Prefrontal Cortex, Thalamus, and Cerebellar Volumes in ...

0145-6008/05/2909-1590$03.00/0
ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH
Prefrontal Cortex, Thalamus, and Cerebellar Volumes
in Adolescents and Young Adults with Adolescent-Onset
Alcohol Use Disorders and Comorbid Mental Disorders
Michael D. De Bellis, Anandhi Narasimhan, Dawn L. Thatcher, Matcheri S. Keshavan, Paul Soloff, and Duncan B. Clark
Background: In adults, prefrontal, thalamic, and cerebellar brain injury is associated with excessive
ethanol intake. As these brain structures are actively maturing during adolescence, we hypothesized that
subjects with adolescent-onset alcohol use disorders, compared with control subjects, would have smaller
brain volumes in these areas. Thus, we compared prefrontal-thalamic-cerebellar measures of adolescents
and young adults with adolescent-onset alcohol use disorders (AUD, defined as DSM-IV alcohol dependence
or abuse) with those of sociodemographically similar control subjects.
Methods: Magnetic resonance imaging was used to measure prefrontal cortex, thalamic, and cerebellar
volumes in 14 subjects (eight males, six females) with an AUD (mean age, 17.0 � 2.1 years) and 28 control
subjects (16 males, 12 females; 16.9 � 2.3 years). All AUD subjects were recruited from substance abuse
treatment programs and had comorbid mental disorders.
Results: Subjects with alcohol use disorders had smaller prefrontal cortex and prefrontal cortex white
matter volumes compared with control subjects. Right, left, and total thalamic, pons/brainstem, right and
left cerebellar hemispheric, total cerebellar, and cerebellar vermis volumes did not differ between groups.
There was a significant sex-by-group effect, indicating that males with an adolescent-onset AUD compared
with control males had smaller cerebellar volumes, whereas the two female groups did not differ in
cerebellar volumes. Prefrontal cortex volume variables significantly correlated with measures of alcohol
consumption.
Conclusions: These findings suggest that a smaller prefrontal cortex is associated with early-onset
drinking in individuals with comorbid mental disorders. Further studies are warranted to examine if a
smaller prefrontal cortex represents a vulnerability to, or a consequence of, early-onset drinking.
Key Words: Alcohol Use Disorders, Alcohol Abuse or Dependence, Prefrontal Cortex, Cerebellum,
Adolescence.
INTRODUCTION
ADOLESCENT-ONSET ALCOHOL USE disorders
(AUD, defined as DSM-IV alcohol dependence or
abuse) are prevalent and serious problems among adolescents
(Clark, 2004; Johnston et al., 2003; Rohde et al.,
1996). Adult AUD usually begin during adolescence (Wagner
and Anthony, 2002). Studies focusing on brain structure
and function associated with ethanol use have been
From the Department of Psychiatry and Behavioral Sciences, Duke University
Medical Center, Durham, North Carolina; and Western Psychiatric
Institute and Clinic, University of Pittsburgh Medical Center, Pittsburgh,
Pennsylvania .
Received for publication February 19, 2005; accepted June 6, 2005.
Presented in part at the 2002 Annual Meeting of the American Psychiatric
Association, May 2002.
Supported in part by NIMH grants K08MHO1324 and RO1AA12479
(MDB), RO1MH01180 and RO1MH43687 (MSK), and K02AA00291 and
R01DA14635 (DBC) and P50AA08746 (DBC, PS).
Reprint requests: Dr. Michael D. De Bellis, Department of Psychiatry and
Behavioral Sciences, Duke University Medical Center, Box 3613, Durham,
NC, 27710; Fax: 919-419-0165; E-mail: debel002@mc.duke.edu.
Copyright © 2005 by the Research Society on Alcoholism.
DOI: 10.1097/01.alc.0000179368.87886.76
Vol. 29, No. 9
September 2005
done primarily in adults. Several studies demonstrated
adult AUD is associated with abnormalities of the prefrontal
cortex (PFC) (Pfefferbaum et al., 1997), thalamus (Sullivan
et al., 2003), and the cerebellar hemispheres (Nicolas,
2000; Torvik, 1986). Studies have demonstrated frontal
gray and white matter neuroanatomical deficits primarily in
the adult samples (Harper, 1987; Pfefferbaum et al., 1997).
One study showed differences between younger (age, 26 to
44 years) and older alcoholics (age, 45 to 63 years); the
older group had cortical volume deficits in both gray and
white matter with greatest tissue volume loss occurring in
the frontal lobes, whereas the younger group had deficits in
only the gray matter (Pfefferbaum et al., 1997). A prominent
finding in adult alcohol-dependent patients is hypometabolism
in the medial frontal region of the cerebral
cortex (Gilman et al., 1990). In another study, smaller
volumes of prefrontal gray but not white matter were seen
in 25 men, 22 to 41 years of age, with polysubstance use
disorders (the majority of subjects used cocaine, but 13 of
the 25 drank six or more drinks a week) compared with 14
healthy volunteers (Liu et al., 1998). Smaller thalami and
pons (Sullivan et al., 2003), cerebellar neuronal loss (Baker
1590 Alcohol Clin Exp Res, Vol 29, No 9, 2005: pp 1590–1600

BRAIN VOLUMES IN YOUTHS 1591
et al., 1999), and compromised pontocerebellar and cerebellothalamocortical
systems (Sullivan 2003) were reported
in adults with an AUD.
PFC and the cerebellar hemispheres are brain regions
that undergo active developmental changes during adolescence
(Durston et al., 2001; Giedd et al., 1999; Paus et al.,
2001; Pfefferbaum et al., 1994; Thompson et al., 2000). The
acquisition of executive cognitive functions, which reflect
the maturation of the prefrontal-thalamic-cerebellar structures,
encompass higher cognitive functions (Miller and
Cohen, 2001; Schmahmann, 1997). Adolescents with AUD
show deficits in cognitive functions, including lower IQ and
achievement scores in reading than control subjects (Moss
et al., 1994) and neurocognitive deficits in attention, visuospatial,
and memory functioning (Brown et al., 2000). In a
prospective study of adolescents with AUD, ages 14 to 17
years, many of whom had at least one other substance use
disorder (60% had significant cannabis use), substance use
disorders predicted poorer performances on tests of memory
and attention (Tapert et al., 2002b). On the other hand,
subjects who were offspring of individuals with alcoholism,
and so were at high familial risk for an AUD, perform
poorly on measures of executive cognitive function (Giancola
et al., 1996; Harden and Pihl 1995; Hill, 2004; Tarter et
al., 1999). This raises speculation that as a result of inherent
prefrontal-thalamic-cerebellar vulnerabilities, there may be
a predisposition to an adolescent-onset AUD (Wiers et al.,
1994).
Consequently, as prefrontal-thalamic-cerebellar structures
are actively maturing during adolescence, we compared
volume measures of these regions of interest in
adolescents and young adults with adolescent-onset AUD
with those of matched comparison subjects. It was hypothesized
that adolescents with an AUD will have structural
deficits in these structures compared with sociodemographically
similar healthy control subjects. Given that
adult females with an AUD may show disproportionately
negative effects on brain structure and function from excessive
drinking than adult males with an AUD (Harper et
al., 1990; Hommer et al., 1996; Hommer, 2003; Mann, et
al., 1992), a comparison of sex differences was also planned.
MATERIALS AND METHODS
Subjects
Adolescents (defined as age 13 to 17 years) and young adults (defined
as age 18 to 21 years) with an adolescent-onset AUD and healthy comparison
subjects were recruited (Table 1). Because of the high degree of
known variability in volume of brain structures (Lange et al., 1997), two
healthy comparison subjects were case-matched for each subject with an
AUD for age (within six months), sex, and handedness. Groups were
similar on height, weight, socioeconomic status (SES) and full-scale IQ.
The comparison group was recruited by advertisement from the community
and these subjects had no lifetime histories of psychiatric disorders,
including alcohol and substance use disorders.
Table 1. Demographic Characteristics of Subjects with an Adolescent Onset AUD and Matched Comparison Subjects
AUD AUD males AUD females Control Control males Control females AUD vs.Controls p
N 14 8 6 28 16 12 X2�0 NS
Age (years)(range in years) 17.0 � 2.1 (14.0 - 20.6) 16.4 � 2.1 (14.0 - 19.1) 17.8 � 2.0 (15.0 - 20.9) 16.9 � 2.3 (13.5 - 21) 15.9 � 1.9 (13.5 - 19.7) 18.1 � 2.2 (15.5 -21) T40� 0.21 0.84
Weight (kg) 68.5 � 11.5 66.0 � 13.2 71.9 � 8.8 68.4 � 17.6 71.8 � 17.8 64.0 � 17.2 T40� 0.02 0.99
Height (cm) 169.6 � 11.0 173.9 � 13.1 163.8 � 2.3 170.1 � 10.2 174.5 � 8.1 164.3 � 10.0 T40�-0.16 0.88
SES 34.3 � 7.5 32.0 � 5.8 37.7 � 8.5 37.8 � 8.8 38.6 � 8.1 36.8 � 9.9 T40�-1.35 0.18
Fullscale IQ 105.6 � 13.0 107.6 � 12.8 103 � 14.1 111.8 � 16.4 112 � 15.9 111 � 17.8 T40�-1.24 0.22
Age of onset of AUD (years) 15.6 � 2.4 14.6 � 2.7 16.6 � 2.2 ––
Duration of AUD (years) 1.4 � 0.7 1.5 � 0.6 1.2 � 0.9 ––
Average number of drinks
7.3 � 3.0 (3 - 13) 7.6 � 2.6 (5 - 13) 6.9 � 3.7 (3 - 13) 0.27 � 0.38 (0 - 1) 0.25 � 0.33 (0 - 1) 0.30 � 0.48 (0 - 1)
per drinking occasion(range)
Number of drinks per maximum 12.1 � 6.8 (5.0 - 25) 13.6 � 7.9 (6.0 - 25) 10.0 � 5.0 (5.0 - 18) 0.54 � 0.8 (0 - 2) 0.5 � 0.7 (0 - 2) 0.6 � 0.9 (0 - 2)
drinking episode(range)
Yearly alcohol quantity/frequency
grouping (number in each group)
Abstainers
0
0
0
18
8
10
Less than 1 drink/week
0
0
0
6
4
2
Less than 0.5 drink/day
2
1
1
4
4
0
0.5 to 1.5 drinks/day
4
2
2
0
0
0
2.0� drinks/day
8
5
3
0
0
0
SES, socioeconomic status.

1592 DE BELLIS ET AL.
Clinical Evaluation
Substance use disorder diagnoses were determined using a modified
form of the Structured Clinical Interview for the DSM-IV (Martin et al.,
2000). Information was gathered by direct interviews because family informants
typically underreport alcohol and drug involvement (Kosten et
al., 1992). For each symptom, ages of onset were recorded to the nearest
month. The interviewers had Masters-level education in mental healthrelated
fields and were individually trained to obtain greater than 90%
agreement with an experienced interviewer. Interrater reliabilities were
high for individual DSM-IV symptoms (� � 0.84 to 1.0) and for AUD
diagnoses (� � 0.94) (Martin et al., 2000). Other Axis I mental disorders
were assessed using a modified version of the Schedule for Affective
Disorders and Schizophrenia for School-Age, Present Episode (K-
SADS-P) (Chambers et al., 1985) and Lifetime Version (K-SADS-E)
(Orvaschel and Puig–Antich, 1987) interview, with both adolescent and
parent(s) as informants. An expanded assessment of posttraumatic stress
disorder (PTSD) was completed as part of the K-SADS Present and
Lifetime Version (Kaufman et al., 1997). Consensus diagnoses were
reached among the interviewer, the assessment coordinator, and a clinically
experienced faculty psychiatrist or psychologist using the best estimate
method (Clark, 1999; Kosten and Rounsaville, 1992), in which a date
of onset, defined as the time at which diagnostic criteria were first met, is
determined for each disorder (Clark et al., 2001). The Lifetime History of
Alcohol Use Interview (Skinner, 1982) was used to collect supplemental
information on alcohol and other abused substances, including the average
quantity and frequency of use and the maximum frequency and
quantity of use for alcohol and seven other drug classes (stimulates
[caffeine, nicotine], sedatives [barbiturates], opioids [heroin, morphine,
codeine], hypnotics [Valium], hallucinogens/PCP, cannabis [marijuana],
inhalants) for each year of a subject’s life. The Alcohol Consumption
Questionnaire (ACQ), which was based on survey instruments that Cahalan
(1981) developed, was used to measure the quantity and frequency
of alcohol consumption in the past year (Cahalan, 1981). The ACQ is a
self-report inventory designed to categorize the average frequency, average
quantity, maximum quantity, frequency of maximum quantity, and
type of alcohol used in the past 12 months to form a yearly alcohol
quantity/frequency grouping (Table 1). The quantity and frequency of
alcohol consumption and other alcohol consumption variables were measured
by a questionnaire using face valid items found to have acceptable
psychometric properties in other studies (Grant et al., 1995; Hasin and
Carpenter, 1998). Quantity variables were based on 0.6-oz. (17-g) ethanol
“standard drinks” (one 12-oz. [340-g] beer, one 5-oz. [142-g] glass of table
wine [12% alcohol by volume], or one 1.5 oz. [42.5 g] of 80-proof hard
liquor), and the frequency variables were calculated as number of occasions
per month.
The alcohol use disorder group consisted of nine with lifetime alcohol
dependence (five males, four females) and five with lifetime alcohol abuse
(three males, two females). AUD subjects were recruited from treatment
programs in the Pittsburgh Area. Comorbidity is common in adolescent
AUD; other studies have shown that a substantial percentage of adolescents
with AUD are comorbid with other substance use disorders (particularly
cannabis), conduct disorder, attention deficient hyperactivity disorder,
mood disorders, and posttraumatic stress disorder (Clark et al., 1997).
In our sample, subjects had a mean of 4.1 � 2.4 and range of 2 to 9 lifetime
Axis I disorders. Comorbidity included the following: other substance
abuse or dependence (cannabis [n � 11], eight males, three females) or
hallucinogens (n � 2, males), major depressive disorder (n � 10; five
males, five females), conduct disorder (n � 8; six males, two females),
posttraumatic stress disorder (n � 7; five males, two females), attentiondeficit
hyperactivity disorder (n � 7; six males, one female), oppositional
defiant disorder (n � 2; two males), generalized anxiety disorder (n � 2;
two females), and bipolar disorder (n � 1, male). It should be noted that
there was overlap in that all AUD subjects with ADHD had conduct
disorder or oppositional defiant disorder, that all AUD subjects with
PTSD except one had major depression, and all AUD subjects with major
depression had a cannabis use disorder except three. The age of onset for
Table 2. Clinical characteristics of adolescents and young adults with an
adolescent-onset AUD
AUD AUD males
(n � 8)
AUD females
(n � 6)
Alcohol abuseAlcohol
dependence
59 35 24
Number of co-morbid
axis I disorders
4.1 � 2.4 4.6 � 1.8 3.3 � 3.0
Number with CUD 11 8 3
Hallucinogen abuse 2 2 0
Number with MDD 10 5 5
Number with PTSD 7 5 2
Number with ADHD 7 6 1
Number with CD 8 6 2
Number with ODD 2 2 0
Number with GAD 2 0 2
Number with bipolar disorder 1 1 0
CUD, Cannabis use disorder; MDD, major depressive disorder; PTSD, posttraumatic
stress disorder; ADHD, attention deficit hyperactivity disorder; CD,
conduct disorder; ODD, oppositional defiant disorder; GAD, generalized anxiety
disorder.
an alcohol use disorder was 15.6 � 2.4 years. Although the mean age of
onset for the 11 subjects with cannabis use disorder was 15.0 � 2.2 years,
for nine of these subjects the alcohol use disorder diagnosis preceded their
cannabis use disorder. Some comorbid disorders preceded (ADHD) or
co-occurred with the AUD (major depression, conduct disorder, bipolar
disorder). See Table 2. Brain structural (cerebral, amygdala, hippocampal,
lateral ventricle) volumes and corpus callosum areas for 12 of the 14
subjects with alcohol use disorder and 24 of the 28 healthy comparison
subjects were previously reported (De Bellis et al., 2000a).
Subjects were excluded from the study if the following were found: 1)
AUD subjects were admitted to the University of Pittsburgh Medical
Center’s General Clinical Research Center to ensure that they did not use
drugs or alcohol before the MRI scan. Thus, AUD subjects were excluded
from the study if the following were found: the use of drugs during the two
weeks before the MR scan (confirmed by a negative urine drug screen 12
hours before the MRI scan), and the use of alcohol within 12 hours of the
MRI scan (confirmed by a negative alcohol breathalyzer test); 2) presence
of a significant medical or neurological illness; 3) gross obesity (weight
greater than 150% of ideal body weight) or growth failure (height under
third percentile); 4) full-scale intelligence below 80, as estimated by the
short form of the Wechsler Intelligence Scale for Children, Third Edition,
or the Wechsler Adult Intelligence Scale, Third Edition; 5) pregnancy; and
6) insufficient English skills for consenting to the protocol. This protocol
was approved by the University of Pittsburgh Institutional Review Board.
Subjects or their parent(s) or legal guardian(s) gave written informed
consent. Adolescents under age 14 years assented before participating in
this protocol. Adolescents 14 years of age and older gave written informed
consent along with the written informed consent of their parent or legal
guardian. Subjects aged 18 years or older gave written informed consent.
Thus, no subject was consented to participate independently of a parent or
legal guardian. Subjects received monetary compensation for participation.
MRI Acquisition
All volumetric MRI scans were performed with the use of a GE
1.5-Tesla Unit (Signa System, General Electric Medical Systems, Milwaukee,
WI) running version 5.4 software located at the University of Pittsburgh
Medical Center (UPMC) MR Research Center. The subject’s head
was aligned in a head holder with foam padding, using soft towels and chin
and forehead straps to minimize head movement. The subject’s nose was
positioned at the 12 o’clock position for alignment, and a gradient echo
localizing axial slice verified this plane. A sagittal series (using TE � 18
msec, TR � 400 msec, flip angle � 90 degrees, acquisition matrix � 256
� 192, NEX � 1, FOV � 20 cm, slices � 21) verified patient position,

BRAIN VOLUMES IN YOUTHS 1593
cooperation, and image quality. We required that the midsagittal slice
show full visualization of the cerebral aqueduct and the anterior and
posterior commissures, in which a line was estimated requiring the
anterior-commissure-posterior-commissure line to be within three degrees
of 180. If these criteria were not met, the subject was realigned until these
criteria were met. A three-dimensional spoiled gradient recalled acquisition
in the steady-state pulse sequence was used to obtain 124 contiguous
images with slice thickness of 1.5 mm in the coronal plane for region of
interest measures (TE � 5 msec, TR � 25 msec, flip angle � 40 degrees,
acquisition matrix � 256 � 192, number of excitations � 1, field of vision
� 24 cm). Coronal sections were obtained perpendicular to the anteriorcommissure-posterior-commissure
line to provide a more reproducible
guide for image orientation. Axial proton density and T 2-weighted images
were obtained to enable exclusion of structural abnormalities on MRI. A
neuroradiologist reviewed all scans and ruled out clinically significant
abnormalities. All subjects tolerated the procedure well. No sedation was
used.
The imaging data from the coronal sections were transferred from the
MRI unit to a computer workstation (PowerMacintosh, Apple Computer)
and analyzed with the use of IMAGE software (version 1.61) developed at
the NIH (Rasband, 1996) that provides valid and reliable volume measurements
of specific structures using a manually operated (hand tracing)
approach. Trained and reliable raters who were blinded to subject information
made all measurements. These methods were previously described
by our group (De Bellis et al., 1999; De Bellis et al., 2002; De Bellis et al.,
2001) and are briefly presented here.
Intracranial volumes were calculated by first manually tracing the
intracranial volume of each coronal slice after exclusion of skull and dura,
then summing these areas of successive coronal slices, including GM and
WM and cerebral spinal fluid (CSF) volumes, and multiplying by slice
thickness. These measures included frontal, parietal, temporal, occipital
cortex, subcortical structures, cerebellum, and brainstem.
Cerebral volumes were measured after manual exclusion of CSF volumes,
cerebellum, and brainstem in the same manner and included cortical
and subcortical structures.
Prefrontal cortex volumes were calculated by summing up areas of
successive coronal slices, including gray and white matter and CSF volumes
and multiplying by slice thickness. The anterior boundary of the
prefrontal cortex was defined as the most anterior coronal section containing
gray matter. The coronal slice showing the genu of the corpus
callosum was used to mark the posterior limit of the prefrontal cortex
(Rosenberg et al., 1997). Prefrontal white and gray matter volumes were
calculated by using a semiautomated segmentation algorithm. This computerized
segmentation technique uses mathematically derived cutoffs for
gray matter–white matter–CSF partitions with histograms of signal intensities.
This computerized segmentation technique is both labor intensive
and manually operated. It uses an interactive method in which mathematically
derived cutoffs for gray matter–white matter–CSF partitions from
histograms of signal intensities are used to individually select gray matter,
white matter, and CSF areas from each coronal slice. Gray matter, white
matter, and CSF areas are thus separately calculated and multiplied by
slice thickness for the individual subject’s gray matter, white matter, and
CSF volumes. In this way, we can minimize the inherent limitations on
qualifying white matter signal hypointensities as gray matter on T 1weighted
MRI scans by visual inspection of slices (i.e., so that hypointensity
artifacts in corpus callosum or cerebral white matter are not calculated
as gray matter). This approach has been validated by using both a stereology
technique for brain morphometric measurements and a phantom
with known absolute volumes (Keshavan et al., 1995) and has been used in
several published neuroimaging studies (De Bellis et al., 1999; De Bellis et
al., 2001; Rosenberg et al., 1997). See Fig. 1.
The boundaries of the thalamus were previously described (Gilbert et
al., 2000). The boundaries of the thalamus were defined as follows: The
mammillary bodies and the interventricular foramen defined the anterior
boundary; the internal capsule defined the lateral boundary; the third
ventricle defined the medial boundary; the hypothalamus defined the
inferior boundary; the lateral ventricle defined the superior boundary; and
Fig. 1. Manual tracings of PFC. PFC volumes were calculated by summing up
areas of successive coronal slices and multiplying by slice thickness. The anterior
boundary of the prefrontal cortex was defined as the most anterior coronal
section containing gray matter. The coronal slice showing the genu of the corpus
callosum was used to mark the posterior limit of the prefrontal cortex. Prefrontal
white and gray matter volumes were calculated by using a semiautomated
segmentation algorithm (not shown).
the crus fornix defined the posterior boundary of the thalamus. Every
coronal slice that included the thalamus was manually traced, and total
volume was computed by summing up successive areas and multiplying by
slice thickness.
The volumes of the cerebellum, vermis, and brainstem were calculated
by summing up areas of successive coronal slices after tracing the region
of interest and excluding CSF as previously described (Hardan et al.,
2001). Briefly, measurements began as the cerebellum appeared laterally
to the pons. The tentorium cerebelli acted as the superior limit and the
base of the cerebellum itself as the inferior limit. The cisterna magna and
transverse sinus were excluded. The last slice included was the one at
which the cerebellum was no longer distinguishable from the transverse
sinus or disappeared. The measurement of the vermis began at the slice
where the anterior and/or inferior posterior lobes appeared. The gray
matter of the vermis structures, determined by mathematically derived
cutoffs for gray matter–white matter–CSF partitions from histograms of
signal intensities as previously described (De Bellis et al., 2001), were
traced separately until the slice where the fourth ventricle was no longer
visible. The cerebellar medullary body and the deep cerebellar nuclei were
excluded. Measurements were made around the vermis until it was no
longer visible. The first slice of the brainstem was measured where the
pons first appeared within the suprasellar cistern. The cerebellar peduncles,
including the brachium pontis (middle), were included in the
brainstem. In the anterior plane, the superior limit of the pons was a
straight line connecting the ambient cisterns from left to right. Posterior
measurements included the cerebral aqueduct and the superior colliculus.
No separate volumetric measurements were made for the midbrain, pons,
and medulla oblongata. See Fig. 2.
Intraclass correlation of interrater and intrarater reliability for independent
designation of regions on segmented images obtained from 20
subjects were 0.99 and 0.99 for intracranial volume, cerebral volume,
cerebral CSF, prefrontal lobe volume, prefrontal lobe gray matter, prefrontal
lobe white matter, prefrontal CSF; 0.96 and 0.98 respectively, for
right, left, and total thalamus volumes; 0.91 and 0.95 respectively, for right,
left, and total cerebellum; 0.89 and 0.94 respectively, for pons/brainstem;
and 0.91 and 0.92 respectively, for cerebellar vermis.
Statistical Methods
Demographic variables were compared by means of Student’s t test and
Pearson 2 as appropriate. Histograms were obtained to assess normality of
the data and to observe any outlying observations. There were no significant
outliers, and we did not exclude any cases in our data analyses.
Formal hypothesis testing was carried out by t tests in two stages, first with
the raw data, then again adjusting for total cerebral volume, to determine
differences between AUD subjects and control subjects. In testing for
covariate effects such as sex and interactions (sex-by-group), multivariate
regression analysis was used. Brain structural means, which differed significantly
between the groups (e.g., PFC volumes), were adjusted for
cerebral volume to correct for individual differences in brain size with
regard to gender and then correlated with clinical data using Spearman
correlation coefficients. All significance testing involving the main hypoth-

1594 DE BELLIS ET AL.
Fig. 2. Manual tracings of cerebellum, vermis, and brainstem. The volumes of the cerebellum, vermis, and brainstem were calculated by summing up areas of
successive coronal slices after tracing the region of interest. Measurements began as the cerebellum appeared laterally to the pons (not shown). The last slice included
was the one at which the cerebellum was no longer distinguishable from the transverse sinus or disappeared (not shown). The measurement of the vermis began at
the slice where the anterior and/or inferior posterior lobes appeared (A). This slice included the brainstem. The gray matter of the vermis structures, determined by
mathematically derived cutoffs for gray matter–white matter–CSF partitions from histograms of signal intensities, were traced separately (B, C, D, E). Measurements
were made around the vermis until it was no longer visible within the cerebellar hemispheres (F). The first slice of the brainstem was measured where the pons first
appeared within the suprasellar cistern (not shown). The cerebellar peduncles, including the brachium pontis (middle), were included in the brainstem (A). Posterior
measurements included the cerebral aqueduct and the superior colliculus. No separate volumetric measurements were made for the midbrain, pons, and medulla
oblongata.
esis was two-tailed with � 0.05. All data are presented as mean � SD
unless otherwise specified.
RESULTS
Subjects with an adolescent-onset AUD had smaller PFC
(p � 0.02) and PFC white matter (p � 0.007) volumes and
greater amounts of PFC CSF (p � 0.01) compared with
healthy comparison subjects. Smaller PFC (p � 0.02), PFC
white matter (p � 0.004), and larger PFC CSF (p � 0.001)
volumes persisted when controlling for cerebral volume.
See Table 3. These results also persisted when controlling
for cerebral volume and age, sex, and sex-by-group interactions
for smaller PFC (F 1,36 � 5.8, p � 0.02) and PFC
white matter (F 1,36 � 9.0, p � 0.005), and larger PFC CSF
(F 1,36 � 12.0, p � 0.001) volumes. No effects of age, sex, or
sex-by-group were seen in these analyses.
Given that both major depression (Steingard et al., 2002)
and ADHD (Castellanos et al., 1996; Mostofsky et al., 2002;
Sowell et al. 2003a) in adolescents has been associated with
smaller frontal volumes, multivariate regression analysis
was used to further examine the results using comorbidity
(number of comorbid disorders), sex, sex-by-group, and
using PFC outcome variables least squares adjusted for
cerebral volume. Although these analyses have decreased
power, the results did confirm larger PFC CSF (F 1,37 �
5.27, p � 0.03) in subjects with an adolescent-onset AUD
and suggested that subjects with an adolescent-onset AUD
had smaller PFC (F 1,37 � 2.73, p � 0.1) and smaller PFC
white matter volumes (F 1,37 � 2.4, p � 0.1), compared with
healthy comparison subjects. Furthermore, multivariate regression
analysis examining PFC variables using comorbidity
(presence of major depression, presence of ADHD),
presence of a cannabis use disorder, and controlling for sex
and cerebral volume did confirm larger PFC CSF (F 1,34 �
9.0, p � 0.005) in subjects with an adolescent-onset AUD
and suggested that subjects with an adolescent-onset AUD
had smaller PFC suggested smaller PFC (F 1,34 � 2.4, p �
0.1), compared with healthy comparison subjects. The estimates
for these comorbidities in these above regression
models were not significant. To further explore this issue of
comorbidity, we ran the following analyses. There were no
significant differences when PFC volumes (adjusted for
cerebral volumes) in AUD subjects with major depression
(mean, 162.2 � 14.2 cm 3 ) and without major depression
(mean, 155.7 � 15.2 cm 3 )(t 1,12 � -0.77, p � 0.45) were
compared within AUD groups or when AUD subjects with
ADHD (mean, 159.9 � 9.2 cm 3 ) and without ADHD
(mean, 161.0 � 18.7 cm 3 )(t 1,12 � 0.13, p � 0.89) were
compared within AUD groups. There were no significant
differences when PFC volumes adjusted for cerebral volumes
in AUD subjects with a cannabis use disorder (mean,
160.7 � 16.1 cm 3 ) and without a cannabis use disorder
(mean, 159.3 � 3.5 cm 3 ) were compared within AUD
groups (t 1,12 � -0.14, p � 0.89). Furthermore, there were no

BRAIN VOLUMES IN YOUTHS 1595
Table 3. Brain structures of subjects with an adolescent-onset AUD and matched comparison subjects
Structures (cm 3 ) AUD AUD males AUD females Control Controlmales Controlfemales AUD vs.controls Covariate, t 1,39, p value
Intracranial volume 1477.8 � 105.4 1503.0 � 117.2 1444.2 � 185.3 1547.7 � 135.0 1618.9 � 104.8 1452.8 � 115.1 t � -1.68 p � 0.1 —
Cerebral volume 1283.5 � 103.2 1307.0 � 116.7 1252.2 � 81.1 1322.0 � 153.3 1404.5 � 89.0 1211.9 � 154.1 t � -0.85 p � 0.40 —
Cerebral CSF 37.7 � 12.8 36.7 � 15.3 39.0� 9.9 41.2 � 11.8 39.3 � 11.9 45.7 � 11.4 t � -0.82 p � 0.42 —
PFC volume 157.1 � 18.1 158.1 � 18.0 155.7 � 19.8 175.9 � 26.6 184.2 � 23.2 164.9 � 27.7 t � -2.38 p � 0.02 Group: t � -2.47, p � 0.0 Cerebral
Vol: t � 6.23, p � 0.0001
PFC gray matter 107.1 � 12.1 106.6 � 12.9 107.8 � 12.2 114.9 � 17.6 117.1 � 15.9 111.8 � 20.0 t 40 � -1.48 p � 0.15 Group: t � -1.19, p � 0.24 Cerebral
Vol: t � 4.14, p � 0.0002
PFC white matter 50.0 � 8.2 51.5 � 8.0 47.9 � 8.9 61.0 � 13.2 67.1 � 10.8 53.0 � 12.1 t 40 � -2.86 p � 0.007 Group: t � -3.09, p � 0.004 Cerebral
Vol: t � 5.99, p � 0.0001
PFC CSF 8.3 � 4.1 8.1 � 3.6 8.6 � 4.9 5.0 � 2.0 4.6 � 2.2 5.6 � 1.4 t 1 � 2.83 p � 0.01 Group: t � 3.48, p � 0.001 Cerebral
Vol: t � 0.25, p � 0.80
Thalamus (total) 8.4 � 2.0 8.5 � 1.9 8.2 � 2.2 7.4 � 2.0 7.4 � 1.8 7.4 � 2.3 t 40 � 1.34 p � 0.19 Group: t � 1.38, p � 0.18 Cerebral
Vol: t � 0.61, p � 0.54
Right thalamus 4.2 � 1.0 4.2 � 1.1 4.1 � 1.1 3.7 � 1.0 3.7 � 0.9 3.8 � 1.2 t 40 � 1.28 p � 0.21 Group: t � 1.30, p � 0.20 Cerebral
Vol: t � 0.46, p � 0.64
Left thalamus 4.2 � 1.0 4.2 � 0.9 4.1 � 1.2 3.7 � 1.1 3.8 � 0.9 3.7 � 1.2 t 40 � 1.32 p � 0.20 Group: t � 1.37, p � 0.18 Cerebral
Vol: t � 0.73, p � 0.47
Pons/brainstem 26.1 � 2.5 26.5 � 2.3 25.5 � 2.9 26.2 � 2.9 27.1 � 3.2 25.0 � 2.0 t 40 � -0.12 p � 0.90 Group: t � 0.10, p � 0.92 Cerebral
Vol: t � 1.67, p � 0.1
Cerebellum(total) 141.3 � 9.1 141.0 � 10.1 141.7 � 8.5 143.8 � 13.3 150.7 � 13.1 134.7 � 6.3 t 40 � -0.63 p � 0.53 Group: t � -0.26, p � 0.79 Cerebral
Vol: t � 3.24, p � 0.002
Right cerebellum 70.9 � 4.9 70.8 � 5.9 71.0 � 3.8 72.3 � 6.9 75.7 � 6.7 67.7 � 4.2 t 40 � -0.67 p � 0.51 Group: t � -0.33, p � 0.74 Cerebral
Vol: t � 2.99, p � 0.005
Left cerebellum 69.7 � 5.2 69.6 � 6.0 69.9 � 4.3 71.4 � 6.7 74.6 � 6.4 67.1 � 4.3 t 40 � -0.80 p � 0.43 Group: t � -0.47, p � 0.64 Cerebral
Vol: t � 2.91, p � 0.006
Cerebellar vermis 7.86 � 0.59 7.84 � 0.67 7.9 � 0.51 7.34 � 1.25 7.64 � 1.04 6.95 � 1.45 t 40 � 1.46 p � 0.15 Group: t � 1.65, p � 0.1 Cerebral
(total)
Vol: t � 1.43, p � 0.16
CV, cerebral volume.
significant differences when PFC volumes adjusted for cerebral
volumes in AUD subjects with PTSD (mean, 163.2 �
18.6 cm 3 ) and without PTSD (mean, 163.2 � 8.4 cm 3 )(t 1,12
� -0.73, p � 0.48) or with conduct disorder (mean, 159.3 �
12.5 cm 3 ) and without conduct disorder (mean, 163.2 �
19.8 cm 3 )(t 1,12 � 0.45, p � 0.66) were compared within
AUD groups.
Right, left and total thalamic, pons/brainstem, right and
left cerebellar hemispheres and total cerebellum and cerebellar
vermis volumes did not differ between groups. See
Table 3. There was a significant sex-by-group effect, indicating
that males with an adolescent-onset AUD compared
with control males had smaller cerebellar volumes (male
AUD mean: 141.8 � 8.5 cm 3; male control mean: 150.7 �
13.1 cm 3 )(F � 4.16, df � 1,37, p � 0.05), whereas the two
female groups were not different on this variable (female
AUD mean: 141.0 � 10.1 cm 3; female control mean: 134.7
� 6.3 cm 3 ). It should be noted six of the eight AUD males
also had comorbid attention deficit hyperactivity disorder,
combined type, whereas only one adolescent female had
this diagnosis. No other significant sex-by-group interactions
were seen.
PFC variables adjusted for cerebral volume to correct for
individual differences in brain size with regard to gender
were correlated with six alcohol consumption variables (age
of onset of AUD, duration of an AUD, average number of
drink per drinking episode, most number of drinks per
maximum drinking episode, quantity/frequency grouping,
and lifetime number of drinks), using Spearman correlation
coefficients. The average number of drinks per drinking
episode significantly correlated with the PFC gray matter
volume (r s � -0.6, df � 12, p � 0.03). The number of drinks
per maximum drinking episode significantly correlated with
the PFC volume (r s � -0.57, df � 12, p � 0.03) and PFC
gray mater volume (r s � -0.78, df � 12, p � 0.0009). See
Fig. 4. Quantity/frequency grouping negatively correlated
with the PFC gray matter volume (r s � -0.71, df � 12, p �
0.005). No significant relationships were seen between
PFC, PFC gray, or prefrontal white matter measures with
age of onset of an AUD or duration of an AUD. No
significant relationships were seen between PFC gray or
white matter measures with age of onset or duration of
cannabis use disorder. Fig. 3
DISCUSSION
Adolescents and young adults with adolescent-onset
AUD and comorbid mental disorders were found to have
significantly smaller PFC and PFC white matter volumes
and greater amounts of PFC CSF compared with healthy
comparison subjects. The findings of greater PFC CSF
persisted when controlling for comorbidity. PFC volume
variables significantly and negatively correlated with the
most number of drinks per maximum drinking episode.
Right, left, and total thalamic, pons/brainstem, right and
left cerebellar hemispheres, and total cerebellum and cer-

1596 DE BELLIS ET AL.
Fig. 3. PFC volumes (cm 3 ) adjusted for cerebral volume of adolescents and
young adults with an adolescent-onset AUD and comparison subjects. Female
subjects are shown in circles. Male subjects are shown in squares. Subjects in
blue indicate history of major depression; open circles or squares indicate history
of ADHD.
Fig. 4. Correlation between PFC gray matter volume (cm 3 ) adjusted for cerebral
volume of adolescents and young adults with an adolescent-onset AUD with
the most number of drinks per maximum drinking episode (r s � -0.78, df � 12,
p � 0.0009). Female subjects are shown in circles. Male subjects are shown in
squares. Subjects in blue indicate history of major depression; open circles or
squares indicate history of ADHD.
ebellar vermis volumes did not differ between groups.
There was a significant sex-by-group effect for males with
an adolescent-onset AUD to have smaller cerebellar volumes
than control males. To our knowledge, this is the first
study to examine the association of an adolescent-onset
AUD on the prefrontal, thalamic, and cerebellar morphology
of adolescents and young adults.
There are several possible explanations for the smaller
PFC volumes and PFC white matter volumes found in
adolescents and young adults with adolescent-onset AUD.
Since the PFC is maturing during adolescence, PFC maturation
may be impeded by the neurotoxic effects of alcohol,
despite the significantly different drinking patterns seen in
adolescents, who tend to binge drink relative to adults, who
are more likely to drink daily. Alcohol affects N-methyl-D-
aspartate (NMDA) receptors, a subclass of glutamate receptors
in the PFC that are maturing during adolescence
(Breese et al., 1995; Lovinger, 1993). Alcohol withdrawal
leads to increased excitatory transmission through its effects
on NMDA receptors. This process can result in discrete
excitotoxic neuronal damage or a more severe and
complicated alcohol withdrawal syndrome including withdrawal
seizures (Becker, 1999). Binging, or chronic intermittent
alcohol exposure, may be more neurotoxic to adolescent
neurons secondary to many brief episodes of
withdrawal (Lundqvist et al., 1995; Lundqvist et al., 1994).
However, only two of our AUD subjects had symptoms of
withdrawal. Adolescence is a period of active myelination
in PFC regions (Paus et al., 2001). Alcoholism is also
associated with white matter loss (Harding et al., 1997).
Although the findings of smaller PFC volumes did not
correlate with age of onset or duration of an AUD, significant
negative correlations were seen between PFC and
PFC gray matter volume measures with alcohol consumption
variables. Thus, we may speculate that the smaller PFC
volumes may be a result of the adverse effects of an AUD
on the adolescent PFC either through a direct or indirect
(via withdrawal) toxic effects on adolescent PFC development.
Another explanation for the smaller PFC and PFC
white matter volumes may be that PFC maturation is
impeded by the neurotoxic effects of other illicit substances
on the adolescent brain. Eleven of the 14 subjects
in this study also had cannabis abuse or dependence. The
active component of the marijuana plant, cannabis sativa, is
9-tetrahydrocannabinol, which affects growth factor gene
expression in the adult rat forebrain (Mailleux et al., 1994).
Preclinical data suggest that cannabinoids modulate the
release of neurotransmitters (i.e., L-glutamate, GABA, noradrenaline,
dopamine, serotonin, and acetylcholine) from
axon terminals (Iversen, 2003). Although GABA functions
primarily as an inhibitory neurotransmitter, it can also act
as a trophic factor during nervous system development to
influence events such as proliferation, migration, differentiation,
synapse maturation, and cell death (Owens and
Kriegstein, 2002). Thus, cannabis use disorder through its
effects on GABA and other neurotransmitters may negatively
influence adolescent brain development. Although
multivariate regression analysis examining the results controlling
for the presence of a cannabis use disorder as well
as comorbidity also suggested smaller PFC and smaller
PFC gray and white matter volumes in the AUD subjects
compared with control subjects, because no adolescentonset
AUD subject had an alcohol use disorder only, it is
not impossible to conclude that an adolescent-onset AUD
is toxic to PFC maturation.
A third explanation for the smaller PFC and PFC white
matter volumes of adolescents and young adults with an
adolescent-onset AUD is an inherent vulnerability for delayed
PFC maturation that enhances the risk for poorer
executive cognitive functioning and adolescent substance

BRAIN VOLUMES IN YOUTHS 1597
use disorders. Impulsivity is thought to be closely associated
with PFC development during adolescence (Spear, 2000).
Increased impulsivity, which may contribute to early-onset
drinking, is seen in subjects with frontal lobe excisions
(Miller, 1992). Alcoholism has been conceptualized as a
frontal lobe condition (Moselhy et al., 2001). Neuroimaging
studies across modalities have demonstrated the PFC to be
involved in intoxication, craving, and withdrawal with respect
to alcohol and other drugs (Goldstein and Volkow,
2002). In a review of the literature on impulsivity and
addiction in adolescence, Chambers et al. (2003) concluded
that substance use disorders constitute neurodevelopmental
disorders and suggested that the common neurobiological
mechanisms involving PFC motivational brain circuitry
could be substrates for both normative impulsivity and
addictive behavior among adolescents. In our study, PFC
volume variables adjusted for cerebral volumes significantly
correlated with the most number of drinks per maximum
drinking episode, a measure of disinhibition and impulsivity.
High-risk studies with subjects with family histories of
substance use disorders support this idea. Young subjects,
who are offspring of fathers with alcoholism and are at high
familial risk for AUD perform poorly on measures of executive
cognitive function (Giancola et al., 1996; Harden
and Pihl, 1995; Hill, 2004). Individuals at high relative risk
for substance use disorders demonstrated impulsive problems
including conduct disorders (Clark et al., 1998), antisocial
behaviors (Clark et al., 1999), and neurobehavioral
disinhibition (Tarter et al., 2003) in late childhood. Deficits
in PFC-mediated executive functions of decision-making,
sustained attention, verbal fluency abstraction, behavioral
inhibition, working memory, regulation of motivation, and
motor control are seen in ADHD (Barkley, 1997; Ernst et
al., 2003; Pineda et al., 1998), conduct disorder (Dery et al.,
1999; Morgan and Lilienfeld, 2000), major depression
(Rogers et al., 2004), and posttraumatic stress disorder
(Beers and De Bellis, 2002), the most common comorbid
disorders in our AUD subjects. Imaging studies also suggest
PFC abnormalities in all these heterogeneous mental
disorders (De Bellis et al., 2000b; Nolan et al., 2002; Schulz
et al., 2004; Sowell et al., 2003b; Spalletta et al., 2001).
Similar results have been attained in community adolescents,
where neurocognitive deficits in measures of attention
ability predicted substance use disorders 8 years later
(Tapert et al., 2002a). Thus, these childhood psychopathologies
may lead to early substance use disorders and
substance-related problems in adolescence, due to preexisting
PFC vulnerability. Thus, unlike our previous findings
in this sample, where we reported that the adolescent
hippocampus may be more vulnerable to the toxic effects of
an adolescent-onset AUD than an adult with similar years
of drinking (De Bellis et al., 2000a) because hippocampal
volumes correlated positively with the age of onset and
negatively with the duration of an AUD, the findings of
smaller PFC did not correlate with age of onset or duration
of an AUD. This leads to the speculation that delayed PFC
maturation (less myelination of the PFC) may be an inherent
vulnerability that enhances the risk for poorer executive
cognitive functioning and an adolescent-onset AUD.
In this particular study, the negative overall cerebellum
findings in adolescents with an AUD are in contrast with
adult findings (Parks et al., 2002). This may be because of
a longer period of exposure to alcohol consumption is
required to cause alterations in the cerebellum or because
of adolescent cerebellar plasticity (Hansel et al., 2001).
However, we note that there was a significant sex-by-group
effect for males with an adolescent-onset AUD to have
smaller cerebellar volumes than control males. It should be
noted that six of the eight AUD males also had comorbid
ADHD, combined type. Alteration in the frontal-striatal
circuitry underlies the psychopathology of ADHD and
probably causes the related deficits of higher cognitive
functions in these individuals (Berquin et al., 1998; Castellanos
et al., 1996). ADHD boys have a smaller cerebellar
vermis compared with healthy control boys (Berquin et al.,
1998; Castellanos et al., 1996), and in most studies, the
difference is limited to the inferior posterior vermis of the
cerebellum (Castellanos et al., 1996; Mostofsky et al.,
1998). The difference in cerebellar volume in boys with
ADHD has been replicated in girls with similar symptom
severity (Castellanos et al., 2001). A longitudinal study
found that both smaller cerebral and cerebellar volumes in
subjects with ADHD persisted over time and were unrelated
to stimulant treatment (Castellanos et al., 2002).
Overall, recent studies suggest a possible modulating cerebellar
influence on the predominantly right frontal-striatal
circuits involved in ADHD (Giedd et al., 2001). Since
premorbid ADHD is a risk factor for substance use disorders
in adolescent and young adults (Biederman et al.,
1997; Clark et al., 1999), any possible relation between
AUD and other comorbid conditions should be considered
when evaluating structural MRI findings in individuals with
AUD. Our study is limited by the fact that our adolescent
AUD group had extensive comorbidity with other Axis I
disorders. It should be noted that it is rare for an adolescent
with alcohol abuse or dependence not to have another
mental disorder (Clark et al., 1997). Imaging studies of
adult and adolescent AUD are only beginning to address
the issues of comorbidity by examining adults with early
and late onset alcohol dependence. However, studies of
very large samples are needed to examine these issues.
Given that most MRI studies of adults with an AUD do not
examine histories of childhood-onset mental disorders
(ADHD, major depression) in their samples, this is an
important and overlooked research issue that may have
influenced the known brain structural findings in the adult
AUD literature.
In summary, unlike in the mature brain, in which chronic
AUD is characterized by a continuum of graded brain
dysmorphology, our preliminary findings suggest that structural
deficits in the adolescent PFC are associated with an
adolescent-onset AUD in individuals with comorbid mental

1598 DE BELLIS ET AL.
disorders. We speculate that smaller PFC may represent a
vulnerability to, or a consequence of, early-onset drinking
in these vulnerable individuals. However, given that our
sample lacked an AUD group without comorbidity, we
cannot conclude that a smaller PFC is a causal factor for an
adolescent-onset AUD. Further studies are warranted to
examine if a smaller PFC represents a vulnerability to or a
consequence of early-onset drinking. Future MRI studies
of individuals with an adolescent-onset AUD that control
for comorbidity and in individuals at high familial risk for
AUD are warranted.
ACKNOWLEDGMENTS
The authors thank Grace Moritz, M.S.W., Cara Renzelli,
M.S.Ed., Julie Hall, B.A., and the staff of the University of
Pittsburgh Medical Center’s General Clinical Research Center
Staff for their assistance in this work.
REFERENCES
Baker KG, Harding AJ, Halliday GM, Kril JJ, Harper CG (1999) Neuronal
loss in functional zones of the cerebellum of chronic alcoholics with
and without Wernicke’s encephalopathy. Neuroscience 91:429–438.
BarkleyRA (1997) Behavioral inhibition, sustained attention, and executive
functions: constructing a unifying theory of ADHD. Psychol Bull
121:65–94.
Becker HC (1999) Alcohol withdrawal: neuroadaptation and sensitization.
CNS Spectrums 4:38–65.
Beers SR, De Bellis MD (2002) Neuropsychological function in children
with maltreatment-related posttraumatic stress disorder. Am J Psychiatry
159:483–486.
Berquin PC, Giedd JN, Jacobsen LK, Hamburger SD, Krain AL, Rapoport
JL, et al (1998) Cerebellum in attention-deficit hyperactivity
disorder: a morphometric MRI study. Neurology 50:1087–1093.
Biederman J, Wilens T, Mick E, Farone S, Weber W, Curtis S, et al (1997)
Is ADHD a risk factor for psychoactive substance use disorders? Findings
from a four year prospective follow-up study. J Am Acad Child
Adolesc Psychiatry 36:21–29.
Breese CR, Freedman R, Leonard SS (1995) Glutamate receptor subtype
expression in human postmortem brain tissue from schizophrenics and
alcohol abusers. Brain Res 674:82–90.
Brown S, Tapert SF, Granholm E, Delis D (2000) Neurocognitive functioning
of adolescents: Effects of protracted alcohol use. Alcohol Clin
Exp Res 24:164–171.
Cahalan D (1981) Quantifying alcohol consumption: Patterns and problems.
Circulation 64:7–14.
Castellanos FX, Giedd JN, Berquin PC, Walter JM, Sharp W, Tran T, et
al (2001) Quantitative brain magnetic resonance imaging in girls with
attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 58:289–
295.
Castellanos FX, Giedd JN, Marsh WL, Hamburger SD, Vaituzis AC,
Dickstein DP, et al (1996) Quantitative brain magnetic resonance imaging
in attention-deficit hyperactivity disorder. Arch Gen Psychiatry
53:607–616.
Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen
LS, et al (2002) Developmental trajectories of brain volume abnormalities
in children and adolescents with attention-deficit/hyperactivity disorder.
JAMA 288:1740–1748.
Chambers RA, Taylor JR, Potenza MN (2003) Developmental neurocircuitry
of motivation in adolescence: a critical period of addiction vulnerability.
Am J Psychiatry 160:1041–1052.
Chambers WJ, Puig-Antich J, Hirsch M, Paez P, Ambrosini PJ, Tabrizi
MA, et al (1985) The assessment of affective disorders in children and
adolescents by semi-structured interview: Test-retest reliability of the
schedule for affective disorders and schizophrenia for school-age children,
present episode version. Arch Gen Psychiatry 42:696–702.
ClarkDB (1999) Psychiatric Assessment. In Sourcebook on Substance
Abuse: Etiology, Epidemiology, Assessment, and Treatment (Ott PJ,
Tarter RE, Ammerman RT, eds), pp 197–211. Allyn & Bacon, Needham
Heights, MA.
Clark DB (2004) The natural history of adolescent alcohol use disorders.
Addiction 99:5–22.
Clark DB, Kirisci L, Moss HB (1998) Early adolescent gateway drug use
in sons of fathers with substance use disorders. Addict Behav 23:561–
566.
Clark DB, Parker AM, Lynch KG (1999) Psychopathology and substancerelated
problems during early adolescence: A survival analysis. J Clin
Child Psychol 28:333–341.
Clark DB, Pollock N, Bukstein OG, Mezzich AC, Bromberger JT, Donovan
JE (1997) Gender and comorbid psychopathology in adolescents
with alcohol dependence. J Am Acad Child Adolesc Psychiatry 36:1195–
1203.
Clark DB, Pollock NK, Mezzich A, Cornelius J, Martin C (2001) Diachronic
assessment and the emergence of substance use disorders. J Child
Adolesc Substance Abuse 10:13–22.
De Bellis MD, Clark DB, Beers SR, Soloff P, Boring AM, Hall J, et al
(2000a) Hippocampal volume in adolescent onset alcohol use disorders.
Am J Psychiatry 157:737–744.
De Bellis MD, Keshavan M, Clark DB, Casey BJ, Giedd J, Boring AM, et
al (1999) A.E. Bennett Research Award: Developmental Traumatology,
II: Brain development. Biol Psychiatry 45:1271–1284.
De Bellis MD, Keshavan M, Shifflett H, Iyengar S, Dahl R, Axelson DA,
et al (2002) Superior temporal gyrus volumes in pediatric generalized
anxiety disorder. Biol Psychiatry 51:553–562.
De Bellis MD, Keshavan MS, Beers SR, Hall J, Frustaci K, Masalehdan A,
et al (2001) Sex differences in brain maturation during childhood and
adolescence. Cereb Cortex 11:552–557.
De Bellis MD, Keshavan MS, Spencer S, Hall J (2000b) N-acetylaspartate
concentration in the anterior cingulate in maltreated children and
adolescents with PTSD. Am J Psychiatry 157:1175–1177.
Dery M, Toupin J, Pauze R, Mercier H, Fortin L (1999) Neuropsychological
characteristics of adolescents with conduct disorder: Association
with attention-deficit-hyperactivity and aggression. J Abnorm Child
Psychol 27:225–236.
Durston S, Hulshoff Pol, HE Casey BJ, Giedd JN, Buitelaar JK, van
Engeland H (2001) Anatomical MRI of the developing human brain:
what have we learned? J Am Acad Child Adolesc Psychiatry 40:1012–
1020.
Ernst M, Kimes AS, London ED, Matochik JA, Eldreth D, Tata S, et al
(2003) Neural substrates of decision making in adults with attention
deficit hyperactivity disorder. Am J Psychiatry 160:1061–1070.
Giancola P, Moss HB, Martin C, Kirisci L, Tarter RE (1996) Executive
cognitive functioning predicts reactive aggression in boys at high risk for
substance abuse: A prospective study. Alcohol Clin Exp Res 20:740–
744.
Giedd JN, Blumenthal J, Jeffries NO, Castellanos X, Liu H, Zijdenbos A,
et al (1999) Brain development during childhood and adolescence: a
longitudinal MRI study. Nat Neurosci 2:861–863.
Giedd JN, Blumenthal J, Molloy E, Castellanos FX (2001) Brain imaging
of attention deficit/hyperactivity disorder. Ann N Y Acad Sci 931:33–49.
Gilbert AR, Moore GJ, Keshavan MS, Paulson LA, Narula V, Mac
Master FP, et al (2000) Decrease in thalamic volumes of pediatric
patients with obsessive-compulsive disorder who are taking paroxetine.
Arch Gen Psychiatry 57:449–456.
Gilman S, Adams K, Koeppe RA, Berent S, Kluin KJ, Modell JG, et al
(1990) Cerebellar and frontal hypometabolism in alcoholic cerebellar
degeneration studied with positron emission tomography. Ann Neurol
28:775–785.
Goldstein RZ, Volkow ND (2002) Drug addiction and its underlying
neurobiological basis: neuroimaging evidence for the involvement of the
frontal cortex. Am J Psychiatry 159:1642–1652.

BRAIN VOLUMES IN YOUTHS 1599
Grant B, Harford T, Dawson D, Chou P, Pickering R (1995) The Alcohol
Use Disorder and Associated Disabilities Interview Schedule (AUDA-
DIS): Reliability of alcohol and drug modules in a general population
sample. Drug Alcohol Depend 39:37–44.
Hansel C, Linden DJ, D’Angelo E (2001) Beyond parallel fiber LTD: The
diversity of synaptic and non-synaptic plasticity in the cerebellum. Nat
Neurosci 4:467–475.
Hardan AY, Minshew NJ, Harenski K, Keshavan M (2001) Posterior fossa
magnetic resonance imaging in autism. J Am Acad Child Adolesc
Psychiatry 40:666–672.
Harden PW, Pihl RO (1995) Cognitive function, cardiovascular reactivity
and behavior in boys at high risk for alcoholism. J Abnorm Psychol
104:94–103.
Harding A, Wong A, Svoboda M, Kril JJ, Halliday GM (1997) Chronic
alcohol consumption does not cause hippocampal neuron loss in humans.
Hippocampus 7:78–87.
Harper C, Kril J, Daly J (1987) Are we drinking our neurons away? Br
Med J 294:534–536.
Harper CG, Smith NA, Kril JJ (1990) The effects of alcohol on the female
brain: a neuropathological study. Alcohol Clin Exp Res 25:445–448.
Hasin D, Carpenter KM (1998) Difficulties with questions on usual drinking
and the measurement of alcohol consumption. Alcohol Clin Exp
Res 22:580–584.
Hill SY (2004) Trajectories of alcohol use and electrophysiological and
morphological indices of brain development: distinguishing causes from
consequences. Ann N Y Acad Sci. 1021:245–259.
Hommer D, Momenan R, Rawlings R, et al (1996) Decreased corpus
callosum size among alcoholic women. Arch Neurol 53:359–363.
Hommer DW (2003) Male and female sensitivity to alcohol-induced brain
damage. Alcohol Res Health J Natl Inst Alcohol Abuse Alcohol 27:
181–185.
Iversen L (2003) Cannabis and the brain. Brain 126:1252–1270.
JohnstonLD,O’MalleyPM,BachmanJG (2003) The Monitoring the Future
National Survey Results on Adolescent Drug Use: Overview of Key Findings
2002. National Institute on Drug Abuse: Bethesda MD.
Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, et al (1997)
Schedule for affective Disorders and Schizophrenia for school-age
children-present and lifetime version (K-SADS-PL): Initial reliability
and validity data. J Am Acad Child Adolesc Psychiatry 36:980–988.
Keshavan MS, Anderson S, Beckwith C, Nash K, Pettegrew J, Krishnan
KRR (1995) A comparison of stereology and segmentation techniques
for volumetric measurements of brain ventricles. Psychiatry Res Neuroimaging
61:53–60.
Kosten TA, Anton SF, Rounsaville BJ (1992) Ascertaining psychiatric
diagnoses with the family history method in a substance abuse population.
Psych Res 26:135–147.
Kosten TA, Rounsaville BJ (1992) Sensitivity of psychiatric diagnosis
based on the best estimate procedure. Am J Psychiatry 149:1225–1227.
Lange N, Giedd JN, Castellanos FX, Vaituzis AC, Rapoport JL (1997)
Variability of human brain structure size: ages 4-20 years. Psychiatry
Res Neuroimaging Sect 74:1–12.
Liu X, Matochik JA, Cadet JL, London ED (1998) Smaller volume of
prefrontal lobe in polysubstance abusers: a magnetic resonance imaging
study. Neuropsychopharmacology 18:243–252.
Lovinger DM (1993) Excitotoxicity and alcohol-related brain damage.
Alcohol Clin Exp Res 17:19–27.
Lundqvist C, Alling C, Knoth R, Volk B (1995) Intermittent ethanol
exposure of adult rats: hippocampal cell loss after one month of treatment.
Alcohol Clin Exp Res 30:737–748.
Lundqvist C, Volk B, Knoth R, Alling C (1994) Long-term effects of
intermittent versus continuous ethanol exposure on hippocampal synapses
of the rat. Acta Neuropathol 87:242–249.
Mailleux P, Preud’homme X, Albala N, Vanderwinden JM, Vanderhaeghen
JJ (1994) Delta-9-Tetrahydrocannabinol regulates gene expression
of the growth factor pleiotrophin in the forebrain. Neurosci Lett 175:
25–27.
Mann K, Batra A, Gunthner A, Schroth G (1992) Do women develop
alcoholic brain damage more readily than men? Alcohol Clin Exp Res
16:1052–1056.
Martin CS, Pollock NK, Bukstein OG, Lynch KG (2000) Inter-rater
reliability of the SCID alcohol and substance use disorders section
among adolescents. Drug Alcohol Depend 54:173–176.
Miller EK, Cohen JD (2001) An integrative theory of prefrontal function.
Ann Rev Neurosci 24:167–202.
Miller LA (1992) Impulsivity, risk-taking, and the ability to synthesize
fragmented information after frontal lobectomy. Neuropsychologia 30:
69–72.
Morgan AB, Lilienfeld SO (2000) A meta-analytic review of the relation
between antisocial behavior and neuropsychological measures of executive
function. Clin Psychol Rev 20:113–136.
Moselhy HF, Georgiou G, Kahn A (2001) Frontal lobe changes in alcoholism:
a review of the literature. Alcohol Alcohol 36:357–368.
Moss HB, Kirisci L, Gordon HW, Tarter RE (1994) A neuropsychological
profile of adolescent alcoholics. Alcohol Clin Exp Res 18:159–163.
Mostofsky SH, Cooper KL, Kates WR, Denckla MB, Kaufmann WE
(2002) Smaller prefrontal and premotor volumes in boys with attentiondeficit/hyperactivity
disorder. Biol Psychiatry 52:785–794.
Mostofsky SH, Reiss AL, Lockhart P, Denckla MB (1998) Evaluation of
cerebellar size in attention-deficit hyperactivity disorder. J Child Neurol
13:434–439.
Nicolas JM, Fernandez-Sola J, Antunez E, Cofan M, Cardenal C,
Sacanella E, Estruch R, Urbano-Marquez A (2000) High ethanol intake
and malnutrition in alcoholic cerebellar shrinkage. QJM 93:449–456.
Nolan CL, Moore GJ, Madden R, Farchione T, Bartoi M, Lorch E, et al
(2002) Prefrontal cortical volume in childhood-onset major depression:
preliminary findings. Arch Gen Psychiatry 59:173–179.
OrvaschelH, Puig-Antich (1987) Schedule for Affective Disorder and
Schizophrenia for School-Age Children, Epidemiologic Version.
K-SADS-E Fourth Version.
Owens DF, Kriegstein AR (2002) Is there more to GABA than synaptic
inhibition? Nat Rev Neurosci 3:715–727.
Parks MH, Dawant BM, Riddle WR, Hartmann SL, Dietrich MS, Nickel
MK, et al (2002) Longitudinal brain metabolic characterization of
chronic alcoholics with proton magnetic resonance spectroscopy. Alcohol
Clin Exp Res 26:1368–1380.
PausT,CollinsDL,EvansAC, LeonardG,PikeB,ZijdenbosA (2001) Maturation
of white matter in the human brain: a review of magnetic resonance
studies. Brain Res Bull 54:255–266.
Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB,
Lim KO (1994) A quantitative magnetic resonance imaging study of
changes in brain morphology from infancy to late adulthood. Arch
Neurol 51:874–887.
Pfefferbaum A, Sullivan EV, Mathalon DH, Lim KO (1997) Frontal lobe
volume loss observed with magnetic resonance imaging in older chronic
alcoholics. Alcohol Clin Exp Res 21:521–529.
Pineda D, Ardila A, Rosselli M, Cadavid C, Mancheno S, Mejia S (1998)
Executive dysfunctions in children with attention deficit hyperactivity
disorder. Int J Neurosci 96:177–196.
Rasband W (1996) NIH IMAGE Manual. National Institutes of Health.
Bethesda, MD.
Rogers M, Kasai K, Koji M, Fukuda R, Iwanami A, Nakagome K, et al
(2004) Executive and prefrontal dysfunction in unipolar depression: A
review of neuropsychological and imaging evidence. Neurosci Res 50:
1–11.
Rohde P, Lewinsohn PM, Seeley JR (1996) Psychiatric comorbidity with
problematic alcohol use in high school students. J Am Acad Child
Adolesc Psychiatry 35:101–109.
Rosenberg DR, Keshavan MS, O’Hearn KM, Dick EL, Bagwell WW,
Seymour AB, et al (1997) Frontostriatal measurement in treatmentnaive
children with obsessive-compulsive disorder. Arch Gen Psychiatry
54:824–830.
Schmahmann JD (1997) The Cerebellum and Cognition, International
Review of Neurobiology, Vol 41. San Diego CA: Academic Press.

1600 DE BELLIS ET AL.
Schulz KP, Fan J, Tang CY, Newcorn JH, Buchsbaum MS, Cheung AM,
et al (2004) Response inhibition in adolescents diagnosed with attention
deficit hyperactivity disorder during childhood: an event-related FMRI
study. Am J Psychiatry 161:1650–1657.
Skinner H (1982) Development and Validation of a Lifetime Alcohol Consumption
Assessment Procedure. Toronto: Addiction Research Foundation.
Sowell ER, Thompson PM, Welcome SE, Henkenius AL, Toga AW,
Peterson BS (2003a) Cortical abnormalities in children and adolescents
with attention-deficit hyperactivity disorder. Lancet 362:1699–1707.
Sowell ER, Thompson PM, Welcome SE, Henkenius AL, Toga AW,
Peterson BS (2003b) Cortical abnormalities in children and adolescents
with attention-deficit hyperactivity disorder. Lancet 362:1699–1707.
Spalletta G, Pasini A, Pau F, Guido G, Menghini L, Caltagirone C (2001)
Prefrontal blood flow dysregulation in drug naive ADHD children
without structural abnormalities. J Neural Transm 108:1203–1216.
Spear LP (2000) The adolescent brain and age-related behavioral manifestations.
Neurosci Biobehav Rev 24:417–463.
Steingard RJ, Renshaw PF, Hennen J, Lenox M, Cintron CB, Young AD,
et al (2002) Smaller frontal lobe white matter volumes in depressed
adolescents. Biol Psychiatry 52:413–417.
Sullivan EV (2003) Compromised pontocerebellar and cerebellothalamocortical
systems: speculations on their contributions to cognitive and
motor impairment in nonamnesic alcoholism. Alcohol Clin Exp Res
27:1409–1419.
Sullivan EV, Rosenbloom MJ, Serventi KL, Deshmukh A, Pfefferbaum A
(2003) Effects of alcohol dependence comorbidity and antipsychotic
medication on volumes of the thalamus and pons in schizophrenia.
Am J Psychiatry 160:1110–1116.
Tapert SF, Baratta MV, Abrantes AM, Brown SA (2002a) Attention
dysfunction predicts substance involvement in community youth. J Am
Acad Child Adolesc Psychiatry 41:680–686.
Tapert SF, Granholm E, Leedy NG, Brown SA (2002b) Substance use and
withdrawal: Neuropsychological functioning over 8 years in youth. J Int
Neuropsychol Soc 8:873–883.
Tarter R, Vanyukov M, Giancola P, Dawes M, Blackson T, Mezzich A, et
al (1999) Epigenetic model of substance use disorder etiology. Dev
Psychopathol 11:657–683.
Tarter RE, Kirisci L, Mezzich A, Cornelius JR, Pajer K, Vanyukov M, et
al (2003) Neurobehavioral disinhibition in childhood predicts early age
at onset of substance use disorder. Am J Psychiatry 160:1078–1085.
Thompson PM, Giedd JN, Woods RP, MacDonald D, Evans AC, Toga
AW (2000) Growth patterns in the developing brain detected by using
continuum mechanical tensor maps. Nature 404:190–193.
Torvik A, Torp S (1986) The prevalence of alcoholic cerebellar atrophy: A
morphometric and histological study of autopsy material. J Neurol Sci
75:43–51.
Wagner FA, Anthony JC (2002) From first drug use to drug dependence:
developmental periods of risk for dependence upon marijuana, cocaine,
and alcohol. Neuropsychopharmacology 26:479–488.
Wiers RW, Sergeant JA, Gunnung WB (1994) Psychological mechanisms
of enhanced risk of addiction in children of alcoholics: A dual pathway?
Acta Paediatr Scand Suppl 404:9–13.