbitofrontal activation during

decision-making between methadone-maintained opiate users,

heroin users and healthy volunteers. Psychopharmacology,188(3), 364–373. doi:10.1007/s00213-006-0515-z.

Eshel, N., Nelson, E., Blair, R., Pine, D., & Ernst, M. (2007). Neural

substrates of choice selection in adults and adolescents: Devel-

opment of the ventrolateral prefrontal and anterior cingulate

cortices. Neuropsychologia, 45(6), 1270–1279. doi:10.1016/

j.neuropsychologia.2006.10.004.

Evans, K., Wright, C., Wedig, M., Gold, A., Pollack, M., & Rauch, S.

(2007). A functional MRI study of amygdala responses to angry

schematic faces in social anxiety disorder. Depression andAnxiety, 25(6), 496–505. doi:10.1002/da.20347.

Everitt, B. J., & Robbins, T. W. (2005). Neural systems of reinforce-

ment for drug addiction: From actions to habits to compulsions.

Nature Neuroscience, 8(11), 1481–1489. doi:10.1038/nn1579.

Fahim, C., Stip, E., Mancini-Marie, A., Potvin, S., & Malaspina, D.

(2007). Orbitofrontal dysfunction in a monozygotic twin discor-

dant for postpartum affective psychosis: A functional magnetic

resonance imaging study. Bipolar Disorders, 9(5), 541–545. doi:

10.1111/j.1399-5618.2007.00404.x.

Fergusson, D. M., Boden, J. M., & Horwood, L. J. (2008). Exposure

to childhood sexual and physical abuse and adjustment in early

adulthood. Child Abuse and Neglect, 32(6), 607–619. doi:

10.1016/j.chiabu.2006.12.018.

Fleck, M., Daselaar, S., Dobbins, I., & Cabeza, R. (2006). Role of

prefrontal and anterior cingulate regions in decision-making

processes shared by memory and nonmemory tasks. CerebralCortex (New York, N.Y.), 16(11), 1623–1630. doi:10.1093/cercor/

bhj097.

Fortin, A., Ptito, A., Faubert, J., & Ptito, M. (2002). Cortical areas

mediating stereopsis in the human brain: A PET study. NeuroRe-port, 13(6), 895–898. doi:10.1097/00001756-200205070-00032.

Fossati, A., Feeney, J. A., Donati, D., Donini, M., Novella, L.,

Bagnato, M., et al. (2003). Personality disorders and adult

attachment dimensions in a mixed psychiatric sample: A

multivariate study. The Journal of Nervous and Mental Disease,191(1), 30–37. doi:10.1097/00005053-200301000-00006.

Fossati, P., Radtchenko, A., & Boyer, P. (2004). Neuroplasticity: From

MRI to depressive symptoms. European Neuropsychopharma-cology, 14(Suppl 5), S503–S510. doi:10.1016/j.euroneuro.2004.

09.001.

Fox, N. A., Henderson, H. A., & Marshall, P. J. (2001). The biology

of temperament: An integrative approach. In C. A. Nelson & M.

Luciana (Eds.), Handbook of developmental cognitive neurosci-ence (pp. 631–645). Cambridge: MIT Press.

Frank, M. J., & Claus, E. D. (2006). Anatomy of a decision: Striato-

orbitofrontal interactions in reinforcement learning, decision

making, and reversal. Psychological Review, 113(2), 300–326.

doi:10.1037/0033-295X.113.2.300.

Frey, B., Andreazza, A., Nery, F., Martins, M., Quevedo, J., Soares, J.

C., et al. (2007). The role of hippocampus in the pathophysi-

ology of bipolar disorder. Behavioural Pharmacology, 18(5–6),

419–430. doi:10.1097/FBP.0b013e3282df3cde.

Friederich, H. C., Uher, R., Brooks, S., Gianpietro, V., Brammer, M.,

Williams, S. C., et al. (2007). I’m not as slim as that girl: Neural

bases of body shape self-comparison to media images. Neuro-Image, 37(2), 674–681. doi:10.1016/j.neuroimage.2007.05.039.

Gallucci, N. (1997). On the identification of patterns of substance

abuse with the MMPI-A. Psychological Assessment, 9(3), 224–

232. doi:10.1037/1040-3590.9.3.224.

Goel, V., Grafman, J., Sadato, N., & Hallett, M. (1995). Modeling

other minds. NeuroReport, 6(13), 1741–1746. doi:10.1097/

00001756-199509000-00009.

Gogate, N., Geidd, J., Janson, K., & Rapport, J. L. (2001). Brain

imaging in normal and abnormal brain development: New

perspectives for child psychiatry. Clinical NeuroscienceResearch, 1, 283–290. doi:10.1016/S1566-2772(01)00014-7.

Goldstein, R., Tomasi, D., Alia-Klein, N., Cottone, L., Zhang, L.,

Telang, F., et al. (2007a). Subjective sensitivity to monetary

gradients is associated with frontolimbic activation to reward in

cocaine abusers. Drug and Alcohol Dependence, 87(2–3), 233–

240. doi:10.1016/j.drugalcdep.2006.08.022.

Goldstein, R., Tomasi, D., Rajaram, S., Cottone, L., Zhang, L.,

Maloney, T., et al. (2007b). Role of the anterior cingulate and

medial orbitofrontal cortex in processing drug cues in cocaine

addiction. Neuroscience, 144(4), 1153–1159. doi:10.1016/j.

neuroscience.2006.11.024.

Good, P. (1993). Permutation tests: A practical guide to resamplingmethods for testing hypotheses (pp. 24–45). New York:

Springer-Verlag.

Good, P. (2005). Permutation, parametric, and bootstrap tests ofhypotheses (3rd ed., pp. 79–140). Springer Series in Statistics.

Springer: New York

Grant, B. F., & Dawson, D. A. (1997). Age at onset of alcohol use and

its association with DSM-IV alcohol abuse and dependence:

Results from the National Longitudinal Alcohol Epidemiologic

Survey. Journal of Substance Abuse, 9, 103–110. doi:10.1016/

S0899-3289(97)90009-2.

Grillon, C. (2007). Models and mechanisms of anxiety: Evidence

from startle studies. Psychopharmacology, 199(3), 421–437. doi:

10.1007/s00213-007-1019-1.

Gusnard, D. A. (2005). Being a self: Considerations from functional

imaging. Consciousness and Cognition, 14(4), 679–697. doi:

10.1016/j.concog.2005.04.004.

Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E.

(2001). Medial prefrontal cortex and self-referential mental

activity: Relation to a default mode of brain function. Proceed-ings of the National Academy of Sciences of the United States ofAmerica, 98(7), 4259–4264. doi:10.1073/pnas.071043098.

Harrison, B. J., Pujol, J., Lopez-Sola, M., Hernandez-Ribas, R., Deus,

J., Ortiz, H., et al. (2008). Consistency and functional special-

ization in the default mode brain network. Proceedings of theNational Academy of Sciences of the United States of America,105(28), 9781–9786. doi:10.1073/pnas.0711791105.

Heiser, M., Iacoboni, M., Maeda, F., Marcus, J., & Mazziotta, J.

(2003). The essential role of Broca’s area in imitation. TheEuropean Journal of Neuroscience, 17(5), 1123–1128. doi:

10.1046/j.1460-9568.2003.02530.x.

Holahan, C. J., & Moos, R. H. (1987). Personal and contextual

determinants of coping strategies. Journal of Personality andSocial Psychology, 52(5), 946–955. doi:10.1037/0022-3514.

52.5.946.

Holmes, A. P., Blair, R. C., Watson, J. D. G., & Ford, I. (1996).

Nonparametric analysis of statistic images from functional

mapping experiments. Journal of Cerebral Blood Flow andMetabolism, 16, 7–22. doi:10.1097/00004647-199601000-00002.

Hyman, S. E., & Malenka, R. C. (2001). Addiction and the brain: The

neurobiology of compulsion and its persistence. Nature Reviews.Neuroscience, 2(10), 695–703. doi:10.1038/35094560.

Joels, M., Krugers, H., & Karst, H. (2007). Stress-induced changes in

hippocampal function. Progress in Brain Research, 167, 3–15.

doi:10.1016/S0079-6123(07)67001-0.

Johnson, M. (2001). Functional brain development in humans. NatureReviews. Neuroscience, 2, 475–483. doi:10.1038/35081509.

Josephs, R. A., & Steele, C. M. (1990). The two faces of alcohol

myopia: Attentional mediation of psychological stress. Journalof Abnormal Psychology, 99(2), 115–126. doi:10.1037/0021-

843X.99.2.115.

Appl Psychophysiol Biofeedback

123

Kaplan, R. F., Glueck, B. C., Hesselbrock, M. N., & Reed, H. B.

(1985). Power and coherence analysis of the EEG in hospitalized

alcoholics and nonalcoholic controls. Journal of Studies onAlcohol, 46, 122–127.

Kaplan, R., Hesselbrock, V., O’Connor, S., & Depalma, N. (1988).

Behavioral and EEG Responses to alcohol in nonalcoholic men

with a family history of alcoholism. Progress in Neuro-Pharmacology & Biological Psychiatry, 12, 873–885. doi:

10.1016/0278-5846(88)90083-8.

Kim, S. H., & Hamann, S. (2007). Neural correlates of positive and

negative emotion regulation. Journal of Cognitive Neuroscience,19(5), 776–798. doi:10.1162/jocn.2007.19.5.776.

Kohnke, M. D., Wiatr, G., Kolb, W., Kohnke, A. M., Schick, S., Lutz,

U., et al. (2003). Plasma homovanillic acid: A significant

association with alcoholism is independent of a functional

polymorphism of the human catechol-O-methyltransferase gene.

Neuropsychopharmacology, 28(5), 1004–1010.

Krain, A., Wilson, A., Arbuckle, R., Castellanos, F., & Milham, M.

(2006). Distinct neural mechanisms of risk and ambiguity: A

meta-analysis of decision-making. NeuroImage, 32(1), 477–484.

doi:10.1016/j.neuroimage.2006.02.047.

Lacerda, A. L., Hardan, A. Y., Yorbik, O., Vemulapalli, M., Prasad,

K. M., & Keshavan, M. S. (2007). Morphology of the

orbitofrontal cortex in first-episode schizophrenia: Relationship

with negative symptomatology. Progress in Neuro-Psychophar-macology and Biological Psychiatry, 31(2), 510–516. doi:

10.1016/j.pnpbp.2006.11.022.

Lancaster, J. L., Rainey, L. H., Summerlin, J. L., Freitas, C. S., Fox, P.

T., Evans, A. C., et al. (1997). Automated labeling of the human

brain: A preliminary report on the development and evaluation of a

forward-transform Method. Human Brain Mapping, 5, 238–242.

doi:10.1002/(SICI)1097-0193(1997)5:4\238::AID-HBM6[3.0.

CO;2-4.

Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas,

C. S., Rainey, L., et al. (2000). Automated Talairach atlas labels

for functional brain mapping. Human Brain Mapping, 10, 120–

131. doi:10.1002/1097-0193(200007)10:3\120::AID-HBM30[3.0.CO;2-8.

Lehmann, D., Faber, P. L., Achermann, P., Jeanmonod, D., Gianotti, L.

R., & Pizzagalli, D. (2001). Brain sources of EEG gamma

frequency during volitionally meditation-induced, altered states of

consciousness, and experience of the self. Psychiatry Research,108(2), 111–121. doi:10.1016/S0925-4927(01)00116-0.

Lehmann, D., Faber, P. L., Galderisi, S., Herrmann, W. M., Kinoshita,

T., Koukkou, M., et al. (2005). EEG microstate duration and

syntax in acute, medication-naive, first-episode schizophrenia: A

multi-center study. Psychiatry Research, 138(2), 141–156. doi:

10.1016/j.pscychresns.2004.05.007.

Lehmann, D., Faber, L., Gianotti, L., Kochi, K., & Pascual-Marqui, R.

(2006). Coherence and phase locking in the scalp EEG and

between LORETA model sources, and microstates as putative

mechanisms of brain temporo-spatial functional organization.

Journal of Physiology, Paris, 99(1), 29–36. doi:10.1016/j.

physparis.2005.06.005.

Levesque, J., Joanette, Y., Mensour, B., Beaudoin, G., Leroux, J. M.,

Bourgouin, P., et al. (2004). Neural basis of emotional self-

regulation in childhood. Neuroscience, 129(2), 361–369. doi:

10.1016/j.neuroscience.2004.07.032.

Lorincz, M. L., Crunelli, V., & Hughes, S. W. (2008). Cellular

dynamics of cholinergically induced alpha (8–13 Hz) rhythms in

sensory thalamic nuclei in vitro. The Journal of Neuroscience,28(3), 660–671. doi:10.1523/JNEUROSCI.4468-07.2008.

Luczak, A. (2006). Spatial embedding of neuronal stress modeled by

diffuse growth. Journal of Neuroscience Methods, 157, 132–141.

doi:10.1016/j.jneumeth.2006.03.024.

Lukas, S. E. (1991). Topographic mapping during cocaine-induced

intoxication and self-administration. In G. Racagni, N. Brunello

& T. Fukuda (Eds.), Biological psychiatry (Vol. 2, pp. 25–29).

New York: Elsevier Science Publishers.

Lukas, S. E. (1993). Advanced electrophysiological imaging tech-

niques for studying drug effects. In E. D. London (Ed.), Imagingdrug action in the brain, chapter 15 (pp. 389–404). Boca Raton,

FL: CRC Press.

Lukas, S. E., Mendelson, J. H., & Benedikt, R. (1995). Electroen-

cephalographic correlates of marihuana-induced euphoria. Drugand Alcohol Dependence, 37, 131–140. doi:10.1016/0376-8716

(94)01067-U.

Lyvers, M., & Yakimoff, M. (2003). Neuropsychological correlates of

opioid dependence and withdrawal. Addictive Behaviors, 28(3),

605–611. doi:10.1016/S0306-4603(01)00253-2.

Malin, E. L., Ibrahim, D. Y., Tu, J. W., & McGaugh, J. L. (2007).

Involvement of the rostral anterior cingulate cortex in consol-

idation of inhibitory avoidance memory: Interaction with the

basolateral amygdala. Neurobiology of Learning and Memory,87(2), 295–302. doi:10.1016/j.nlm.2006.09.004.

Mata, I., Rodriguez-Sanchez, J. M., Pelayo-Teran, J. M., Perez-

Iglesias, R., Gonzalez-Blanch, C., Ramirez-Bonilla, M., et al.

(2008). Cannabis abuse is associated with decision-making

impairment among first-episode patients with schizophrenia-

spectrum psychosis. Psychological Medicine, 38(9), 1257–1266.

doi:10.1017/S0033291707002218.

Matto, H., Miller, K., & Spera, C. (2007). Examining the relative

importance of social context referents in predicting intention

to change substance abuse behavior using the EASE. AddictiveBehaviors, 32(9), 1826–1834. doi:10.1016/j.addbeh.2006.12.

015.

McDermott, P., Alterman, A., Brown, L., Zaballero, A., Snider, E., &

McKay, J. (1996). Construct refinement and confirmation for the

addiction severity index. Psychological Assessment, 8(2), 182–

189. doi:10.1037/1040-3590.8.2.182.

McEwen, B. (2000). The neurobiology of stress: From serendipity to

clinical relevance. Brain Research, 886(1–2), 172–189. doi:

10.1016/S0006-8993(00)02950-4.

McNamara, R. K., Jandacek, R., Rider, T., Tso, P., Hahn, C. G.,

Richtand, N. M., et al. (2007). Abnormalities in the fatty acid

composition of the postmortem orbitofrontal cortex of schizo-

phrenic patients: Gender differences and partial normalization

with antipsychotic medications. Schizophrenia Research, 91(1–

3), 37–50. doi:10.1016/j.schres.2006.11.027.

Michael, A., Mirza, K. A., Mukundan, C., & Channabasavanna, S. M.

(1993). Interhemispheric electroencephalographic coherence as a

biological marker in alcoholism. Acta Psychiatrica Scandinav-ica, 87, 213–217. doi:10.1111/j.1600-0447.1993.tb03358.x.

New, A. S., Hazlett, E. A., Buchsbaum, M. S., Goodman, M., Mitelman,

S. A., Newmark, R., et al. (2007). Amygdala-prefrontal discon-

nection in borderline personality disorder. Neuropsychophar-macology, 32(7), 1629–1640. doi:10.1038/sj.npp.1301283.

Newcomb, M., & Felix-Ortiz, M. (1992). Multiple protective and risk

factors for drug use and abuse: Cross-sectional and prospective

findings. Journal of Personality and Social Psychology, 63(2),

280–296. doi:10.1037/0022-3514.63.2.280.

O’Doherty, J. P. (2007). Lights, Camembert, Action! The role of

human orbitofrontal cortex in encoding stimuli, rewards and

choices. Annals of the New York Academy of Sciences, 1121,

254–272. doi:10.1196/annals.1401.036.

Osipova, D., Takashima, A., Oostenveld, R., Fernandez, G., Maris, E.,

& Jensen, O. (2006). Theta and gamma oscillations predict

encoding and retrieval of declarative memory. The Journal ofNeuroscience, 26(28), 7523–7531. doi:10.1523/JNEUROSCI.

1948-06.2006.

Appl Psychophysiol Biofeedback

123

Pais-Vieira, M., Lima, D., & Galhardo, V. (2007). Orbitofrontal

cortex lesions disrupt risk assessment in a novel serial decision-

making task for rats. Neuroscience, 145(1), 225–231. doi:

10.1016/j.neuroscience.2006.11.058.

Papageorgiou, C. C., Sfagos, C., Kosma, K. K., Kontoangelos, K. A.,

Triantafyllou, N., Vassilopoulos, D., et al. (2007). Changes in

loreta and conventional patterns of p600 after steroid treatment

in multiple sclerosis patients. Progress in Neuro-Psychophar-macology and Biological Psychiatry, 31, 234–241. doi:

10.1016/j.pnpbp.2006.07.005.

Pascual-Marqui, R. D. (1999). Review of methods for solving the

EEG inverse problem. International Journal of Bioelectromag-netism, 1, 75–86.

Pascual-Marqui, R. D. (2002). Standardized low resolution brain

electromagnetic tomography (sLORETA): Technical details.

Methods and Findings in Experimental and Clinical Pharma-cology, 24D, 5–12.

Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehmann, D.

(2002a). Functional imaging with low-resolution brain electro-

magnetic tomography (LORETA): A review. Methods andFindings in Experimental and Clinical Pharmacology, 24(Suppl

C), 91–95.

Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehmann, D.

(2002b). Functional imaging with low resolution brain electro-

magnetic tomography (LORETA): A review. Methods andFindings in Experimental and Clinical Pharmacology, 24C,

91–95.

Pascual-Marqui, R. D., Lehmann, D., Koenig, T., Kochi, K., Merlo,

M. C., Hell, D., et al. (1999). Low resolution brain electromag-

netic tomography (LORETA) functional imaging in acute,

neuroleptic-naive, first-episode, productive schizophrenia. Psy-chiatry Research, 90(3), 169–179. doi:10.1016/S0925-4927(99)

00013-X.

Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low

resolution electromagnetic tomography: A new method for

localizing electrical activity in the brain. International Journalof Psychophysiology, 18, 49–65. doi:10.1016/0167-8760(84)

90014-X.

Peniston, E. G., & Kulkosky, P. J. (1989). Alpha-theta brainwave

training and beta endorphin levels in alcoholics. Alcoholism,Clinical and Experimental Research, 13, 271–279. doi:

10.1111/j.1530-0277.1989.tb00325.x.

Peniston, E. G., & Kulkosky, P. J. (1990). Alcoholic personality and

alpha-theta brainwave training. Medical Psychotherapy, 2, 37–55.

Peniston, E. G., & Kulkosky, P. J. (1991). Alpha-theta brain wave

neurofeedback for Vietnam veterans with combat related post

traumatic stress disorder. Medical Psychotherapy, 4, 1–14.

Petraitis, J., Flay, B., & Miller, T. (1995). Reviewing theories of

adolescent substance use: Organizing pieces in the puzzle.

Psychological Bulletin, 117(1), 67–86. doi:10.1037/0033-2909.

117.1.67.

Posner, M. I., & Rothbart, M. K. (1998). Attention, self-regulation

and consciousness. Philosophical Transactions of the RoyalSociety B. Biological Sciences, 353, 1915–1927. doi:10.1098/

rstb.1998.0344.

Rangaswamy, M., Porjesz, B., Chorlian, D. B., Wang, K., Jones, K.

A., Bauer, L. O., et al. (2002). Beta power in the EEG of

alcoholics. Biological Psychiatry, 52(8), 831–842. doi:10.1016/

S0006-3223(02)01362-8.

Roesch, M., Calu, D., Burke, K., & Schoenbaum, G. (2007). Should I

stay or should I go? Transformation of time-discounted rewards in

orbitofrontal cortex and associated brain circuits. Annals of theNew York Academy of Sciences, 1104, 21–34. doi:10.1196/annals.

1390.001.

Rosen, B. R., Belliveau, J. W., Aronen, H. J., Kennedy, D.,

Buchbinder, B. R., Fischman, A., et al. (1991). Susceptibility

contrast imaging of cerebral blood volume: Human experience.

Magnetic Resonance in Medicine, 22(2), 293–299. doi:10.1002/

mrm.1910220227. discussion 300–293.

Rouse, S., Butcher, J., & Miller, B. (1999). Assessment of substance

abuse in psychotherapy clients: The effectiveness of the MMPI-2

substance abuse scales. Psychological Assessment, 11(1), 101–

107. doi:10.1037/1040-3590.11.1.101.

Sakagami, M., & Watanabe, M. (2007). Integration of cognitive and

motivational information in the primate lateral prefrontal cortex.

Annals of the New York Academy of Sciences, 1104, 89–107. doi:

10.1196/annals.1390.010.

Scott, W. C., Brod, T. M., Sideroff, S., Kaiser, D., & Sagan, M. (2002,

May 15). Type-specific EEG biofeedback improves residentialsubstance abuse treatment. Paper presented at American

Psychiatric Association Annual Meeting.

Scott, W., & Kaiser, D. (1998). Augmenting chemical dependency

treatment with neurofeedback training. Journal of Neurotherapy,3(1), 66.

Scott, W. C., Kaiser, D., Othmer, S., & Sideroff, S. I. (2005). Effects

of an EEG biofeedback protocol on a mixed substance abusing

population. The American Journal of Drug and Alcohol Abuse,31(3), 455–469. doi:10.1081/ADA-200056807.

Seger, C., Stone, M., & Keenan, J. (2004). Cortical activations during

judgments about the self and another person. Neuropsychologia,42(9), 1168–1177. doi:10.1016/j.neuropsychologia.2004.02.003.

Shad, M. U., Muddasani, S., & Keshavan, M. S. (2006). Prefrontal

subregions and dimensions of insight in first-episode schizo-

phrenia—A pilot study. Psychiatry Research, 146(1), 35–42.

doi:10.1016/j.pscychresns.2005.11.001.

Sherlin, L., Budzynski, T., Kogan-Budzynski, H., Congedo, M.,

Fischer, M. E., & Buchwald, D. (2006). Low-resolution brain

tomography (LORETA) of monozygotic twins disconcordant for

chronic fatigue syndrome. NeuroImage, 34(4), 1438–1442. doi:

10.1016/j.neuroimage.2006.11.007.

Smythies, J. R. (1966). The neurological foundations of psychiatry:An outline of the mechanisms of emotion, memory, learning andthe organization of behaviour, with particular regard to thelimbic system (pp. 40–102). Oxford: Blackwell Scientific.

Sokhadze, E. M., Cannon, R. L., & Trudeau, D. L. (2008). EEG

biofeedback as a treatment for substance use disorders: Review,

rating of efficacy, and recommendations for further research.

Applied Psychophysiology and Biofeedback, 33(1), 1–63. doi:

10.1007/s10484-007-9047-5.

Stein, L., Graham, J., Ben-Porath, Y., & McNulty, J. (1999). Using

the MMPI-2 to detect substance abuse in an outpatient mental

health setting. Psychological Assessment, 11(1), 94–100. doi:

10.1037/1040-3590.11.1.94.

Talairach, J., & Tournoux, P. (1988). Co-planar stereoaxic atlas ofthe human brain. New York: Theme Medical Publishers.

Tapert, S. F., Brown, G. G., Baratta, M. V., & Brown, S. A. (2004).

fMRI BOLD response to alcohol stimuli in alcohol dependent

young women. Addictive Behaviors, 29(1), 33–50. doi:

10.1016/j.addbeh.2003.07.003.

Toro, C., & Deakin, J. (2005). NMDA receptor subunit NRI and

postsynaptic protein PSD-95 in hippocampus and orbitofrontal

cortex in schizophrenia and mood disorder. SchizophreniaResearch, 80(2–3), 323–330. doi:10.1016/j.schres.2005.07.003.

Toro, C., Hallak, J., Dunham, J., & Deakin, J. (2006). Glial fibrillary

acidic protein and glutamine synthetase in subregions of prefrontal

cortex in schizophrenia and mood disorder. Neuroscience Letters,404(3), 276–281. doi:10.1016/j.neulet.2006.05.067.

Towle, V. L., Bolanos, J., Suarez, D., Tan, K., Grzeszczuk, R., Levin,

D. N., et al. (1993). The spatial location of EEG electrodes:

Locating the best fitting sphere relative to cortical anatomy.

Electroencephalography and Clinical Neurophysiology, 86, 1–6.

doi:10.1016/0013-4694(93)90061-Y.

Appl Psychophysiol Biofeedback

123

Tsao, D. Y., & Livingstone, M. S. (2008). Mechanisms of face

perception. Annual Review of Neuroscience, 31, 411–437. doi:

10.1146/annurev.neuro.30.051606.094238.

van Pelt, J. (1997). Effect of pruning on dendritic tree topology.

Journal of Theoretical Biology, 186, 17–32. doi:10.1006/jtbi.

1996.0341.

Verdejo-Garcia, A., Rivas-Perez, C., Lopez-Torrecillas, F., & Perez-

Garcia, M. (2006). Differential impact of severity of drug use on

frontal behavioral symptoms. Addictive Behaviors, 31(8), 1373–

1382. doi:10.1016/j.addbeh.2005.11.003.

Verdejo-Garcia, A., Rivas-Perez, C., Lopez-Vilar, R., & Perez-

Garcia, M. (2007). Strategic self-regulation, decision-making

and emotion processing in poly-substance abusers in their first

year of abstinence. Drug and Alcohol Dependence, 86(2–3),

139–146. doi:10.1016/j.drugalcdep.2006.05.024.

Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to

decision-making. Annual Review of Neuroscience, 30, 31–56.

doi:10.1146/annurev.neuro.30.051606.094334.

Walton, M., Croxson, P., Behrens, T., Kennerley, S., & Rushworth,

M. (2007). Adaptive decision making and value in the anterior

cingulate cortex. NeuroImage, 36(Suppl 2), T142–T154. doi:

10.1016/j.neuroimage.2007.03.029.

Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R.,

et al. (2003). Physiological self-regulation of regional brain

activity using real-time functional magnetic resonance imaging

(fMRI): Methodology and exemplary data. NeuroImage, 19(3),

577–586. doi:10.1016/S1053-8119(03)00145-9.

Westfall, P. H., & Young, S. S. (1993). Resampling based multipletesting. Examples and methods for p-values adjustment. New

York: Wiley & Sons.

Wiers, R. W., van de Luitgaarden, J., van den Wildenberg, E., &

Smulders, F. T. (2005). Challenging implicit and explicit alcohol-

related cognitions in young heavy drinkers. Addiction (Abingdon,England), 100(6), 806–819. doi:10.1111/j.1360-0443.2005.

01064.x.

Windmann, S., Kirsch, P., Mier, D., Stark, R., Walter, B., Gunturkun,

O., et al. (2006). On framing effects in decision making: Linking

lateral versus medial orbitofrontal cortex activation to choice

outcome processing. Journal of Cognitive Neuroscience, 18(7),

1198–1211. doi:10.1162/jocn.2006.18.7.1198.

Winstanley, C. A., La Plant, Q., Theobald, D. E., Green, T. A.,

Bachtell, R. K., Perrotti, L. I., et al. (2007). DeltaFosB induction

in orbitofrontal cortex mediates tolerance to cocaine-induced

cognitive dysfunction. The Journal of Neuroscience, 27(39),

10497–10507. doi:10.1523/JNEUROSCI.2566-07.2007.

Winterer, G., Enoch, M. A., White, K., Saylan, M., Coppola, R., &

Goldman, D. (2003a). EEG phenotype in alcoholism: Increased

coherence in the depressive subtype. Acta Psychiatrica Scandi-navica, 108, 51–60. doi:10.1034/j.1600-0447.2003.00060.x.

Winterer, G., Smolka, M., Samochowiec, J., Ziller, M., Mahlberg, R.,

Gallinat, J., et al. (2003b). Association of EEG coherence and an

exonic GABABR1 gene polymorphism. American Journal ofMedical Genetics, 117B, 51–56. doi:10.1002/ajmg.b.10031.

Zumsteg, D., Andrade, D. M., & Wennberg, R. A. (2006). Source

localization of small sharp spikes: Low resolution electromag-

netic tomography (loreta) reveals two distinct cortical sources.

Clinical Neurophysiology, 117, 1380–1387. doi:10.1016/

j.clinph.2006.02.019.

Appl Psychophysiol Biofeedback

123