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:
1
Department 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 Center
University of Michigan
4250 Plymouth Rd
Ann Arbor, MI 48109
Phone: 248-766-4806
Fax: 734-232-0287
Email: bweiland@umich.edu
Key Words: Adolescent, alcoholism, functional connectivity, nucleus accumbens, reward
substance use.
Abstract: 261 words ------- limit 250
Article: 4193 words ---------limit 4000 --- about 5 over now
Figures: 5
Tables: 3
Supplemental 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- = 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 NAccorbitofrontal 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 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 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 singleshot 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 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) 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 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 interaction term, contrast vector and
extracted time-series plus motion regressors from the original design. Single subject contrasts
for the first regressor (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 Ztransformations 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 biascorrected 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 ttests 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 FHgroup 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 taskrelated 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 FHduring 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.
FH21
8:14
20.1 (1.3)
19.6 (1.6)
112 (9)
1
2
0
1
2
2
0
1
2
3.24 (3.07)
29.2 (40.4)
0.32 (0.93)
1.2 (2.4)
FH+
49
16:32
20.1 (1.3)
19.5 (1.5)
110 (12)
5
4
1
1
6
0
1
0
5
3.47 (2.95)
31.2 (42.2)
0.27 (0.48)
1.5 (1.7)
N
Males: Females
Age at Scanning (years)
Age at Most Recent Assessment (years)
IQ (WISC-III)a
Alcohol Abuse or Dependence
Marijuana Abuse or Dependence
Nicotine Dependence
Other Drug Abuse or Dependence
Any Substance Use Disorder Dxb
Depression Dx
Conduct Disorder Dx
Attention Deficit Disorder Dx
Any Dxc
Drinking and Drug Use (DDU)
Drink Volume (drinks/month)
Packs/year Cigarettes Smoked
# Illicit drugs ever used
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
4/6/1
Mother/Father/Both Alcohol Abuse or Dependence
NA
3/22/24
d,e
Mother/Father/Both Abused other Drugs
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 dependence
c
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.
MID task performance
Hit Rates (%)
Lose Small
Lose Big
Neutral
Win Small
Win Big
Hit Response Time (msecs)
Lose Small*
Lose Big
Neutral
Win Small
Win Big Hit*
MID task activation (effect size)
Left NAcc
Lose Small - Neutral
Lose Big - Neutral
Win Small - Neutral
Win Big Hit - Neutral
Right NAcc
Lose Small - Neutral
Lose Big - Neutral
Win Small - Neutral
Win Big Hit - Neutral
FH-
FH+
58 (17)
62 (22)
48 (21)
62 (15)
68 (19)
57 (17)
65 (18)
48 (17)
57 (18)
67 (19)
172 (35)
175 (40)
172 (33)
176 (33)
159 (40)
190 (37)
187 (37)
183 (41)
182 (42)
182 (41)
-0.004 (0.586)
0.608 (1.005)
0.889 (0.889)
1.657 (1.324)
0.030 (0.800)
1.000 (0.966)
0.780 (1.058)
1.807 (1.502)
0.238 (0.612)
0.797 (1.713)
0.897 (0.804)
1.756 (1.282)
0.032 (0.741)
0.935 (0.917)
0.752 (1.011)
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.
MNI
Cluster
Cluster
Brain Region
Brodman’s
space
Size
Peak
Level p
Areas
x
y
z
(voxels)
t
(corrected)
Left NAcc
FH+ > FHR/L SSMA
4/6
6 -34 68
746
5.6
<0.001
R Precuneus
7
16 -64 44
221
5.4
0.006
R Postcentral gyrus
2
46 -28 42
547
4.9
<0.001
L Postcentral gyrus
2/40
-44 -34 44
447
4.5
<0.001
R/L SMA
6
-6 -4 50
205
4.1
0.008
Right NAcc
FH+ > FHR/L SSMA
R Superior parietal gyrus
6
5
FH- > FH+
R Middle occipital gyrus
R Lingual gyrus
18/19
18
-6 -16
18 -46
50
58
609
248
32 -84 14
26 -86 -10
163
281
4.2
4.2
5.1
5.0
<0.001
0.004
0.027
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 FHcontrols 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.
MNI
Cluster
Voxel
Cluster
Brain Region
Brodman’s
space
Size
Peak Level p
Level p
Areas
x
y
z
(voxels)
t
(FDR-corr) (corrected)
FHNegative Connectivity
R Inferior Parietal Lobe
L Paracentral/Precunes
R Superior Frontal Gyrus
R Paracentral Lobule
L Inferior Frontal Gyrus
L Precentral Gyrus
L Inferior Parietal Lobe
R Precentral Gyrus
L Superior Frontal Gyrus
R Precuneus
R Middle Frontal Gyrus
39
6
8
4
45
4
48
NA
11
7
47
48
-6
26
4
-50
-52
-52
34
-14
14
36
-2
-34
22
-36
30
-4
-28
-12
62
-62
50
38
56
42
66
12
28
24
40
-6
46
0
66
93
32
57
32
33
16
18
32
36
33
5.75
5.15
4.96
4.94
4.83
4.82
4.80
4.76
4.75
4.67
4.62
0.034
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.020
0.005
0.146
0.033
0.146
0.137
0.415
0.364
0.146
0.046
0.137
FH+
Negative Connectivity
L Superior Temporal Gyrus
R Inferior Frontal Gyrus
R Precuneus
R Orbital Frontal Gyrus
R Parahippocampal Gyrus
L Middle Occipital Gyrus
R Orbital Frontal Gyrus
L Medial Occipital Lobe
L Inferior Occipital Lobe
R Inferior Frontal Gyrus
R Middle Temporal Gyrus
21
47
23
NA
36
39
11
17
19
45
21
-46
36
10
2
28
-40
4
-16
-20
48
54
-2
32
-62
18
-14
-76
36
-60
-76
32
-54
-12
-12
22
-16
-24
24
-16
6
22
6
12
138
47
44
98
63
30
42
55
143
18
18
5.78
5.40
5.00
4.82
4.77
4.72
4.57
4.49
4.48
4.47
4.25
0.005
0.006
0.012
0.014
0,014
0.015
0.015
0.016
0.016
0.017
0.020
0.006
0.174
0.004
0.023
0,089
0.363
0.216
0.124
0.005
0.600
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.
MNI
Cluster
Voxel
Cluster
Brain Region
Brodman’s
space
Size
Peak Level p
Level p
Areas
x
y
z
(voxels)
t
(FDR-corr) (corrected)
FHPositive PPI
R Middle Occipital Lobe
27
32 -82 14
15
5.91
0.011
0.035
Negative PPI
R Precuneus/Paracentral
L Superior Temporal Gyrus
R Middle Occipital Lobe
L Lateral Parietal Lobe
L Hippocampus
R Middle Cingulate
L Superior Frontal Gyrus
R Superior Frontal Gyrus
R Insula
R Anterior Cingulate
R Lateral Parietal Lobe
L Angular Gyrus
R Superior Frontal Gyrus
L Orbital Frontal Gyrus
R Precentral Gyrus
R Superior Frontal Gyrus
L Superior Frontal Gyrus
R Thalaums
L Orbital Frontal Gyrus
R Paracentral Lobule
R Caudate
L Middle Cingulate
R Putamen
L Thalamus
L Middle Cingulate
FH+
Positive PPI
L Thalamus
Negative PPI
R Calcarine Fissure
48
11
23
48
48
10
19
39
11
47
48
19
20
NA
NA
4
48
48
-12
NA
45
17
47
39
23
8
-38
50
-52
-32
16
-24
16
36
10
68
-40
14
-28
52
12
-12
14
-12
8
10
-4
34
-8
-6
-50 44
-24
2
-62 32
-28 20
-8 -28
24 32
36 34
8 54
-16 20
46 22
-24 28
-68 36
44
0
48 -2
2 36
60
8
50 13
-26 10
60 -8
-36 66
12 -8
-4 34
-12 -4
-26 12
-42 40
3188
433
312
275
126
157
295
132
110
59
20
80
118
44
32
47
28
22
72
52
37
16
23
18
34
5.98
5.87
5.45
5.36
5.02
5.02
4.97
4.58
4.49
4.39
4.39
4.28
4.18
4.15
4.14
4.11
4.00
3.99
3.93
3.89
3.79
3.73
3.67
3.61
3.43
0.007
0.007
0.007
0.007
0.007
0.007
0.007
0.010
0.011
0.011
0.011
0.013
0.014
0.015
0.015
0.015
0.017
0.017
0.018
0.019
0.021
0.022
0.024
0.026
0.031
<0.001
<0.001
0.002
0.005
0.143
0.066
0.003
0.123
0.214
0.693
0.997
0.445
0.175
0.871
0.964
0.839
0.981
0.995
0.533
0.781
0.933
0.999
0.993
0.998
0.953
NA
-2 -20
6
53
4.68
0.130
0.029
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
Family History
Negative
Positive
FH- vs. FH+ +
Sensation Drinking Sensation Drinking Sensation Drinking
Seeking Drug Use Seeking Drug Use Seeking Drug Use
r
r
r
r
z
z
Brain Region
p
p
p
p
p
p
L NAcc
FH+ vs. FHR/L SSMA
R Precuneus
R Postcentral
L Postcentral
R/L SMA
-.652**
.054
.378**
.304
-3.72++
-0.82
.003
.825
.016
.056
0.0002
0.412
-.020
.232
.328*
.284
-1.14
-0.18
.934
.339
.039
.075
0.252
0.857
-.247
.212
.069
-.041
-1.02
0.81
.309
.384
.674
.802
0.308
0.418
-.048
.383
.080
-.181
-1.91
1.85
.846
.106
.622
.265
0.056
0.064
-.108
.180
.035
-.042
-0.45
0.72
.659
.461
.829
.798
0.653
0.478
-.228
.244
.177
.100
-1.30
0.47
.348
.314
.275
.539
0.194
0.638
-.474*
.131
.102
.216
-1.95
0.28
.040
.495
.529
.182
0.051
1.780
-.090
-.239
.293
.410**
-1.24
-2.14+
.713
.324
.066
.009
0.215
0.032
-.199
-.312
.221
.308
-1.35
-2.03+
.414
.193
.170
.053
0.177
0.042
R NAcc
FH+ vs. FHR/L SSMA
R Superior parietal
FH- vs. FH+
R Middle occipital
R Lingual
*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):56770.
4. Schultz W. Predictive Reward Signal of Dopamine Neurons. J Neurophysiol. 1998;80(1):127.
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):94121.
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.