Striatal dysfunction marks preexisting risk and medial prefrontal dysfunction is related to problem drinking in children of alcoholics.
ABSTRACT Parental alcoholism substantially raises risk for offspring alcoholism, an effect thought to be mediated by a dysregulation in impulse control. Adult alcoholics have alterations in the frontostriatal system involved in regulating impulsive responses. However, it remains controversial whether these alterations reflect preexisting traits predisposing to problem alcohol use or are secondary to alcohol involvement.
Sixty-one 16 to 22 year olds were tested using a go/no-go task during functional magnetic resonance imaging. Forty-one were family history positive (FH+), having at least one parent with a diagnosis of alcohol use disorder (AUD), and 20 were family history negative (FH-). Two FH+ subgroups were created to disentangle alcohol involvement from preexisting risk: the FH+ control group (n = 20) had low alcohol problems, differing from the FH- group only by family history. The FH+ problem group (n = 21) had high alcohol problems.
The ventral caudate deactivated during successful inhibition in the FH- but not the FH+ groups, regardless of problem alcohol involvement. Regression analyses showed that ventral caudate deactivation was related to fewer externalizing problems as well as to family history. Orbital and left medial prefrontal regions were deactivated in both the FH- and FH+ control groups but not the FH+ problem group. Activation in these regions was associated with alcohol and other drug use.
These findings suggest a preexisting abnormality in ventral striatal function in youth at risk for AUD, which may lead to inappropriate motivational responding, and suggest that with alcohol use, the prefrontal "control" mechanism loses efficiency, further dysregulating the frontostriatal motivational circuitry.
- [Show abstract] [Hide abstract]
ABSTRACT: Objective The primary symptom of fibromyalgia is chronic, widespread pain; however, patients report additional symptoms including decreased concentration and memory. Performance-based deficits are seen mainly in tests of working memory and executive functioning. It has been hypothesized that pain interferes with cognitive performance; however, the neural correlates of this interference are still a matter of debate. In a previous, cross-sectional study, we reported that fibromyalgia patients (as compared with healthy controls) showed a decreased blood oxygen level dependent (BOLD) response related to response inhibition (in a simple Go/No-Go task) in the anterior/mid cingulate cortex, supplementary motor area, and right premotor cortex.Methods Here in this longitudinal study, neural activation elicited by response inhibition was assessed again in the same cohort of fibromyalgia patients and healthy controls using the same Go/No-Go paradigm.ResultsA decrease in percentage of body pain distribution was associated with an increase in BOLD signal in the anterior/mid cingulate cortex and the supplementary motor area, regions that have previously been shown to be “hyporeactive” in this cohort.Conclusions Our results suggest that the clinical distribution of pain is associated with the BOLD response elicited by a cognitive task. The cingulate cortex and the supplementary motor area are critically involved in both the pain system as well as the response inhibition network. We hypothesize that increases in the spatial distribution of pain might engage greater neural resources, thereby reducing their availability for other networks. Our data also point to the potential for, at least partial, reversibility of these changes.Pain Medicine 07/2014; · 2.46 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Deficits in behavioural inhibitory control are attracting increasing attention as a factor behind the development and maintenance of substance dependence. However, evidence for such a deficit is varied in the literature. Here, we synthesised published results to determine whether inhibitory ability is reliably impaired in substance users compared to controls.Drug and alcohol dependence. 08/2014;
- [Show abstract] [Hide abstract]
ABSTRACT: Brain white matter (WM) tracts, playing a vital role in the communication between brain regions, undergo important maturational changes during adolescence and young adulthood, a critical period for the development of nicotine dependence. Attention-deficit/hyperactivity disorder (ADHD) is associated with increased smoking and widespread WM abnormalities, suggesting that the developing ADHD brain might be especially vulnerable to effects of smoking. This study aims to investigate the effect of smoking on (WM) microstructure in adolescents and young adults with and without ADHD. Diffusion tensor imaging was performed in an extensively phenotyped sample of nonsmokers (n = 95, 50.5% ADHD), irregular smokers (n = 41, 58.5% ADHD), and regular smokers (n = 50, 82.5% ADHD), aged 14-24 years. A whole-brain voxelwise approach investigated associations of smoking, ADHD and their interaction, with WM microstructure as measured by fractional anisotropy (FA) and mean diffusivity (MD). Widespread alterations in FA and MD were found for regular smokers compared to irregular and nonsmokers, mainly located in the corpus callosum and WM tracts surrounding the basal ganglia. Several regions overlapped with regions of altered FA for ADHD versus controls, albeit in different directions. Irregular and nonsmokers did not differ, and ADHD and smoking did not interact. Results implicate that smoking and ADHD have independent effects on WM microstructure, and possibly do not share underlying mechanisms. Two mechanisms may play a role in the current results. First, smoking may cause alterations in WM microstructure in the maturing brain. Second, pre-existing WM microstructure differences possibly reflect a risk factor for development of a smoking addiction. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.Human Brain Mapping 12/2014; · 6.88 Impact Factor
Striatal Dysfunction Marks Preexisting Risk and Medial
Prefrontal Dysfunction Is Related to Problem Drinking
in Children of Alcoholics
Mary M. Heitzeg, Joel T. Nigg, Wai-Ying Wendy Yau, Robert A. Zucker, and Jon-Kar Zubieta
impulse control. Adult alcoholics have alterations in the frontostriatal system involved in regulating impulsive responses. However, it
remains controversial whether these alterations reflect preexisting traits predisposing to problem alcohol use or are secondary to alcohol
Methods: Sixty-one 16 to 22 year olds were tested using a go/no-go task during functional magnetic resonance imaging. Forty-one were
alcohol problems, differing from the FH– group only by family history. The FH? problem group (n ? 21) had high alcohol problems.
Results: The ventral caudate deactivated during successful inhibition in the FH– but not the FH? groups, regardless of problem alcohol
Activation in these regions was associated with alcohol and other drug use.
Conclusions: These findings suggest a preexisting abnormality in ventral striatal function in youth at risk for AUD, which may lead to
inappropriate motivational responding, and suggest that with alcohol use, the prefrontal “control” mechanism loses efficiency, further
dysregulating the frontostriatal motivational circuitry.
Key Words: Adolescent, alcoholism, caudate, medial prefrontal,
response inhibition, ventral striatum, vulnerability
predicting alcohol use disorder (AUD) from early childhood
onward is behavioral undercontrol (7,8), including externalizing
behavior (aggression and delinquency), impulsivity, and sensa-
tion seeking. These variables share the characteristic of behav-
ioral disinhibition, involving the inability, unwillingness, or fail-
ure to inhibit behavioral impulses even in the face of negative
consequences (9). Weakness in response inhibition specifically
has been found to be a general liability factor for a range of
externalizing and substance use problems (10). A primarily
right-hemisphere network including the prefrontal cortex, pari-
etal cortex, and striatum is critical to response inhibition and the
control of behavior more generally (11–13).
Few studies have investigated these neural systems directly in
children of alcoholics (COA). One functional magnetic reso-
nance imaging (fMRI) study found less activation in the left
middle frontal gyrus during response inhibition in 12- to 14-year-
old COA compared with non-COA, despite similar performance
(14). Similarly, a dysregulation index of risk was negatively
arental alcoholism significantly raises risk for offspring
alcoholism (1,2), and some of this risk is mediated through
intermediary behavioral traits (3–6). One of the core traits
correlated with left frontal eye field activation during inhibition
of eye movement, but not with performance in 12–19 year olds
(15). These findings suggest that a weakness in frontal response
inhibition circuitry may be related to risk.
Other studies have examined the neural correlates of familial
risk using cognitive or emotional paradigms that do not specifi-
cally probe impulse control but recruit neural systems involved
in behavioral regulation (16–18). A recent study of 12 to 14 year
olds found family history was related to a failure of deactivation
in medial prefrontal regions involved in the default mode
network (DMN) (19) during spatial working memory compared
with vigilance (18). This suggests familial risk may be related to
less inhibition of task-irrelevant processing. Using a reward task
known to activate the ventral striatum (20), familial risk was
investigated in 12 to 16 years old COA and non-COA (16).
Although ventral striatal activation did not differ between the
groups in this sample, it correlated positively with a personality
measure of impulsivity across groups, suggesting a possible
relationship with risk.
One limitation of these studies is that the participants showed
little evidence of behavior problems typically considered to lie
on a developmental spectrum with AUD. Most were nondrinking
youth with no behavioral or mood disorders, which may result in
a diluted representation of risk. Furthermore, it is important to
consider these neural systems in conjunction with the develop-
mental transition from late adolescence to early adulthood, when
there is major build up of alcohol use and alcohol use-related
problem behavior (21).
A recent study investigated emotional processing during this
developmental period in a design that accounted for both
familial risk and behavioral risk (17). This study revealed in-
creased dorsomedial prefrontal activation and decreased striatal
activation to emotional versus neutral stimuli in COA showing
risky drinking behavior compared with a control (nonrisky
From the Department of Psychiatry (MMH, W-YWY, RAZ, J-KZ), Addiction
Research Center (MMH, W-YWY, RAZ), and Molecular and Behavioral
Neuroscience Institute (W-YWY, J-KZ), University of Michigan, Ann Ar-
bor, Michigan, and Department of Psychiatry (JTN), Oregon Health and
Science University, Portland Oregon.
Received May 5, 2009; revised Feb 18, 2010; accepted Feb 19, 2010.
BIOL PSYCHIATRY 2010;68:287–295
© 2010 Society of Biological Psychiatry
behavior) COA group and nonCOA. These findings suggest
different neural activation in COA on a risky trajectory versus
those who are not, highlighting the importance of capturing a
range of behavior problems in studies designed to investigate
risk. To date, these neural systems have not been studied during
response inhibition in the context of familial risk during late
Another important consideration is that drug and alcohol use
undoubtedly alters the brain maturation processes, as reviewed
by Spear and Varlinskaya (22). A recent fMRI study of impulse
control found that adult abstinent alcoholics had altered neural
processing, including decreases in left dorsolateral prefrontal
cortex and increases in bilateral middle frontal gyrus (23).
Evidence for altered functional networks has also been found
during spatial working memory in adult (24,25) and adolescent
This study was designed to build on prior work by investi-
gating frontostriatal functioning during response inhibition in
late adolescence/early adulthood and relate it to both familial
risk and problem alcohol involvement. A go/no-go task (27,28)
was used during fMRI acquisition. Participants with a family
history of AUD (FH?) were divided into those with low alcohol
problems (FH? control), matched to a control group with no
parental AUD (FH?), and those with high alcohol problems
(FH? problem). This allowed the following comparisons: 1) FH–
vs. FH? control isolates effects of family history and 2) FH?
control versus FH? problem isolates effects of problem alcohol
involvement. On the basis of prior work, we expected familial
risk to be reflected in a weakness in frontal response inhibition
circuitry. Evidence also exists for striatal differences based on
risk; however, the direction this might take during impulse
control is not clear considering there are reports of both striatal
activation (29) and deactivation (30,31) during response suppres-
sion. We further expected evidence of additional recruitment of
frontal regions because of problem alcohol involvement due to
altered functional networks.
Given that alcohol problems can encompass both externaliz-
ing problem behavior (which includes both aggressive and
conduct/antisocial problems) and level of alcohol consumption,
we additionally conducted a series of supplemental analyses to
take into account the contribution of each of these variables to
the main findings. Other drug use was also considered because
it tends to co-occur with externalizing problems and alcohol
consumption and could contribute to differences in blood oxy-
gen level–dependent (BOLD) activation. Marijuana use in par-
ticular has been shown to increase prefrontal activation during
inhibitory processing (32,33).
Methods and Materials
Sixty-one (35 males) right-handed participants aged 16 to 22
(mean 19.1 ? 1.6) were recruited from the Michigan Longitudinal
Study (MLS), an ongoing, prospective community study of fami-
lies with high levels of parental AUD and a contrast sample of
nonalcoholic families drawn from the same neighborhoods (34).
Families in which the target child displayed evidence of fetal
alcohol effects were excluded from the original ascertainment.
Handedness was determined with the Edinburgh Handedness
The FH– group (n ? 20) had no parent history of AUD and
low alcohol problems (operationalized below). The FH? Control
group (n ? 20) had at least one parent with an AUD diagnosis
and low alcohol problems. The FH? Problem group (n ? 21)
had at least one parent with an AUD diagnosis and high alcohol
problems. Parent diagnosis was based on DSM-IV criteria and
established via multiple face-to-face assessments. Characteristics
of these groups are summarized in Table 1.
Exclusionary criteria for the study were neurological, acute,
uncorrected, or chronic medical illness; current or recent (within
6 months) treatment with centrally active medications; and
history of psychosis or schizophrenia in first-degree relatives. In
addition, participants were given a multidrug five-panel urine
screen before scanning, and those with a positive drug screen
were not included in this study. The presence of most Axis I
psychiatric or developmental disorders was exclusionary, with
the exception of conduct disorder, attention deficit/hyperactivity
disorder (ADHD), or prior substance use disorder (SUD). These
Table 1. Subject Characteristics and Task Performance
FH? Control FH? Problem
Alcohol Abuse or Dependence
Mj Abuse or Dependence
Other Drug Abuse or
Any Substance Use Disorder
Conduct Disorder Dx
Attention Deficit Disorder Dx
No. of Alcohol Problems from
Drinking Volumedfrom DDHx
Mj Use—Past 12 Months from
Number Illicit Drugs Ever Used
Dependence Or Abuse
YSR Form t Scores
Go/No-Go Task Performance
Reaction times (msec)
False alarm rate
Total error rate
1.1 (2.4).8 (1.8) 3.3 (2.8)e
.45 (.83) .55 (.89)2.67 (2.4)e
0/3/0 1/6/2 2/11/3
DDHx, drinking and drug history form; Dx, diagnosis; FH?, at least one
use disorder; Mj, marijuana; YSR, Youth Self-Report.
aWechsler Intelligence Scale for Children—3rd ed. These data were col-
lected when participants were between the ages of 12 and 14 years as part
of the ongoing Michigan Longitudinal Study.
bIncludes alcohol abuse or dependence, marijuana abuse or depen-
dence, and/or other drug abuse or dependence.
cIncludes conduct disorder, attention-deficit disorder, and/or any sub-
stance use disorder.
dDrinking days over past year ? usual number of drinks per day.
eSignificant differences between groups (described fully in text).
288 BIOL PSYCHIATRY 2010;68:287–295
M.M. Heitzeg et al.
disorders were allowed because they are believed to lie on an
externalizing developmental spectrum with AUD risk (36), and
their exclusion would preferentially eliminate part of the phe-
nomena of interest. Diagnosis was determined using the Diag-
nostic Interview Schedule—Child (37) for participants under age
18 and the Diagnostic Interview Schedule—Version IV for par-
ticipants 18 and older (38).
All participants gave written informed consent after explana-
tion of the experimental protocol, as approved by the local
institutional review board. Participants under age 18 signed their
assent and at least one parent gave written informed consent.
fMRI Task. A go/no-go task (27) was used to probe response
inhibition. Participants were instructed to respond to target
stimuli (letters other than X) by pressing a button (go trials) but
make no response to infrequent nontarget stimuli (X; no-go
trials). Stimulus duration was 500 msec, followed by 3500 msec
of fixation. There were five runs of 49 trials, each lasting 3 min 24
sec and containing 11, 12, or 13 no-go trials for a total of 60 no-go
trials out of 245 total trials. Before scanning, all participants had
a practice session of 49 trials on a desktop computer. False-alarm
rate and reaction times (RTs) to hits were calculated as perfor-
and Drug History Form (39,40) was used to determine alcohol
problems, drinking volume, frequency of marijuana smoking,
and number of illicit drugs used as part of the ongoing MLS. The
alcohol problem score was the number of drinking-related
problems out of a possible 37 reported by the subject since the
age of 11 (total sample mean ? SD: 4.9 ? 5.7). “Problem” alcohol
involvement was defined as a score above the mean. Supplement
1 provides more detailed information regarding the DDHx form
and specific alcohol problems reported.
Externalizing Behavior Problems. Behavior problems were
assessed with the Youth Self-Report (YSR) (41) as part of the
ongoing MLS. The YSR yields scores on two broad-band sub-
scales (externalizing and internalizing) and was completed by
each participant when they were between 15 and 17 years old.
Supplement 1 provides more detail on this measure.
MRI Data Acquisition. Whole-brain BOLD functional images
were acquired on a 3.0-Tesla GE Signa scanner (Milwaukee,
Wisconsin) using a T2*-weighted single-shot combined spiral
in–out sequence (42) with the following parameters: repetition
time (TR) ? 2000 msec, echo time (TE) ? 30 msec, flip angle ?
90°; field-of-view (FOV) ? 200 mm; matrix size ? 64 ? 64;
in-plane resolution ? 3.12 ? 3.12 mm; and slice thickness ? 4
mm. The entire volume of 29 axial slices was acquired every 2
sec. A high-resolution anatomic T1 scan was obtained for spatial
normalization (three-dimensional spoiled gradient-recalled echo,
TR ? 25 msec, min TE, FOV ? 25 cm, 256 ? 256 matrix, slice
thickness ? 1.4 mm). Participant motion was minimized using
foam pads placed around the head along with a forehead strap.
In addition, the importance of keeping as still as possible was
fMRI Data. Functional images were reconstructed using an
iterative algorithm (43,44). Subject head motion and slice-acqui-
sition timing were corrected using FSL 4.0 (Analysis Group,
FMRIB, Oxford, United Kingdom) (45). Analysis of estimated
motion parameters confirmed that overall head motion within
each run did not exceed 2-mm translation or 2° rotation in any
direction. All remaining image processing and statistical analysis
were completed using statistical parametric mapping (SPM2; Well-
come Institute of Cognitive Neurology, London, United Kingdom).
Functional images were spatially normalized to a standard ste-
reotactic space as defined by the Montreal Neurological Institute.
A 6 mm full-width half-maximum Gaussian spatial smoothing
kernel was applied to improve signal-to-noise ratio and to
account for individual differences in anatomy.
Individual analysis was completed using a general linear
model. Three regressors of interest (correct no-go trials, failed
no-go, and all go) were convolved with the canonical hemody-
namic response function, with event durations of 4 sec from
stimulus presentation. Motion parameters were modeled as
nuisance regressors to remove residual motion artifacts. The
main contrast of interest was correct no-go trials versus go trials,
as described in previous studies (28, 46, 47) and was calculated
for second-level group analyses by linearly combining parameter
estimates over all five runs of the task. Failed no-go trials were
not included in the analysis.
A random effects model was used for group-level analyses.
Task effect was first determined with one-sample t tests for each
group. Areas of activation (correct no-go ? go) and deactivation
(go ? correct no-go) were deemed significant if they reached a
threshold of p ? .05, corrected for spatial extent and multiple
comparisons (48). Group effects (FH–, FH? control, FH? prob-
lem) were modeled with a one-way analysis of variance
(ANOVA). Differences within the a priori hypothesized regions
of the prefrontal cortex and striatum were deemed significant if
they reached a statistical threshold of p ? .0001, uncorrected for
multiple comparisons and a cluster extent of 10. Five regions
were found to have a main effect of group. Region of interest
(ROI) masks for these clusters were generated using MarsBaR
ROI toolbox (49). Effect sizes were extracted from parameter
estimates over all five runs and linearly combined for post hoc
analyses, described below.
Performance, DDHx measures, and brain activation were
tested for normality using SPSS. All DDHx variables were found
to be right skewed and kurtotic. Therefore, an inverse transfor-
mation [1/(1 ? the skewed variable)] was performed on these
variables for further analyses. One-wayANOVAswithgroupasthe
main factor were performed separately on performance, DDHx
measures, and YSR behavior problems. Tukey’s post hoc tests were
used to determine significant pairwise differences between groups.
Activation in ROIs were entered into the following post hoc
analyses: 1) Tukey’s post hoc t tests to determine pairwise differ-
ences (FH– vs. FH? control; FH? control vs. FH? problem), 2)
Pearson correlation with YSR externalizing problems and DDHx
variables and exploratory multiple regressions described later, and
3) Pearson correlation with task performance. Bonferroni correction
for multiple comparisons was used to determine significance for
regression analyses (.05/5 brain regions ? .01).
Performance, YSR, DDHx
No differences emerged between groups on performance
measures (Table 1). An effect of group was found in YSR
externalizing problems (F ? 6.8; df ? 57,2; p ? .01); FH?
problem differed from FH– (p ? .002) and approached difference
with FH? control (p ? .06). FH– and FH? control did not differ
(p ? .5). As expected, an effect of group was also found for
M.M. Heitzeg et al.
BIOL PSYCHIATRY 2010;68:287–295 289
drinking volume (F ? 6.7, df ? 58,2; p ? .01), marijuana use (F ?
6.6; df ? 58,2, p ? .01), and number of illicit drugs used (F ? 9.4,
df ? 58,2; p ? .001), with FH? problem differing in all three use
variables from FH? control and FH– (all p ? .01). There were no
differences between FH– and FH? control (all p ? .9).
Effect of task for each group is reported in Figure 1, Table 2.
Each group activated a primarily right-hemisphere network
including prefrontal, posterior parietal, medial temporal, and
median cingulate regions. Patterns of deactivation during re-
sponse inhibition were similar in FH– and FH? control, includ-
ing regions involved in the DMN. FH? problem showed fewer
Whole-Brain ANOVA Results
Group effects were obtained in the left and right medial
orbitofrontal cortex (OFC), the left dorsomedial prefrontal cortex
(mDPFC), left medial prefrontal cortex (mPFC), and the left
ventral caudate (Figure 2, Table 3). Tukey’s post hoc tests
showed that ventral caudate activation differed between FH– and
both FH? groups, whereas it did not differ between the two
FH? groups. This difference was in the direction of greater
deactivation in FH–, but not FH?, groups.
The four prefrontal regions all differed between FH? prob-
lem and both FH? control and FH–, but not between FH– and
FH? control. These differences were in the direction of greater
deactivation in FH– and FH? control but not FH? problem. In
the mDPFC, FH? problem showed activation to correct no-go
relative to go trials (t ? 4.0, uncorrected p ? .0001), although this
did not reach the corrected threshold for significance.
A secondary analysis was conducted in which those in FH?
problem with any diagnosis (AUD, SUD, conduct disorder, or
ADHD; n ? 8, Table 1) were excluded, resulting in n ? 13. All
differences remained significant at p ? .05. This confirms that
effects of problem use in frontal regions were not due to
comorbid diagnoses in FH? problem.
Correlations with YSR, DDHx
Only ventral caudate BOLD response correlated with exter-
nalizing problems (failure to deactivate this region was related to
more externalizing problems). Drinking volume correlated with
activity in the left and right OFC and the mDPFC; number of illicit
drugs correlated with activation in all four prefrontal regions, and
marijuana use correlated with left OFC activity (Table 4).
Multiple regression analyses were conducted to explore these
findings by brain region. Sex and age were entered at the first
step for all analyses but were omitted in subsequent analyses as
neither was a significant contributor to the variance. We set out
to determine: 1) whether family history contributed to the
variance in ventral caudate response above and beyond exter-
nalizing problems and 2) whether alcohol problems contributed
to the variance in each of the four prefrontal regions above and
beyond drinking volume. Marijuana and illicit drug use were left
out of the model because they are strongly correlated with the
Figure 1. Activation and deactivation for each group displayed at a statistical threshold of p ? .005 and minimum cluster size of 10. Each group activated a
primarily right-hemisphere network including prefrontal, posterior parietal, medial temporal, and median cingulate regions. Patterns of deactivation were
The FH? problem group showed fewer regions of significant deactivation during response inhibition—the left precuneus, posterior cingulate and left
hippocampus. FH?, at least one parent with a diagnosis of alcohol use disorder; FH–, no parent with alcohol use disorder; LHPC, left hippocampus; LIPL, left
frontal gyrus; RIPL, right inferior parietal lobe; RMFG, right medial frontal gyrus. Color bars represent t scores.
290 BIOL PSYCHIATRY 2010;68:287–295
M.M. Heitzeg et al.
other variables, and we had no clear hypothesis regarding the
order of their inclusion.
In the ventral caudate, family history contributed significantly
to variance when controlling for externalizing behavior (?R2?
.18, p ? .001; final model: R2? .30, F ? 11.5, p ? .0001; ?EXT?
.21, p ? .08; ?FH? .44, p ? .001). In each prefrontal region,
alcohol problems did not account for significant variability when
controlling for drinking volume (right OFC: ?R2? .02, p ? .32;
left OFC: ?R2? .04, p ? .08; mDPFC: ?R2? .03, p ? .14; mPFC:
?R2? .05, p ? .07).
Correlations with Performance
Across the entire sample, deactivations in mPFC and mDPFC
were correlated with faster RTs (Table 4), indicating that greater
activation to go trials compared with correct inhibitions in these
regions is associated with faster responding.
This study was undertaken to identify differences in frontos-
triatal functioning during response inhibition in adolescents/
young adults with and without a family history of AUD and to
clarify the effects of preexisting vulnerability versus the effects of
problem alcohol involvement. Two main findings were obtained.
First, the FH– group showed a robust deactivation of ventral
caudate during successful inhibition trials, which was not present
in the FH? control and problem groups, suggesting a preexisting
vulnerability factor. Second, medial prefrontal regions were
deactivated during successful inhibition in the groups with low
problem alcohol use regardless of family history, but not in the
FH? problem group. Contrary to our hypotheses, we did not see
evidence of weakened prefrontal responding during inhibition
because of familial risk alone.
Table 2. Activations and Deactivations by Group
FH? Control FH? Problem
R Middle Frontal Gy (BA 9/10/2046)
R Middle Frontal Gy (BA 6/8)
R Inferior Frontal Gy (BA 47)/insula
R Inferior Frontal Gy (BA 44/45)
L Precentral Gy
R Precentral Gy
Median (BA 23/24/31)
R Middle Temporal Gy (BA 22)
R Middle Temporal Gy (BA 21)
R Inferior Parietal lobe (BA 39/40)
L Inferior Parietal lobe (BA 39/40)
L Postcentral Gy
R Postcentral Gy
Medial Orbital Frontal Ctx (BA 10/11)
L Inferior Frontal Gy (BA 47)
Posterior (BA 29)
R Fusiform Gy
Bilateral Lobules 1/6
34, 54, 28 5.440, 60, ?6
46, 24, 46
42, 16, ?10
58, 10, 24
?38, ?14, 60
50, 8, 42
40, 54, 14
32, ?2, 60
38, 14, 6
54, 22, 38
46, 18, ?8 4.7
?38, ?16, 644.6
?2, ?8, 42 5.8
?2, 2, 44 4.28, ?6, 365.8
64, ?28, ?12 4.862, ?38, ?12 3.0a
60, ?34, ?6
50, ?48, 2
48, ?42, 56
?40, ?66, 52
?40, ?22, 54
52, ?48, 52
?44, ?48, 56
48, ?52, 405.6
?38, ?22, 54
54, ?28, 52
?8, 60, ?10
?20, 36, ?10
0, 62, ?16
?36, 34, ?16
?8, ?52, 18
16, ?48, 18
?8, ?52, 10
8, ?50, 10
?8, ?54, 8 4.0
?8, ?40, 8 4.9
36, ?52, ?184.8 28, ?30, ?24 4.5
?12, 24, ?4
?2, 6, ?4
?12, ?20, 20
16, ?28, 18
?30, ?26, ?8
2, ?22, ?14
?16, ?24, 2
16, ?28, 2
?32, ?32, ?8
?16, ?10, ?144.0a
22, ?58, ?20 6.54, ?64, ?167.324, ?60, ?20 6.0
BA, Brodmann’s area; Ctx, cortex; FH?, at least one parent with a diagnosis of alcohol use disorder; FH–, no parent with alcohol use disorder; Gy, gyrus;
L, left hemisphere; R, right hemisphere.
aFalse discovery rate voxel-level corrected p ? .01. Note: these clusters did not reach the stated cluster-level corrected threshold of p ? .05 but were
included to illustrate similarity in pattern of activation across groups.
M.M. Heitzeg et al.
BIOL PSYCHIATRY 2010;68:287–295 291
Each group showed patterns of activation consistent with
those observed in previous studies (50–52): a primarily right-
hemisphere frontoparietal network. Deactivations were found
mainly in the bilateral posterior parietal and inferior frontal
regions in FH– and FH? control groups as previously reported
(30,33). The FH? problem group showed more widespread
activations and fewer regions of deactivation during successful
inhibition. Differences between groups emerged in left prefron-
tal and orbitofrontal regions and the ventral striatum; all areas
showing deactivations in the FH– group.
To understand these findings, the functional significance of
regional deactivations during response inhibition must first be
addressed. Along with task-related activations, Hester et al. (30)
reported deactivations before successful response inhibition in
left medial frontal gyrus, left insula, and caudate in a cued
go/no-go task. Failure to inhibit the medial frontal and insula
regions related to poorer performance. Stevens et al. (31)
investigated functional neural networks underlying response
inhibition and found an inhibitory influence of three distinct
circuits on one another based on task demands. In particular, a
frontoparietal network was found to activate with concurrent
nucleus caudate deactivation, consistent with our findings in
FH?. These findings indicate that successful inhibition requires
both activation of task-related brain regions and deactivation of
irrelevant brain regions. This has been proposed for cognitive
tasks more generally, with the DMN reducing its baseline level of
activity during effortful, task-relevant processing (19,53).
We observed a difference between FH– and FH? control
groups in left ventral caudate, suggestive of a family history
effect, which was present after controlling for externalizing
behavior. The ventral striatum is part of a system involved in
evaluating and responding to motivational stimuli (54) and is
dysregulated in substance abuse (55,56) and other disorders of
impulse control, such as pathological gambling (57) and ADHD
(58,59). In addition, a relationship between impulsivity and
ventral striatal activation to monetary reward in nonpathologic
children (16) and adults (60,61) has been observed. Our finding
that externalizing problems correlated with activation in the
ventral caudate—but did not completely account for the effects
of family history—suggests that the attenuated deactivation of the
left ventral striatum in FH? groups is associated with preexisting
familial risk for AUD through an externalizing pathway.
We also observed a difference between FH? control and
FH? problem in orbitofrontal and medial prefrontal regions,
again following the direction of lesser deactivation in the riskier
group. This finding suggests a dysregulation in the normal
response of these regions during response inhibition in the
problem group, independent of family history. Furthermore,
BOLD response in these regions was related to alcohol consump-
Figure 2. Top: Whole-brain analysis of variance results. Bottom: regions from whole-brain analysis plotted for FH–, FH? control and FH? problem groups.
no parent with alcohol use disorder; L, left; mDPFC, dorsomedial prefrontal cortex; mPFC, medial prefrontal cortex; OFG, orbitofrontal gyrus; R, right. *
Significant difference from both other groups, p ? .05.
Table 3. Whole-Brain Analysis of Variance Results
MNI Space Cluster Voxel-Level
x, y, zExtentPeak F Peak Zp Value
L OFC (BA 10)
R OFC (BA 10)
L Medial PFC (BA 9)
L mDPFC (BA 8)
?8, 62, ?12
14, 50, ?2
?8, 58, 40
?10, 38, 50
?12, 24, ?6
prefrontal cortex; R, right hemisphere.
292 BIOL PSYCHIATRY 2010;68:287–295
M.M. Heitzeg et al.
tion and other drug use, but not externalizing problems. Number
of alcohol problems did not account for additional variability
beyond alcohol consumption, suggesting that the effects of
alcohol (and perhaps other drug involvement) on the brain in the
problem group is responsible for the differences. Indeed, a
number of studies indicate that the frontal lobes are more
vulnerable to alcohol-related impairment than other cortical
regions (62–65). Furthermore, there were no differences be-
tween groups in the right hemisphere, which is traditionally
considered central for inhibitory control (66). Therefore, the
problem group may be recruiting additional left hemisphere
resources—particularly the left dorsomedial PFC, which showed
inhibition-related activation—to inhibit responses successfully,
an effect that is not present in the FH? control group. Increased
bilateral activation during response inhibition has been observed
in the elderly (67) and is typically interpreted as a compensatory
mechanism secondary to age-associated neurodegeneration.
Similar developmental effects are found from childhood to
adulthood, a period during which inhibitory responding shifts
from more diffuse, bilateral regions to focal, right-hemisphere
circuits; occurring concurrent with improvement in response
inhibition (47,68). Thus, one interpretation of our finding is that
alcohol exposure in the problem group is associated with a
disruption of a normally right-lateralized prefrontal cortical sys-
tem involved in response inhibition, necessitating recruitment of
additional resources for successful performance. The overall
pattern of activation was more diffuse and bilateral in this group,
which supports this hypothesis.
It is possible that our findings additionally represent a varia-
tion in the modulation of the DMN during effortful tasks.
Activation in midline structures have been found to decrease as
a function of greater task difficulty (69). Given that the main
differences in our study were in the direction of impaired
deactivation in midline regions involved in the DMN during the
more effortful cognitive task of inhibiting a response versus
continuous responding to go trials, this is a likely explanation.
This was shown recently during spatial working memory relative
to vigilance as a function of family history and may represent an
impaired ability to modulate the DMN in response to increasing
task demands (18).
There are some study limitations that should be mentioned.
First, this study was not specifically design to investigate the
functioning of the DMN. Therefore, interpretations regarding
differences in the functioning of the DMN between groups are
inferential. Confirmation of this hypothesis will need to be borne
out with studies that include rest conditions and differing levels
of cognitive load. Similarly, the contribution of factors typically
linked with a family environment of alcoholism (e.g., stress)
would also need to be addressed in subsequent studies, because
they are beyond the scope of this report. Furthermore, internal-
izing, emotional dysregulation effects have been reported to be
associated with alcoholism and drug use risk (70). No difference
in internalizing problems was evident in this sample. However,
the exclusion of volunteers with diagnoses of mood and anxiety
disorders may have preferentially eliminated those at greatest
risk because of an internalizing pathway, reducing the variability
that could be attributed to this factor. Therefore, future studies
would be necessary to investigate familial risk as it relates to
internalizing symptomatology. Finally, as noted in Supplement 1,
the YSR data were collected approximately 3 years before
scanning. Therefore, these data are not an exact measure of
externalizing problems at the time the functional imaging data
were collected. However, externalizing behavior problems have
been found to be stable through adolescence and young adult-
hood (71–74), suggesting that data collected between ages 15
and 17 is a useful indicator of externalizing behavior throughout
this developmental period.
In conclusion, we found dissociation between circuitry asso-
ciated with vulnerability for AUD and brain regions related to
problem alcohol use. A lesser magnitude of ventral striatal
deactivation during inhibition was associated with preexisting
vulnerability in COA. In contrast, abnormal responding in the
orbital and medial prefrontal regions was observed only in COA
showing problem use and was associated with the magnitude of
alcohol involvement. Findings indicate a preexisting abnormality
in ventral striatal function in youth at risk for AUD, which may
lead to inappropriate motivational responding, and suggest that
with alcohol use, the prefrontal “control” mechanism loses effi-
ciency, further dysregulating the frontostriatal motivational circuitry.
This work was supported by K01 DA020088 to MMH, R01
AA12217 and R37 AA07065 to RAZ, and a National Alliance for
Research on Schizophrenia and Depression Independent Inves-
tigator award to JKZ.
The authors report no biomedical financial interests or po-
tential conflicts of interest.
Supplementary material cited in this article is available
1. National Institute on Alcohol Abuse and Alcoholism (2000): Alcohol
Involvement over the Life Course. NIAAA, Tenth Special Report to the US
Table 4. Correlations with Blood Oxygen Level–Dependent Response
Drinking and Drug UseYSR Performance
Drink VolumeIllicit DrugsMarijuanaExternalizing RTs
L OFC (BA 10)
R OFC (BA 10)
L Medial PFC (BA 9)
L mDPFC (BA 8)
reaction times; YSR, Youth Self-Report.
were all correlated at p ? .0001; rs ranged from .29 for drinking volume and externalizing to .74 for illicit drug use and alcohol problems.
M.M. Heitzeg et al.
BIOL PSYCHIATRY 2010;68:287–295 293
Congress on Alcohol and Health: Highlights from Current Research.
Bethesda, MD: Department of Health and Human Services, 28–53.
New York: Guilford, 9–38.
3. Caspi A, Moffitt TE, Newman DL, Silva PA (1996): Behavioral observa-
tions at age 3 years predict adult psychiatric disorders. Longitudinal
evidence from a birth cohort. Arch Gen Psychiatry 53:1033–1039.
4. Cloninger CR, Sigvardsson S, Bohman M (1988): Childhood personality
5. Chassin L, Ritter J (2001): Vulerability to substance use disorders in
6. Zucker R (2000): Alcohol involvement over the life course. In: Na-
tional Institute on Alcohol Abuse and Alcoholism. Tenth Special Re-
Research. Bethesda, MD: Department of Health and Human Services,
7. Sher KJ, Walitzer KS, Wood PK, Brent EE (1991): Characteristics of chil-
dren of alcoholics: Putative risk factors, substance use and abuse, and
tal-biopsychosocial systems formulation covering the life course. In:
Cicchetti D, Cohen DJ, editors. Developmental Psychopathology, 2nd ed.
Hoboken, NJ: Wiley, 620–656.
9. Sher KJ, Trull TJ (1994): Personality and disinhibitory psychopathology:
et al. (2009): Behavioral disinhibition: Liability for externalizing spec-
trum disorders and its genetic and environmental relation to response
inhibition across adolescence. J Abnorm Psychol 118:117–130.
AB, et al. (1997): Implication of right frontostriatal circuitry in response
inhibition and attention-deficit/hyperactivity disorder. J Am Acad Child
et al. (1998): Selective effects of methylphenidate in attention deficit
hyperactivity disorder: A functional magnetic resonance study. Proc
13. Aron AR, Poldrack RA (2005): The cognitive neuroscience of response
inhibition: Relevance for genetic research in attention-deficit/hyperac-
tivity disorder. Biol Psychiatry 57:1285–1292.
C, et al. (2004): An FMRI study of response inhibition in youths with a
15. McNamee RL, Dunfee KL, Luna B, Clark DB, Eddy WF, Tarter RE (2008):
Brain activation, response inhibition, and increased risk for substance
vation in adolescent children of alcoholics. Addiction 103:1308–1319.
17. Heitzeg MM, Nigg JT, Yau WY, Zubieta JK, Zucker RA (2008): Affective
tostriatal responses between vulnerable and resilient children of alco-
18. Spadoni AD, Norman AL, Schweinsburg AD, Tapert SF (2008): Effects of
19. Greicius MD, Krasnow B, Reiss AL, Menon V (2003): Functional connec-
tivity in the resting brain: A network analysis of the default mode hy-
20. Knutson B, Adams CM, Fong GW, Hommer D (2001): Anticipation of
increasing monetary reward selectively recruits nucleus accumbens.
21. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE (2009): Moni-
College Students and Adults Ages 19–50. Washington, DC: US Govern-
ment Printing Office.
22. Spear LP, Varlinskaya EI (2005): Adolescence. Alcohol sensitivity, toler-
ance, and intake. Recent Dev Alcohol 17:143–159.
alcohol dependence: Neural measures of stop signal performance. Al-
24. Desmond JE, Chen SH, DeRosa E, Pryor MR, Pfefferbaum A, Sullivan EV
working memory: An fMRI study. Neuroimage. 19:1510–1520.
25. Pfefferbaum A, Desmond JE, Galloway C, Menon V, Glover GH, Sullivan
EV (2001): Reorganization of frontal systems used by alcoholics for
spatial working memory: An fMRI study. Neuroimage. 14:7–20.
et al. (2004): Blood oxygen level dependent response and spatial work-
ing memory in adolescents with alcohol use disorders. Alcohol Clin Exp
27. Durston S, Thomas KM, Worden MS, Yang Y, Casey BJ (2002): The effect
28. Durston S, Tottenham NT, Thomas KM, Davidson MC, Eigsti IM, Yang Y,
with and without ADHD. Biol Psychiatry 53:871–878.
29. Durston S, Thomas KM, Yang Y, Ulug AM, Zimmerman RD, Casey BJ
30. Hester RL, Murphy K, Foxe JJ, Foxe DM, Javitt DC, Garavan H (2004):
to response inhibition. J Cogn Neurosci 16:776–785.
ral networks underlying response inhibition in adolescents and adults.
32. Gruber SA, Yurgelun-Todd DA (2005): Neuroimaging of marijuana
smokers during inhibitory processing: A pilot investigation. Brain Res
33. Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA,
Yang TT, et al. (2007): Functional MRI of inhibitory processing in absti-
nent adolescent marijuana users. Psychopharmacology 194:173–183.
34. Zucker RA, Fitzgerald HE, Refior SK, Puttler LI, Pallas DM, Ellis DA (2000):
The clinical and social ecology of childhood for children of alcoholics:
In: Fitzgerald HE, Lester BM, Zucker RA, editors. Children of Addiction:
Research, Health and Policy Issues. New York: RoutledgeFalmer, 109–
35. Oldfield RC (1971): The assessment and analysis of handedness: The
Edinburgh inventory. Neuropsychologia 9:97–113.
36. Krueger RF (1999): Personality traits in late adolescence predict mental
disorders in early adulthood: A prospective-epidemiological study. J
37. Costello A, Edelbrook C, Dulcan M, Kalas R, Klanc S (1984): Development
and Testing of the NIMH Diagnostic Interview Schedule for Children in a
tional Institute of Mental Health.
38. Robins L, Cottler LB, Bucholz KK, Compton WM, North CS, M, RK (2000):
Diagnostic Interview Schedule for the DSM-IV (DSM-IV). St. Louis, MO:
Washington University School of Medicine.
39. Zucker R, Fitzgerald H, Noll R (1990): Drinking and Drug History, rev. ed.,
Version 4. Ann Arbor: University of Michigan Department of Psychiatry,
Addiction Research Center.
40. Zucker RA, Fitzgerald HE (1994): Drinking and drug history form for chil-
dren. Ann Arbor: University of Michigan Department of Psychiatry, Ad-
diction Research Center.
41. Achenbach TM (1991): Manual for the youth self-report form and 1991
profile. Burlington, VT: University Associates in Psychiatry.
42. Glover GH, Law CS (2001): Spiral-in/out BOLD fMRI for increased SNR
and reduced susceptibility artifacts. Magn Reson Med 46:515–522.
43. Sutton BP, Noll DC, Fessler JA (2003): Fast, iterative image reconstruc-
tion for MRI in the presence of field inhomogeneities. IEEE Trans Med
44. Noll DC, Fessler JA, Sutton BP (2005): Conjugate phase MRI reconstruc-
tion with spatially variant sample density correction. IEEE Trans Med
45. Jenkinson M, Bannister P, Brady M, Smith S (2002): Improved optimiza-
tion for the robust and accurate linear registration and motion correc-
tion of brain images. Neuroimage. 17:825–841.
46. Schulz KP, Fan J, Tang CY, Newcorn JH, Buchsbaum MS, Cheung AM, et
deficit hyperactivity disorder during childhood: An event-related FMRI
294 BIOL PSYCHIATRY 2010;68:287–295
M.M. Heitzeg et al.
48. Friston KJ, Frith CD, Liddle PF, Frackowiak RSJ (1991): Comparing func-
49. Brett M, Anton J-L, Valabregue R, Poline JB (2002): Region of interest
ence on Functional Mapping of the Human Brain; June 2–6, 2002;
50. Aron AR, Poldrack RA (2006): Cortical and subcortical contributions to
stop signal response inhibition: Role of the subthalamic nucleus.J Neu-
51. Liddle PF, Kiehl KA, Smith AM (2001): Event-related fMRI study of re-
52. Garavan H, Ross TJ, Stein EA (1999): Right hemispheric dominance of
GL (2001): A default mode of brain function. Proc Natl Acad Sci U S A
54. Chambers RA, Taylor JR, Potenza MN (2003): Developmental neurocir-
55. Bjork JM, Smith AR, Hommer DW (2008): Striatal sensitivity to reward
56. Volkow ND, Fowler JS, Wang GJ, Swanson JM, Telang F (2007): Dopa-
ment implications. Arch Neurol 64:1575–1579.
57. Reuter J, Raedler T, Rose M, Hand I, Glascher J, Buchel C (2005): Patho-
logical gambling is linked to reduced activation of the mesolimbic
reward system. Nat Neurosci 8:147–148.
58. Volkow ND, Wang GJ, Newcorn J, Telang F, Solanto MV, Fowler JS, et al.
(2007): Depressed dopamine activity in caudate and preliminary evi-
hyporesponsiveness during reward anticipation in attention-deficit/
hyperactivity disorder. Biol Psychiatry 61:720–724.
60. Hariri AR, Brown SM, Williamson DE, Flory JD, de Wit H, Manuck SB
(2006): Preference for immediate over delayed rewards is associated
with magnitude of ventral striatal activity. J Neurosci 26:13213–13217.
61. Hahn T, Dresler T, Ehlis AC, Plichta MM, Heinzel S, Polak T, et al. (2009):
Neural response to reward anticipation is modulated by Gray’s impul-
sivity. Neuroimage. 46:1148–1153.
62. Pfefferbaum A, Sullivan EV, Mathalon DH, Lim KO (1997): Frontal lobe
volume loss observed with magnetic resonance imaging in older
63. Gilman S, Koeppe RA, Adams K, Johnson-Greene D, Junck L, Kluin KJ, et
al. (1996): Positron emission tomographic studies of cerebral benzo-
diazepine-receptor binding in chronic alcoholics. Ann Neurol
al. (2000): Hypoperfusion of inferior frontal brain regions in abstinent
alcoholics: A pilot SPECT study. J Stud Alcohol 61:32–37.
65. Oscar-Berman M, Marinkovic K (2007): Alcohol: Effects on neurobehav-
ioral functions and the brain. Neuropsychol Rev 17:239–257.
66. Garavan H, Hester R, Murphy K, Fassbender C, Kelly C (2006): Individual
differences in the functional neuroanatomy of inhibitory control. Brain
67. Nielson KA, Langenecker SA, Garavan H (2002): Differences in the func-
tional neuroanatomy of inhibitory control across the adult life span.
68. Tamm L, Menon V, Reiss AL (2002): Maturation of brain function associ-
ated with response inhibition. J Am Acad Child Adolesc Psychiatry 41:
69. Greicius MD, Menon V (2004): Default-mode activity during a passive
sensory task: Uncoupled from deactivation but impacting activation. J
70. Mezzich A, Tarter R, Kirisci L, Clark D, Buckstein O, Martin C (1993):
self-reported problem behaviors from adolescence into young adult-
of deviance in late adolescence and early adulthood. Am Sociol Rev
73. Verhulst FC, van Wattum PJ (1993): Two-year stability of self-reported
problems in an epidemiological sample of adolescents. Acta Psychiatr
74. Reitz E, Dekovic M, Meijer AM (2005): The structure and stability of
externalizing and internalizing problem behavior during early adoles-
M.M. Heitzeg et al.
BIOL PSYCHIATRY 2010;68:287–295 295