Accumbens functional connectivity during reward mediates sensation seeking and substance use in at-risk youth

Title length: 96, limited to 100 characters

Authors:

Barbara J. Weiland1,2, Wai-Ying Wendy Yau1-3, Robert C. Welsh1, Robert A. Zucker1,2, Jon-Kar Zubieta1,3, Mary Heitzeg1,2

Institutions:

1Department of Psychiatry, 2Addiction Research Center, and 3 Molecular and Behavioral Neuroscience Institute, The University of Michigan, Ann Arbor, MI.

Corresponding Author:

Barbara J. Weiland, Ph.D.Department of Psychiatry and Addiction Research CenterUniversity of Michigan4250 Plymouth RdAnn Arbor, MI 48109Phone: 248-766-4806Fax: 734-232-0287Email: bweiland@umich.edu

Key Words: Adolescent, alcoholism, functional connectivity, nucleus accumbens, reward substance use.

Abstract: 261 words ------- limit 250Article: 4193 words ---------limit 4000 --- about 5 over nowFigures: 5Tables: 3Supplemental Tables: 3

Background: Alterations in fronto-striatal connectivity have been found in substance users suggesting reduced influence of cognitive regions on reward-salience regions. Furthermore, an imbalance between reward and control systems in youth may influence their engagement in risky behaviors, including substance use. Parental alcoholism and sensation seeking represent additional vulnerability factors. We hypothesized that individual differences in accumbens functional connectivity during reward anticipation would mediate relationships between sensation seeking and drinking and drug use (DDU) in youth with (FH+) and without (FH-) family history of alcoholism.

Methods: Seventy 18-22 year olds performed a modified monetary incentive delay task during functional magnetic resonance imaging (FH+:/FH-, n= 49:/21). Group differences in connectivity for incentive (reward/loss) vs. neutral conditions were evaluated with psychophysiological interaction (PPI) analysis, seeded in the nucleus accumbens (NAcc). Indirect effects of sensation seeking on DDU through striatal connectivity were tested for each group.

Results: NAcc connectivity with paracentral lobule/precuneus and sensoriomotor areas was decreased for FH- versus increased for FH+ during incentive anticipation. Task-related functional coupling between left NAcc and supplementary sensoriomotor area (SSMA), involved in both attention and motor networks, correlated negatively with sensation-seeking in FH-. In FH+, however, this correlation was positive and mediated the effect of sensation seeking on DUU.

Conclusions: These results suggest preexisting differences in striatal reward-related functional connectivity between low- and high-risk youth. Alterations in NAcc functional coupling with attention/motor regions appear to mediate the association between sensation seeking and substance use in those most at-risk. Atypical accumbens connectivity with attention/motor systems may extend beyond the hypothesized imbalance between reward and control systems influencing vulnerability for substance abuse.

Research on the neurobiology of substance abuse has provided evidence for the role of

the mesocorticolimbic dopamine system in positive reinforcement of drugs of abuse (1-3).

Reward processing is associated with dopaminergic projections from the midbrain to the ventral

striatum/nucleus accumbens (4). Firing of dopamine cells are linked to encoding of reward (5),

reward expectancy (6) and event salience (7, 8) suggesting reward circuitry modulates

motivation for reward procurement and facilitates consolidation of memory traces connected

with substance use (9). The ventral striatum also receives inputs from cortical areas and limbic

regions (10) which are involved in cognitive control through both learning and motivational

circuits (11).

Reward processing has been studied using functional connectivity, seeded from the

nucleus accumbens (NAcc), showing an extensive network, that includes insular and

orbitofrontal cortices, amygdala, hippocampus and midbrain regions in healthy young adults

(12). Further connectivity studies have evaluated relationships between reward and control

systems in substance dependent adults, and suggest increased saliency responses and less

prominent cognitive inhibitory influences in addictive states. For example, heroin users show

stronger resting state connectivity between the striatum and both cingulate and frontal regions

than control subjects (13). In heavy drinkers, correlations between prefrontal cortex, striatum

and ventral tegmental area suggested strong connectivity between mesocorticolimbic structures

during cue-elicited urges (14). Connectivity between these regions has also been investigated in

the context of eating behaviors, under the hypothesis that similar reward circuitry may be

involved with food intake behavior in obesity (15). In response to food cues, increased NAcc-

orbitofrontal connectivity was found in obese versus normal weight women (16).

Psychophysiological interaction (PPI) analysis in healthy subjects found that viewing appetizing

versus bland foods caused changes in connectivity between ventral striatum, amygdala, and

premotor cortex that correlated with external food sensitivity, a measure associated with risk for

obesity (17, 18), suggesting that ‘less efficient’ connectivity within the reward network

potentially influences development of addictive disorders (19). Together, these studies suggest

dynamic interactions within the reward and control networks that may be related to risk.

However, they do not clarify whether connectivity differences between groups represent

preexisting vulnerability, or are consequences of substance use or learned addiction-related

behaviors.

The transition years into early adulthood, ages 18-23, are a critical developmental period

relative to onset of substance use concomitant to developmental changes in neurotransmitter

activation and the brain’s patterns of function (20). Evidence suggests a developmental

imbalance, between an earlier maturing subcortical reward-related brain circuit compared to a

cortical control-related circuitry, may bias motivation toward immediate over long-term reward

(21-23) resulting in risky behaviors, including experimentation with substance use (24-26). This

cortical-subcortical imbalance may manifest in the high prevalence of substance abuse and

dependence during this age range (27)

Another significant risk factor for substance use disorders, is parental alcoholism (28)

with genetic influence accounting for 40-60% of the variance in substance abuse risk (reviewed

in (29, 30). Neurocognitive studies evaluating adolescents with a family history of alcoholism

(FH+) have provided evidence of disrupted ventral striatal functioning in at-risk individuals. For

example, during passive viewing of emotional stimuli, abnormal suppression of ventral striatal

activation was found in adolescents identified as vulnerable based on early drug and alcohol

involvement (31). Abnormal ventral striatal modulation was also found during response

inhibition in high-risk adolescents (32). As the direction of abnormal ventral striatal reactivity

varied between these domains, disrupted modulation appears contextually-driven in high-risk

populations.

Individual personality traits might provide additional insight to the role of the striatal

reward system in substance abuse risk. Sensation seeking is characterized by the desire for

intense and novel experiences (33) and linked with heavy alcohol use, early onset of substance

use and poly substance use (34, 35). Furthermore, NAcc response during anticipation of reward

has been positively correlated with sensation-seeking scores (36).

This study investigated the role of functional connectivity of reward-related circuitry in

vulnerability to substance abuse in transitioning young adults. Participants were recruited from

the Michigan Longitudinal Study (MLS), an ongoing, prospective community study of FH+

families and contrast nonalcoholic families (FH-) recruited from the same neighborhoods (37).

We used PPI to investigate how physiological connectivity is affected by psychological valence

(reward or loss compared to a neutral condition) using a monetary incentive delay task (MID).

We hypothesized that connectivity during incentive anticipation between the NAcc and other

regions associated with reward processing would differ by family history. We further

hypothesized that individual differences in connectivity during incentive expectation would

mediate the relationship between sensation seeking personality and levels of substance use in

high-risk youth.

METHODS AND MATERIALS

Participants

Participants were 70 right-handed young adults (46 males, 24 females), aged 18.0-22.3

years (mean 20.1 ± 1.3), recruited from the MLS, an ongoing, prospective community study of

families with parental alcoholism and contrast nonalcoholic families (37). Parental alcoholism

was based on DSM-IV criteria; detailed description regarding MLS recruitment and assessments

can be found elsewhere (37). During the 11-26 year period, subjects are assessed annually with

psychosocial measures. Forty-nine participants in the current study had one or both parents with

a lifetime history of alcoholism (FH+) and 21 participants had no parental history of alcoholism

(FH-). All participants were Caucasian.

Exclusionary criteria were: any neurological, acute, uncorrected or chronic medical

illness; any current or recent (within six months) treatment with centrally active medications,

including sedative hypnotics; and a history of psychosis or schizophrenia in first-degree

relatives. The presence of most Axis I psychiatric or developmental disorders was exclusionary.

However, externalizing disorders were not exclusionary as these may lie on a developmental

spectrum with alcoholism risk (38) namely conduct disorders, attention deficit/hyperactivity

disorder (ADHD), or prior substance use disorder (SUD) using DSM-IV criteria. Subject

characteristics are summarized in Table 1. Written informed consent, approved by the

University of Michigan Medical School Institutional Review Board, was obtained.

Measures

fMRI paradigm. Brain response during anticipation of incentive stimuli was probed in a fMRI

experiment using a modified MID task (39), see Figure 1. Each 6- second trial consisted of four

events: incentive cues (five conditions: $0.20 win, $5 win, $0.20 loss, $5 loss, $0 no change);

anticipation delay; variable duration target requiring a button press response to gain, or to avoid

loss, of money; feedback. Subjects were instructed to respond to neutral targets despite no

incentive value. Trials were presented contiguously in pseudorandom order in two 5-minute runs

of twenty trials/condition. Response target duration was calculated based on individual subject’s

reaction time during a practice session prior to scanning and calibrated for overall success rate of

approximately 60%. Participants were paid fixed participation rates plus additional money won

during task.

Drinking and drug use. The drinking and drug use variable (DDU) is a composite variable

derived from the Drinking and Drug History (DDHx) Form (40-42). Participants are were asked:

how many days/month they drank over the past 6 months and the 6 months prior; and on a day

when they are drinking, how many drinks they usually have in 24 hours over the same intervals

and used to calculate drink volume/month. Participants were coded: 0 (0 drinks, n=11), 1 (<10

drinks, n=10), 2 (10-30 drinks, n=7), 3 (31-50 drinks, n=7), 4 (51-100 drinks, n=5) and 5 (>100

drinks, n=2). Participants are were asked: How old were you when you first began to smoke at

least once/week; and how frequently have you smoked cigarettes during the past 30 days (how

many cigarettes/day)?. Packyears was calculated: 0.5*(# •(# of packs/ day •* # years smoked).

The multiplier (0.5) assumed participants smoked less when first starting to smoke. Participants

were coded: 0 (non-smoker, n=25), 1 (light smoker, <1 packyear, n=14) and 2 (regular smoker,

>1 packyear, n=5). Number of illicit drugs ever used was defined as total number of illicit drugs

participant ever reported using during annual assessments since age 11. Participants were coded:

0 (none, n=17), 1 (1, n=12), 2 (2-3, n=11) and 3 (>3, n=4). DDU was calculated for each

individual by summing codes for drink volume/month, packyears and number- illicit- drugs.

The possible range of scores: 0 to 10; actual range:0 to 9. A one-sample Kolmogorov-Smirnov

(KS) test found DDU normally distributed within each group (p’s>0.21).

Sensation seeking. The Multiple Affect Adjective Checklist (MAACL) (43) assessed Sensation

Seeking, Positive Affect, Anxiety, Depression, and Hostility. Sensation seeking scores were

normally distributed within each group (KS test, p’s>0.32).

fMRI data acquisition. Whole-brain blood oxygen level-dependent (BOLD) functional images

were acquired on a 3.0 Tesla GE Signa scanner (Milwaukee, WI) using T2*-weighted single-

shot combined spiral in/out sequences (44), parameters: repetition time (TR)=2000 ms, echo time

(TE)=30 ms, flip angle (FA)=90; field-of-view (FOV)=200 mm; matrix size=64x64; in plane

resolution=3.12x3.12 mm; slice thickness=4 mm. High resolution anatomical T1 scans were

obtained for spatial normalization. Motion was minimized with foam pads and emphasis on

importance of keeping still.

Data analysis

Demographic, psychometric and task measures. Independent t- or χ2-tests examined group

differences. Response time (RT) and success rate for each incentive condition were calculated

and found normally distributed (KS test, p’s>.17). Repeated-measures ANOVAs were

conducted for RT and success rate, separately, assessing performance differences between

groups: valence (win/loss) x amount ($0.20/$5). Post-hoc t-tests determined the source of any

differences. Pearson’s correlations evaluated relationships between variables.

Functional data preprocessing. Functional images were reconstructed using an iterative

algorithm (45, 46). Data were motion corrected using FSL4.0 (Analysis Group, FMRIB, Oxford,

UK) (47). Runs exceeding 2 mm translation or 2° rotation were excluded. Image processing and

statistical analysis used statistical parametric mapping (SPM2, Wellcome Institute of Cognitive

Neurology, London, UK). Functional images were spatially normalized to standard stereotactic

space as defined by the Montreal Neurological Institute. Images were spatially smoothed with a

6mm isotropic kernel.

Individual subject statistical maps. Individual analysis used implemented a general linear model

(GLM) with five regressors of interest (for each incentive condition) convolved with the

hemodynamic response function (HRF). Motion parameters were modeled as nuisance

regressors. Contrasts for anticipation of reward ($0.2 and $5.0 combined) minus neutral and

anticipation of loss (combined) minus neutral, were calculated.

NAcc Regions of Interest (ROIs). Anatomical 5 mm-diameter spherical masks for the NAcc

were created as specified in Bjork et al (2008b) (48) were created using MarsBaR (49). Figure 1

illustrates mask location: the ventromesial intersection of caudate and putamen (50). Visual

inspection of each dataset confirmed masks were accurately placed on the NAcc, repositioning

up to 1 mm ensured accurate placement. NAcc activations were extracted from each individual’s

contrast images using MarsBaR.

Individual and group functional connectivity analysis. Psychophysiological interaction (PPI) is

an exploratory analysis that determines regions whose time series of activation exhibit significant

covariance with the seed differently in two conditions, i.e. an incentive (reward or loss) versus

neutral condition. Regressing out the contribution of the seed ROI time series and that of the

experimental context, the interaction is the contribution-dependent change in regional responses

to the experimental factor, response to incentive anticipation (51). Based on a priori interest in

reward, NAcc ROIs seeded the PPI analysis. For each NAcc, the time-series of the first

eigenvariate from the primary model was extracted and deconvolved with the HRF (52) and

multiplied by a binary contrast vector for reward or loss anticipation vs. neutral. The product

term was then convolved with the HRF (51). PPI model regressors consisted of the product

interaction term, contrast vector and extracted time-series plus motion regressors from the

original design. Single subject contrasts for the first regressor (product interaction term) were

calculated for each valence for second-level analysis.

Whole brain one-sample t-tests in SPM used single subject PPI contrasts of both

incentive conditions to evaluate functional connectivity with right or left NAcc separately for

each FH group. Contrasts identified brain regions with positive and negative connectivity with

seed ROIs during incentive anticipation. Statistical significance was established at p<0.05,

corrected for multiple comparisons at the cluster level. Single subject contrasts were also used

for whole brain 2x2 ANOVAs in SPM with valence (reward, loss) and group (FH-, FH+) as

factors for each NAcc. To identify brain regions showing significant group differences in

functional coupling, two contrasts were created, FH+ minus FH- and FH- minus FH+, using the

significance threshold above. For identified clusters, incentive connectivity was extracted from

individual PPI maps.

Post-hoc analyses of connectivity included repeated-measures ANOVAs (valence x FH)

to confirm effect of FH on connectivity and correlations with psychometric measures, controlling

for RT, with significance established at p≤0.025 corrected for multiple comparisons. Fisher’s Z-

transformations determined differences in group correlations coefficients.

Model of Connectivity as Mediator of Substance Use. To test for an indirect effect of

connectivity, a bias-corrected bootstrapped mediation analysis used an SPSS macro (53) for each

FH group. The dependent variable was DDU, independent variable was sensation seeking score,

and mediator was incentive anticipatory connectivity change with NAcc. As our interest was in

FH effects, connectivity effect for reward and loss anticipation trials was linearly combined for

this analysis. A point estimate of the indirect effect was derived from the mean of n=5000

estimates and 95% confidence intervals computed using the 2.5% highest and lowest scores of

the empirical distribution. Indirect effects were considered as significant when the bias-

corrected and accelerated confidence interval did not include zero (53). Mediation was tested

for all ROIs identified in the PPI analysis.

RESULTS

Demographic and psychometric measures. Table 1 shows sample characteristics by FH,

showing no group differences by sex, age, age at assessment of psychometric measures, IQ or

substance abuse/dependence (p’s>0.41). The FH- group had a higher incidence of lifetime

history of depression (χ2=4.34, p=0.037); however this should be interpreted cautiously given the

small sample with diagnosis (n=2). There were no group difference for other disorders

(p’s>0.39) or in DDU, substance use subscales, or MAACL measures (p’s>0.59). No

correlations were found between sensation seeking and DDU for the entire sample or either

group (p’s>0.76).

Task performance and activation. Success rate for each condition did not differ between groups

(p’s>0.24). The group x valence x amount ANOVA revealed a significant effect of valence on

success rate (win>avoid loss; F1,68=5.0, p=0.028) and amount ($5>$0.20; F1,68=25.0, p<0.001),

but interactions between valence, amount and/or FH did not meet significance (p’s>0.13).

The group x valence x amount ANOVA revealed an effect of valence on RT (avoid loss>win;

F1,68=9.5, p=0.003) and amount ($0.20>$5; F1,68=4.6, p=0.036), but interaction between valence

and amount did not meet significance (p=0.068). A main effect of FH on RT approached

significance (F1,68=2.6, p=0.111) and post-hoc analysis revealed this was due to slower RTs in

FH+ to “win $5” (t=-2.2, p=0.030) and to “lose $0.20” (t=-2.0, p=0.049). There were no

interactions by group with valence or amount (p’s>0.17). There was a group x valence x amount

interaction (F1,68=6.7, p=0.012). Further group analyses controlled for RT. Table 2 presents a

summary of task performance.

Extracted task effect size for bilateral NAcc exhibited expected increase with amount.

Post-hoc t-tests revealed no group differences in task effect size for any condition (p’s>0.13), see

Figure 1 and Table 2.

PPI connectivity analysis. Whole brain t-tests revealed positive connectivity changes between

NAcc and occipital lobe in FH- and between NAcc and left thalamus in FH+. For FH-,

negative changes were found between bilateral NAcc and inferior parietal, paracentral and

precuneus regions (see Figure 2). FH+ had negative incentive coupling of NAcc with superior

temporal and occipital regions. Extended results, at more lenient voxel-level thresholds of

p<0.05 false-discovery-rate-corrected, voxel extent≥15, are available in Supplemental Tables 1

and 2.

The whole-brain ANOVA revealed group differences in NAcc incentive connectivity

with medial frontal and parietal cortices (FH+>FH-) and occipital regions (FH->FH+), see Table

3. For left and right NAcc, the former contrast found a peak cluster centered in the paracentral

lobule extending into the precuneus and supplemental motor area, mapping onto the

supplementary sensoriomotor area (SSMA) designated such based on electrical stimulation

studies (54, 55).

Extracted connectivity data were entered into post-hoc repeated-measures ANOVA

(valence, FH) with RT as covariate. A main effect of FH (all F67,2>5.7 , p’s<0.019) was

confirmed on connectivity change for all ROIs. There was a valence-by-FH interaction

(F67,2=10.8,p=0.002) with connectivity between right NAcc and right lingual gyrus; post-hoc t-

tests revealed that FH- had increased NAcc-lingual coupling for reward>loss, (t=3.16, p=0.005)

whereas FH+ did not show this effect (t=-1.83, p=0.07). There were no main effects of valence

(p’s>0.08) or interactions with FH (p’s>0.23) on connectivity with other ROIs.

Further post-hoc analyses revealed medial and parietal areas had positive functional

coupling changes with NAcc in FH+ as opposed to negative changes for FH-. In contrast,

occipital regions had positive incentive connectivity with NAcc in FH- and negative in FH+, see

Figure 3.

PPI ROI correlations with psychometric measures. Left NAcc-SSMA, incentive connectivity

and sensation seeking correlated positively in FH+ and negatively in FH-, (test between

correlation coefficients, z=-3.72, p=0.0002) and showed a trend with DDU for FH+, see Figure 3

and Supplemental Table 3. Correlations for other ROIs showed similar directional relationships

(Supplemental Table 3).

Test of mediation. The mediation model showed a significant indirect effect of left NAcc-SSMA

connectivity on the relationship between sensation seeking and DDU for FH+ subjects with the

bootstrapped bias-corrected estimate, see Figure 5. No significant effects were found for other

ROIs for either group (p’s>0.05).

DISCUSSION

We used a monetary incentive delay (MID) task to probe the reward network of young

adults hypothesizing that reward system connectivity would differ based on familial risk for

substance abuse and these differences would manifest in the relationship between sensation

seeking personality and levels of drinking and drug use (DDU). This is the first study to

demonstrate differences in anticipatory reward-related functional connectivity based on family

history using psychophysiological interaction (PPI) analysis seeded from the NAcc. We tested a

mediation model proposing that task-related coupling with the NAcc would mediate the effects

of sensation seeking on DDU. These analyses showed that left NAcc connectivity with

supplementary sensoriomotor area (SSMA) is a mediator for this effect in high-risk youth (FH+).

Striatal functional connectivity with the SSMA, as well as other frontal and parietal regions

including the paracentral lobule, precuneus and sensoriomotor areas, was not only significantly

different by group, but these couplings changed in opposite directions during task. The FH-

group demonstrated reductions in coupling between these structures during incentive anticipation

while FH+ had increased coupling potentially representing a heritable neurobiological difference

related to vulnerability for substance abuse.

Sensation seeking, specifically in adolescents, has been associated with risk for early

onset of substance use, use of multiple substances (34), and high levels of alcohol use (56, 57).

Here we report that sensation seeking scores, DDU, reward task performance and anticipatory

striatal activation were not significantly different by familial risk in young adults. However we

observed group differences in reward-related functional connectivity between the striatum and

SSMA that influenced the relationship between personality and outcome .

The NAcc is considered a key node in reward circuitry involved in assigning salience

(58, 59) and hypothesized to be involved in vulnerability for drug and alcohol addiction (50, 60).

Functional connectivity mapping during resting state in healthy subjects has shown positive

connectivity between NAcc seeds and regions including the orbitofrontal, lateral temporal lobe

and precuneus (61). Within-group PPI analysis in our control group revealed decreased task-

related connectivity (Figure 2) with some of those same regions consistent with the brain’s

default mode network (DMN) shown to decrease in activity during attention-demanding tasks in

healthy subjects (62-64). Areas positively correlated in resting state may have competing

functions when focus is necessary for a task (65). Therefore task-related reductions in

connectivity with DMN regions during reward processing may represent expected decoupling as

focus shifts to process and react to incentive stimuli.

Of further interest, the between-group PPI identified differences in connectivity with

regions that map onto multiple functional networks. Neuroimaging has mapped large-scale

networks as distinct functional systems including the DMN, attention/control, visual,

auditory/phonology, motor and self-referential networks (66-72). These networks, which closely

represent underlying anatomical connectivity, maintain a high level of coherence at all times

(66). The FH+ subjects in our study had significant increased striatal couplings with attention

and motor structures, specifically the medial SSMA, precuneus and pre-and postcentral gyri

while the FH- subjects decoupled these regions during incentive anticipation, representing

distinctly different network utilization.

For example, the precuneus has been identified as a key node within the DMN in

functional connectivity analysis (73). The precuneus has among the brain’s highest glucose

metabolism consistent with a role requiring high levels of information processing relative to both

orientation within and monitoring of external environment (74). In a recent resting state study

of chronic heroin users, left NAcc had reduced connectivity with left precuneus compared with

controls (75). Our PPI results complement these results. PPI provides a measure of changes in

correlations between structures activity as a function of task manipulation (51), here between

reward or loss and neutral conditions reflecting the effect of incentive. The connectivity

differences between heroin users (less) and controls (greater) in resting state is seen in the

reverse direction between FH+ subjects (greater) and FH- controls (less) during incentive

processing. The failure to decouple the NAcc and precuneus during incentive anticipation may

suggest a pre-existing dysfunction related to coupling between reward and DMN networks in our

non-addicted, yet high-risk sample.

Our results are also consistent with recent PPI analysis of eating behaviors (19). That

study found reduced negative connectivity change between NAcc and premotor areas in response

to viewing appetizing vs. bland foods in subjects with higher food sensitivity. The authors

propose the premotor cortex may mediate the transformation of desire generated by the striatum

into preparation for action and suggest inefficient coupling between reward and feeding

networks, potentially marking vulnerability for abnormal behaviors such as overeating (19). As

similarities have been suggested between eating behaviors in obesity and drug use in addicts

(15), the sensorimotor cortex may have a similar preparatory role in use of addictive substances.

In FH+ subjects, the striatal-SSMA connectivity change was not only less negative than for FH-

during reward anticipation, but actually positive, potentially representing significant

inefficiencies between salience assignation and action.

Furthermore, we found NAcc-SSMA connectivity correlated with sensation seeking in

opposite directions as a function of family history, with higher sensation seeking associated with

increasingly positive connectivity in FH+. Incentive-related connectivity mediated the

relationship between this personality trait and DDU in at-risk subjects, suggesting that individual

variations in neural connectivity, and not only single brain regions, influence the relationship

between personality and outcome. The SSMA, encompassing the mesial portion of the superior

frontal gyrus, paracentral lobule, cingulate gyrus and precuneus (54), incorporates structures

within both the attention and motor networks (66). As such, it is involved in initiation and

integration of motor function with visual sensory and emotional guidance, or an “urge to move”

(54, 76, 77). The atypical reward-related coupling in high-risk subjects may reflect inefficient

communication of incentive salience processed through the NAcc and linked with internal

mentation through the precuneus and sensoriomotor regions.

As PPI does not yield information regarding causal or directional relationships between

functional coupled regions but infers context-driven changes in interregional correlations

between structures, interpretation of our results is limited. In addition, as a developmental

imbalance between control and reward regions in adolescents has been proposed to influence

risky behaviors (25), it was interesting that we did not find connectivity differences with

executive/cognitive areas as initially hypothesized. These results do not rule out developmental

differences based on familial risk. Indeed as competition between functional networks has been

shown to mediate task performance (78), this highlights the need for additional study to

illuminate maturational trajectories of competing networks.

This study found that functional connectivity with the reward/salience regions,

specifically the NAcc, may represent a preexisting neurobiological difference in FH+ youth.

Despite similar performance and NAcc activation, reward processing in the high-risk group

involved positive striatal functional connectivity with attention, motor and DMN structures

versus decoupling seen in FH- subjects, suggestive of inefficient inter-network communication in

the FH+ group. Importantly, NAcc-SSMA connectivity mediated the relationship between the

personality trait of sensation seeking and DDU in high-risk subjects representing a potential

model of vulnerability. Abnormal coupling between the reward system and multiple functional

networks may extend beyond the currently hypothesized imbalance between reward and

executive control systems influencing vulnerability for substance abuse.

Table 1. Subject Characteristics.FH- FH+

N 21 49Males: Females 8:14 16:32Age at Scanning (years) 20.1 (1.3) 20.1 (1.3)Age at Most Recent Assessment (years) 19.6 (1.6) 19.5 (1.5)IQ (WISC-III)a 112 (9) 110 (12)Alcohol Abuse or Dependence 1 5Marijuana Abuse or Dependence 2 4Nicotine Dependence 0 1Other Drug Abuse or Dependence 1 1Any Substance Use Disorder Dxb 2 6Depression Dx 2 0Conduct Disorder Dx 0 1Attention Deficit Disorder Dx 1 0Any Dxc 2 5Drinking and Drug Use (DDU) 3.24 (3.07) 3.47 (2.95) Drink Volume (drinks/month) 29.2 (40.4) 31.2 (42.2) Packs/year Cigarettes Smoked 0.32 (0.93) 0.27 (0.48) # Illicit drugs ever used 1.2 (2.4) 1.5 (1.7)Multiple Affect Adjective Checklist Anxiety Youth 1.25 (1.45) 1.43 (2.18) Depression 1.20 (2.28) 1.09 (2.05) Hostility 1.05 (1.50) 1.48 (2.00) Positive Affect 10.95 (4.90) 10.13 (5.18) Sensation Seeking 5.25 (2.47) 5.37 (2.25)Mother/Father/Both Alcohol Abuse NA 4/6/1Mother/Father/Both Alcohol Abuse or Dependence NA 3/22/24Mother/Father/Both Abused other Drugsd,e 1/2/0 5/15/7

FH-, family history negative; FH+, family history positive; Dx, diagnosis. a Wechsler Intelligence Scale for Children – 3rd edition. These data were collected when participants were between the ages of 12 and 14 years as part of the ongoing Michigan Longitudinal Study.b Includes alcohol abuse or dependence, marijuana abuse or dependence and/or other drug abuse or dependencec Includes conduct disorder, attention deficit disorder and/or any substance use disorder, excluding nicotine.d Includes endorsing at least one of the following: amphetamines, cocaine, sedatives/hypnotics, opiates, or marijuana.e For FH- group: 2 marijuana only; 1 marijuana and amphetamines. Data presented as Mean (Standard Deviation) where applicable.

Table 2. Task Performance and NAcc Activations by Family History.FH- FH+

MID task performance Hit Rates (%) Lose Small 58 (17) 57 (17) Lose Big 62 (22) 65 (18) Neutral 48 (21) 48 (17) Win Small 62 (15) 57 (18) Win Big 68 (19) 67 (19) Hit Response Time (msecs) Lose Small* 172 (35) 190 (37) Lose Big 175 (40) 187 (37) Neutral 172 (33) 183 (41) Win Small 176 (33) 182 (42) Win Big Hit* 159 (40) 182 (41)

MID task activation (effect size) Left NAcc Lose Small - Neutral -0.004 (0.586) 0.030 (0.800) Lose Big - Neutral 0.608 (1.005) 1.000 (0.966) Win Small - Neutral 0.889 (0.889) 0.780 (1.058) Win Big Hit - Neutral 1.657 (1.324) 1.807 (1.502)

Right NAcc Lose Small - Neutral 0.238 (0.612) 0.032 (0.741) Lose Big - Neutral 0.797 (1.713) 0.935 (0.917) Win Small - Neutral 0.897 (0.804) 0.752 (1.011) Win Big Hit - Neutral 1.756 (1.282) 1.756 (1.296)

FH-, family history negative; FH+, family history positive.*Significant differences between groups (described fully in text).

Table 3. Brain Regions with Group Differences in Functional Connectivity with Nucleus Accumbens during Incentive Anticipation.

Brain Region Brodman’s Areas

MNI space

x y z

Cluster Size

(voxels)Peak

t

Cluster Level p(corrected)

Left NAcc FH+ > FH-R/L SSMA 4/6 6 -34 68 746 5.6 <0.001R Precuneus 7 16 -64 44 221 5.4 0.006R Postcentral gyrus 2 46 -28 42 547 4.9 <0.001L Postcentral gyrus 2/40 -44 -34 44 447 4.5 <0.001R/L SMA 6 -6 -4 50 205 4.1 0.008

Right NAcc FH+ > FH-R/L SSMA 6 -6 -16 50 609 4.2 <0.001R Superior parietal gyrus 5 18 -46 58 248 4.2 0.004

FH- > FH+R Middle occipital gyrus 18/19 32 -84 14 163 5.1 0.027R Lingual gyrus 18 26 -86 -10 281 5.0 0.002

L, left; R, right; NA, not applicable; NAcc, nucleus accumbens; SSMA supplementary sensoriomotor area; SMA, supplementary motor area.

Figure 1. A) Schematic illustration of the monetary incentive delay task performed by subjects in the fMRI scanner. B) Location of 5-mm diameter spherical nucleus accumbens mask at the ventromesial intersection of caudate and putamen (y=13 MNI). C) Activation in bilateral nucleus accumbens during reward and loss by family history groups. Error bars: ± 1 Standard Error.

Figure 2. Task-related negative functional connectivity with NAcc during Incentive>Neutral for FH- controls maps onto regions identified in default mode network resting state regions, cluster-level p<0.05 corrected, representing areas significantly decoupled during incentive processing.

Figure 3. Extracted strengths of task-related functional connectivity with NAcc during Incentive>Neutral anticipatory processing by family history group.

Figure 4. A) Statistical parametric maps identifying large cluster designated supplementary sensoriomotor area (SSMA). B) Correlations between left NAcc–SSMA incentive connectivity and sensation seeking by family history group. C) Correlations between left NAcc–SSMA incentive connectivity and Drinking and Drug Use by family history group.

Figure 5. Meditation model of vulnerability with unstandardized coefficients testing indirect effect of connectivity on relationship of sensation seeking on drinking and drug use.

Supplemental Table 1. Brain Regions with Functional Connectivity to Left Nucleus Accumbens during Incentive Anticipation.

Brain Region Brodman’s Areas

MNI space

x y z

Cluster Size

(voxels)Peak

t

VoxelLevel p

(FDR-corr)

ClusterLevel p(corrected)

FH- Negative Connectivity

R Inferior Parietal Lobe 39 48 -2 38 66 5.75 0.034 0.020L Paracentral/Precunes 6 -6 -34 56 93 5.15 0.037 0.005R Superior Frontal Gyrus 8 26 22 42 32 4.96 0.037 0.146R Paracentral Lobule 4 4 -36 66 57 4.94 0.037 0.033L Inferior Frontal Gyrus 45 -50 30 12 32 4.83 0.037 0.146L Precentral Gyrus 4 -52 -4 28 33 4.82 0.037 0.137L Inferior Parietal Lobe 48 -52 -28 24 16 4.80 0.037 0.415R Precentral Gyrus NA 34 -12 40 18 4.76 0.037 0.364L Superior Frontal Gyrus 11 -14 62 -6 32 4.75 0.037 0.146R Precuneus 7 14 -62 46 36 4.67 0.037 0.046R Middle Frontal Gyrus 47 36 50 0 33 4.62 0.037 0.137

FH+ Negative ConnectivityL Superior Temporal Gyrus 21 -46 -2 -12 138 5.78 0.005 0.006R Inferior Frontal Gyrus 47 36 32 -12 47 5.40 0.006 0.174R Precuneus 23 10 -62 22 44 5.00 0.012 0.004R Orbital Frontal Gyrus NA 2 18 -16 98 4.82 0.014 0.023R Parahippocampal Gyrus 36 28 -14 -24 63 4.77 0,014 0,089L Middle Occipital Gyrus 39 -40 -76 24 30 4.72 0.015 0.363R Orbital Frontal Gyrus 11 4 36 -16 42 4.57 0.015 0.216L Medial Occipital Lobe 17 -16 -60 6 55 4.49 0.016 0.124L Inferior Occipital Lobe 19 -20 -76 22 143 4.48 0.016 0.005R Inferior Frontal Gyrus 45 48 32 6 18 4.47 0.017 0.600R Middle Temporal Gyrus 21 54 -54 12 18 4.25 0.020 0.600

L, left; R, right; NA, not applicable; FDR-corr, false discovery rate corrected.

Supplemental Table 2. Brain Regions with Functional Connectivity to Right Nucleus Accumbens during Incentive Anticipation.

Brain Region Brodman’s Areas

MNI space

x y z

Cluster Size

(voxels)Peak

t

VoxelLevel p

(FDR-corr)

ClusterLevel p(corrected)

FH- Positive PPIR Middle Occipital Lobe 27 32 -82 14 15 5.91 0.011 0.035

Negative PPIR Precuneus/Paracentral 48 8 -50 44 3188 5.98 0.007 <0.001L Superior Temporal Gyrus 11 -38 -24 2 433 5.87 0.007 <0.001R Middle Occipital Lobe 23 50 -62 32 312 5.45 0.007 0.002L Lateral Parietal Lobe 48 -52 -28 20 275 5.36 0.007 0.005L Hippocampus 48 -32 -8 -28 126 5.02 0.007 0.143R Middle Cingulate 10 16 24 32 157 5.02 0.007 0.066L Superior Frontal Gyrus 19 -24 36 34 295 4.97 0.007 0.003R Superior Frontal Gyrus 39 16 8 54 132 4.58 0.010 0.123R Insula 11 36 -16 20 110 4.49 0.011 0.214R Anterior Cingulate 47 10 46 22 59 4.39 0.011 0.693R Lateral Parietal Lobe 48 68 -24 28 20 4.39 0.011 0.997L Angular Gyrus 19 -40 -68 36 80 4.28 0.013 0.445R Superior Frontal Gyrus 20 14 44 0 118 4.18 0.014 0.175L Orbital Frontal Gyrus NA -28 48 -2 44 4.15 0.015 0.871R Precentral Gyrus NA 52 2 36 32 4.14 0.015 0.964R Superior Frontal Gyrus 4 12 60 8 47 4.11 0.015 0.839L Superior Frontal Gyrus 48 -12 50 13 28 4.00 0.017 0.981R Thalaums 48 14 -26 10 22 3.99 0.017 0.995L Orbital Frontal Gyrus -12 -12 60 -8 72 3.93 0.018 0.533R Paracentral Lobule NA 8 -36 66 52 3.89 0.019 0.781R Caudate 45 10 12 -8 37 3.79 0.021 0.933L Middle Cingulate 17 -4 -4 34 16 3.73 0.022 0.999R Putamen 47 34 -12 -4 23 3.67 0.024 0.993L Thalamus 39 -8 -26 12 18 3.61 0.026 0.998L Middle Cingulate 23 -6 -42 40 34 3.43 0.031 0.953

FH+ Positive PPIL Thalamus NA -2 -20 6 53 4.68 0.130 0.029

Negative PPIR Calcarine Fissure 19 18 -86 2 22 5.08 0.071 0.034

L, left; R, right; NA, not applicable; FDR-corr, false discovery rate corrected.

Supplementary Table 3. Statistics for Correlations of Functional Connectivity during Incentive Anticipation with Left NAcc Controlling for Response Time.

Family History

NegativeFamily History

Positive FH- vs. FH+ + Sensation Seeking

Drinking Drug Use

Sensation Seeking

Drinking Drug Use

Sensation Seeking

Drinking Drug Use

Brain Region r p

r p

r p

r p

z p

z p

L NAcc FH+ vs. FH-

R/L SSMA-.652** .054 .378** .304 -3.72++ -0.82

.003 .825 .016 .056 0.0002 0.412

R Precuneus-.020 .232 .328* .284 -1.14 -0.18.934 .339 .039 .075 0.252 0.857

R Postcentral -.247 .212 .069 -.041 -1.02 0.81.309 .384 .674 .802 0.308 0.418

L Postcentral-.048 .383 .080 -.181 -1.91 1.85 .846 .106 .622 .265 0.056 0.064

R/L SMA-.108 .180 .035 -.042 -0.45 0.72 .659 .461 .829 .798 0.653 0.478

R NAcc FH+ vs. FH-

R/L SSMA-.228 .244 .177 .100 -1.30 0.47.348 .314 .275 .539 0.194 0.638

R Superior parietal-.474* .131 .102 .216 -1.95 0.28.040 .495 .529 .182 0.051 1.780

FH- vs. FH+

R Middle occipital-.090 -.239 .293 .410** -1.24 -2.14+

.713 .324 .066 .009 0.215 0.032

R Lingual-.199 -.312 .221 .308 -1.35 -2.03+

.414 .193 .170 .053 0.177 0.042

*significant ≤0.05,**significant≤0.025 corrected for multiple comparisons,+ results of Fisher’s Z-transformation for testing differences between correlation coefficients, ++ significant difference between group correlation coefficients. NAcc, nucleus accumbens; RT, response time; L, left; R, right; SSMA, supplementary sensoriomotor area; SMA, supplementary motor area.

REFERENCES

1. Le Moal M, Simon H. Mesocorticolimbic dopaminergic network: functional and regulatory roles. Physiol Rev. 1991;71(1):155-234.

2. Koob GF, Le Moal M. Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology. 2001;24(2):97-129.

3. Robbins TW, Everitt BJ. Drug addiction: bad habits add up. Nature. 1999;398(6728):567-70.

4. Schultz W. Predictive Reward Signal of Dopamine Neurons. J Neurophysiol. 1998;80(1):1-27.

5. Tobler PN, O'Doherty JP, Dolan RJ, Schultz W. Reward Value Coding Distinct From Risk Attitude-Related Uncertainty Coding in Human Reward Systems. J Neurophysiol. 2007;97(2):1621-32.

6. Volkow ND, Wang G-J, Ma Y, Fowler JS, Zhu W, Maynard L, et al. Expectation Enhances the Regional Brain Metabolic and the Reinforcing Effects of Stimulants in Cocaine Abusers. J Neurosci. 2003;23(36):11461-8.

7. Horvitz JC. Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience. 2000;96(4):651-6.

8. Zink CF, Pagnoni G, Martin ME, Dhamala M, Berns GS. Human striatal response to salient nonrewarding stimuli. J Neurosci. 2003;23(22):8092-7.

9. Volkow ND, Fowler JS, Wang GJ, Baler R, Telang F. Imaging dopamine's role in drug abuse and addiction. Neuropharmacology. 2009;56(Supplement 1):3-8.

10. Groenewegen HJ, Wright CI, Beijer AVJ, Voorn P. Convergence and Segregation of Ventral Striatal Inputs and Outputs. Annals of the New York Academy of Sciences. 1999;877(1):49-63.

11. Camara E, Rodriguez-Fornells A, Ye Z, Munte TF. Reward networks in the brain as captured by connectivity measures. Frontiers in Neuroscience. 2009;3(3):350-62.

12. Camara E, Rodriguez-Fornells A, Munte TF. Functional connectivity of reward processing in the brain. Front Hum Neurosci. 2008;2:19.

13. Ma N, Liu Y, Li N, Wang C-X, Zhang H, Jiang X-F, et al. Addiction related alteration in resting-state brain connectivity. NeuroImage. 2010;49(1):738-44.

14. Filbey FM, Claus E, Audette AR, Niculescu M, Banich MT, Tanabe J, et al. Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology. 2008;33(6):1391-401.

15. Volkow ND, Wise RA. How can drug addiction help us understand obesity? Nat Neurosci. 2005;8(5):555-60.

16. Stoeckel LE, Kim J, Weller RE, Cox JE, Cook Iii EW, Horwitz B. Effective connectivity of a reward network in obese women. Brain Research Bulletin. 2009;79(6):388-95.

17. Braet C, Van Strien T. Assessment of emotional, externally induced and restrained eating behaviour in nine to twelve-year-old obese and non-obese children. Behav Res Ther. 1997;35(9):863-73.

18. Elfhag K, Tholin S, Rasmussen F. Consumption of fruit, vegetables, sweets and soft drinks are associated with psychological dimensions of eating behaviour in parents and their 12-year-old children. Public Health Nutrition. 2008;11(09):914-23.

19. Passamonti L, Rowe JB, Schwarzbauer C, Ewbank MP, von dem Hagen E, Calder AJ. Personality Predicts the Brain's Response to Viewing Appetizing Foods: The Neural Basis of a Risk Factor for Overeating. J Neurosci. 2009;29(1):43-51.

20. Spear LP. The adolescent brain and age-related behavioral manifestations. Neuroscience and biobehavioral reviews. 2000;24(4):417-63.

21. Scheres A, Dijkstra M, Ainslie E, Balkan J, Reynolds B, Sonuga-Barke E, et al. Temporal and probabilistic discounting of rewards in children and adolescents: effects of age and ADHD symptoms. Neuropsychologia. 2006;44(11):2092-103.

22. Steinberg L, Graham S, O'Brien L, Woolard J, Cauffman E, Banich M. Age differences in future orientation and delay discounting. Child Dev. 2009;80(1):28-44.

23. Olson EA, Hooper CJ, Collins P, Luciana M. Adolescents' performance on delay and probability discounting tasks: contributions of age, intelligence, executive functioning, and self-reported externalizing behavior. Pers Individ Dif. 2007;43(7):1886-97.

24. Casey BJ, Getz S, Galvan A. The adolescent brain. Dev Rev. 2008;28(1):62-77.25. Galvan A, Hare TA, Parra CE, Penn J, Voss H, Glover G, et al. Earlier development of

the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. J Neurosci. 2006;26(25):6885-92.

26. Van Leijenhorst L, Moor BG, Op de Macks ZA, Rombouts SARB, Westenberg PM, Crone EA. Adolescent risky decision-making: Neurocognitive development of reward and control regions. NeuroImage. 2010;51(1):345-55.

27. Substance Abuse and Mental Health Services Administration. Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of National Findings. (SAMHSA, Office of Applied Studies, NSDUH Series H-38A, HHS Publication No. SMA 10-4586 Findings). Rockville MD2010.

28. National Institute on Alcohol Abuse and Alcoholism. Alcohol involvement over the life course. NIAAA, Tenth Special Report to the US Congress on Alcohol and Health:

Highlights from current research. Bethesda, MD: U.S. Department of Health and Human Services; 2000. p. 28-53.

29. Nestler EJ. Genes and addiction. Nature Genetics. 2000;26(3):277-81.30. Tyndale RF. Genetics of alcohol and tobacco use in humans. Ann Med. 2003;35(2):94-

121.31. Heitzeg MM, Nigg JT, Yau WY, Zubieta JK, Zucker RA. Affective circuitry and risk for

alcoholism in late adolescence: differences in frontostriatal responses between vulnerable and resilient children of alcoholic parents. Alcohol Clin Exp Res. 2008;32(3):414-26.

32. Heitzeg MM, Nigg JT, Yau W-YW, Zucker RA, Zubieta J-K. Striatal Dysfunction Marks Preexisting Risk and Medial Prefrontal Dysfunction Is Related to Problem Drinking in Children of Alcoholics. Biological Psychiatry. 2010;In Press, Corrected Proof.

33. Zuckerman M. Behavioral expressions and biosocial bases of sensation seeking. New York: Cambridge University Press; 1994.

34. Malmberg M, Overbeek G, Monshouwer K, Lammers J, Vollebergh W, Engels R. Substance use risk profiles and associations with early substance use in adolescence. Journal of Behavioral Medicine. 2010;33(6):474-85.

35. Zuckerman M. Drug usage as one manifestation of a sensation seeking trait. In: Keup W, editor. Drug abuse; current concepts and research. Springfield, IL: Charles Thomas; 1972. p. 154-63.

36. Bjork JM, Knutson B, Hommer DW. Incentive-elicited striatal activation in adolescent children of alcoholics. Addiction. 2008;103(8):1308-19.

37. Zucker RA, Fitzgerald HE, Refior SK, Puttler LI, Pallas DM, Ellis DA. The clinical and social ecology of childhood for children of alcoholics: Description of a study and implications for a differentiated social policy. In: Fitzgerald HE, Lester BM, Zuckerman BS,

editors. Children of addiction: Research, Health and Policy Issues. New York: Routledge Falmer; 2000. p. 109-41.

38. Krueger RF. Personality traits in late adolescence predict mental disorders in early adulthood: a prospective-epidemiological study. J Pers. 1999;67(1):39-65.

39. Knutson B, Westdorp A, Kaiser E, Hommer D. FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage. 2000;12(1):20-7.

40. Zucker RA. Scaling the developmental momentum of alcoholic process via the lifetime alcohol problems score. Alcohol Alcohol Suppl. 1991;1:505-10.

41. Zucker RA, Fitzgerald HE, Noll RB. Drinking and Drug History (Revised edition, Version 4). Ann Arbor: University of Michigan Department of Psychiatry, Addiction Research Center; 1990.

42. Zucker RA, Fitzgerald HE. Drinking and Drug History Form for Children. Ann Arbor: University of Michigan Department of Psychiatry, Addiction Research Center; 1994.

43. Zuckerman M, Lubin B. Manual for the Multiple Affect Adjective Checklist. (Revised, 1985) ed. San Diego, CA: Educational and Industrial Testing Service; 1965.

44. Glover GH, Law CS. Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts. Magn Reson Med. 2001;46(3):515-22.

45. Sutton BP, Noll DC, Fessler JA. Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities. IEEE Trans Med Imaging. 2003;22(2):178-88.

46. Fessler J, Lee S, Olafsson V, Shi H, Noll D. Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity. IEEE Transactions on Signal Processing. 2005;53(9):3393-402.

47. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17(2):825-41.

48. Lacadie CM, Fulbright RK, Rajeevan N, Constable RT, Papademetris X. More accurate Talairach coordinates for neuroimaging using non-linear registration. Neuroimage. 2008;42(2):717-25.

49. Brett M, Anton J-L, Valabregue R, Poline JB, editors. Region of interest analysis using an SPM toolbox. 8th International Conference on Functional Mapping of the Human Brain; 2002 June 2-6; Sendai, Japan.

50. Bjork JM, Smith AR, Hommer DW. Striatal sensitivity to reward deliveries and omissions in substance dependent patients. Neuroimage. 2008;42(4):1609-21.

51. Friston KJ, Buechel C, Fink GR, Morris J, Rolls E, Dolan RJ. Psychophysiological and modulatory interactions in neuroimaging. Neuroimage. 1997;6(3):218-29.

52. Gitelman DR, Penny WD, Ashburner J, Friston KJ. Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. Neuroimage. 2003;19(1):200-7.

53. Preacher KJ, Hayes AF. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput. 2004;36(4):717-31.

54. Lim SH, Dinner DS, Pillay PK, Lüders H, Morris HH, Klem G, et al. Functional anatomy of the human supplementary sensorimotor area: results of extraoperative electrical stimulation. Electroencephalography and Clinical Neurophysiology. 1994;91(3):179-93.

55. Penfield W, Jasper H. Functional localization in the cerebral cortex. In: Penfield W, Jasper H, editors. Epiilepsy and the functional anatomy of the human brain. London: J. & A. Churchill; 1954. p. 41-155.

56. Sher KJ, Bartholow BD, Wood MD. Personality and substance use disorders: A prospective study. Journal of Consulting and Clinical Psychology. 2000;68(5):818-29.

57. Schall M, Kemeny A, Maltzman I. Factors associated with alcohol use in university students. J Stud Alcohol. 1992;53(2):122-36.

58. Knutson B, Cooper JC. Functional magnetic resonance imaging of reward prediction. Current Opinion in Neurology. 2005;18(4):411-7.

59. McClure SM, York MK, Montague PR. The neural substrates of reward processing in humans: the modern role of FMRI. Neuroscientist. 2004;10(3):260-8.

60. Blum K, Braverman ER, Holder JM, Lubar JF, Monastra VJ, Miller D, et al. Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. J Psychoactive Drugs. 2000;32 Suppl:i-iv, 1-112.

61. Di Martino A, Scheres A, Margulies DS, Kelly AMC, Uddin LQ, Shehzad Z, et al. Functional Connectivity of Human Striatum: A Resting State fMRI Study. Cerebral Cortex. 2008;18(12):2735-47.

62. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(2):676-82.

63. Greicius MD, Menon V. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J Cogn Neurosci. 2004;16(9):1484-92.

64. Mazoyer B, Zago L, Mellet E, Bricogne S, Etard O, Houde O, et al. Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Res Bull. 2001;54(3):287-98.

65. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(27):9673-8.

66. Deco G, Corbetta M. The Dynamical Balance of the Brain at Rest. The Neuroscientist.67. Cordes D, Haughton V, Carew JD, Arfanakis K, Maravilla K. Hierarchical clustering to

measure connectivity in fMRI resting-state data. Magn Reson Imaging. 2002;20(4):305-17.68. De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM. fMRI resting state

networks define distinct modes of long-distance interactions in the human brain. NeuroImage. 2006;29(4):1359-67.

69. Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences. 2006;103(37):13848-53.

70. Dosenbach NUF, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RAT, et al. Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences. 2007;104(26):11073-8.

71. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences. 2006;103(26):10046-51.

72. Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. J Neurosci. 2006;26(51):13338-43.

73. Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. NeuroImage. 2008;42(3):1178-84.

74. Gusnard DA, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci. 2001;2(10):685-94.

75. Ma DL, Tang YC, Chen PM, Chia SC, Jiang FL, Burgunder JM, et al. Reorganization of CA3 area of the mouse hippocampus after pilocarpine induced temporal lobe epilepsy with special reference to the CA3-septum pathway. J Neurosci Res. 2006;83(2):318-31.

76. Fox PT, Fox JM, Raichle ME, Burde RM. The role of cerebral cortex in the generation of voluntary saccades: a positron emission tomographic study. Journal of Neurophysiology. 1985;54(2):348-69.

77. Tanji J, Evarts EV. Anticipatory activity of motor cortex neurons in relation to direction of an intended movement. Journal of Neurophysiology. 1976;39(5):1062-8.

78. Kelly AMC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Competition between functional brain networks mediates behavioral variability. NeuroImage. 2008;39(1):527-37.