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

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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,
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