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Recent binge drinking predicts smaller cerebellar volumes in adolescents
In this study, the authors found that increased binge drinks during the past 3 months significantly predicted smaller bilateral cerebellar gray matter and left hemisphere cerebellar white matter volumes, and marginally predicted smaller right hemisphere white matter cerebellar volume as assessed by high-resolution MRI in a sample of 106 normally developing 16- to 19-year olds. Gender did not moderate these effects. Given the high rates of binge drinking in teens and emerging adults, these findings highlight an important public health concern. Interventions geared towards lowering damaging patterns of binge alcohol use in teens and young adults that have shown evidence of efficacy need to be implemented more aggressively to prevent long-term neuronal damage, and to ensure optimal brain health and cognitive functioning in youth.
Psychiatry Research 2013; Volume 211, pages 17–23
The current study examined the effects of recent binge drinking on cerebellar morphometry in a sample of healthy adolescents. Participants were 106 teenagers (46 bingers and 60 controls) aged 16–19 who received a high-resolution magnetic resonance imaging (MRI) scan. FreeSurfer segmented and quantified the volume of each cerebellum. Maximum drinks during a binge in the past 3 months and duration since last binge were examined as predictors of cerebellar volume, after controlling for potentially confounding variables. In the 106 teens, higher peak drinks predicted smaller left hemisphere cerebellar gray and whitematter, and right hemisphere cerebellar gray matter, and marginally predicted smaller right hemisphere cerebellar white matter. Gender did not moderate these effects. More intense adolescent binge drinking is linked to smaller cerebellar volumes even in healthy teens, above and beyond variability attributable to risk factors for binge drinking. Longitudinal research is needed to see if cerebellar volumes worsen with protracted drinking and recover with abstinence. Interventions aimed at improving brain structure in adolescent binge drinkers are necessary given the high prevalence of risky drinking in youth.
Abbreviations: WRAT-3 - Wide Range Achievement Test-3rd edition, BAC - blood-alcohol concentration..
Keywords: Adolescence, Brain imaging, MRI, Alcohol, Binge drinking, Cerebellum.
Alcohol continues to be the most widely used intoxicant among teens, with almost a third of 12th graders reporting past month binge drinking (four standard alcohol drinks on an occasion in females and five drinks for males; Johnston et al., 2011 ). Despite this, relatively few studies have examined the impact of binge drinking on brain structure in teens.
Alcohol exposure appears to particularly affect the cerebellum (see Sullivan and Pfefferbaum, 2005 , for review) by exerting its effects on GABAergic neurons and glutamatergic granule cells ( Valenzuela et al., 2010 ). In rats, chronic alcohol exposure has resulted in structural abnormalities in Purkinje cells ( Pierce et al., 2011 ). The cerebellum plays a critical role in coordination and balance, affect regulation, and executive functioning ( Schmahmann and Sherman, 1998 ), which are often disrupted in adult alcohol dependent samples (see Sullivan and Pfefferbaum, 2005 , for review).
Because adolescence is a dynamic developmental stage, adult findings might not apply to teens. Several brain regions including the cerebellum continue to undergo gray matter synaptic pruning into the mid-twenties ( Lenroot and Giedd, 2006 ), and maturation of white matter tracts appears to continue into the early thirties ( Jernigan and Gamst, 2005 ). More specific to the current study, the cerebellum continues to functionally (e.g., Luna et al., 2001 ) and structurally mature through adolescence, with most studies suggesting continued pruning into the early twenties (Castellanos et al, 2002 and Hill et al, 2007). Gender differences in whole brain ( Lenroot and Giedd, 2010 ) and cerebellar ( Tiemeier et al., 2010 ) development have also been reported, with girls peaking earlier and having relatively smaller total cerebellar volumes.
Given this neurodevelopment, the emergence of binge drinking during the teen years may differentially influence the trajectory of brain development for boys and girls. Animal studies suggest that, compared to adults, adolescents may be particularly vulnerable to the neurotoxic effects of both chronic and binge alcohol exposure (see Spear, 2010 , for review). Despite the much higher prevalence of binge drinking compared to alcohol use disorders (AUD), the majority of the studies conducted thus far have specifically recruited teens with a history of AUD. Studies using magnetic resonance imaging (MRI) have revealed abnormalities in teens with AUD, including smaller left hippocampal (De Bellis et al, 2000, Nagel et al, 2005, and Medina et al, 2007) and prefrontal cortex (De Bellis et al, 2005 and Medina et al, 2008) volumes. Only one study investigated the cerebellum, which reported smaller cerebellar volumes in boys with AUD as compared to male controls ( De Bellis et al., 2005 ).
More recently, studies have examined the link between hazardous alcohol use (i.e., binge drinking) and brain health in youth. Using diffusion tensor imaging, our laboratory ( McQueeny et al., 2009 ) found that adolescent binge drinkers, compared to light drinkers, had significantly reduced white matter quality in several brain regions. Although binge drinking was not directly assessed, we ( Medina et al., 2010 ) found that increased overall use of alcohol during the past year was significantly related to smaller cerebellar vermis volumes in substance-using teens. Taken together, these studies suggest that recent binge drinking is associated with structural brain changes, although effects on the cerebellum have not been examined. Therefore, the aim of the current study was to assess thedose-dependenteffects of recent binge drinking on cerebellar volumes in a sample of 106 typically developing teenagers without comorbid psychiatric disorders. Based on adult findings, we hypothesized that higher peak binge drinking levels would be associated with smaller cerebellar volumes. Based on prior research (Medina et al, 2008, Squeglia et al, 2009, and Squeglia et al,), we also hypothesized that the relationship between binge drinking and smaller cerebellar volumes would be stronger in females than males.
Adolescents aged 16–19 were recruited from two projects examining youth at risk for substance use and consequences of marijuana use (McQueeny et al, 2009 and Hanson et al, 2010). In both, teens were recruited via flier distribution at area schools. Studies were approved by the University of California, San Diego Human Research Protections Program, and written assent and consent were obtained from each adolescent and the adolescent's parent/legal guardian, respectively. Each teen and his/her parent or guardian was given an extensive screening interview to determine eligibility. Exclusionary criteria were history of DSM-IV Axis I disorder other than mild conduct disorder; use of psychotropic medications; learning disability or mental retardation; head injury; serious medical or neurological problems; prenatal alcohol or drug exposure; complicated or premature birth; left handedness; vision or hearing problem; MRI contraindications; use of >5 joints of marijuana per month; >25 lifetime uses of all other illicit substances; and use of >10 cigarettes per month.
In order to collect data from a wide range of binge drinking histories, eligible participants were classified into two groups based on pattern of substance use: (1) binge drinkers (n=46), who reported at least one binge drinking episode within the past 3 months, defined as ≥5 (for males) or ≥4 (for females) standard alcohol drinks consumed in one sitting (NIAAA, 2004); or (2) controls (n=60) with no binge drinking episodes within the previous 3 months, and no lifetime history of an AUD. All participants were requested to abstain from any alcohol or drug use for 72 h prior to the session, which was confirmed with breathalyzer and urine toxicology.
Demographic and psychiatric assessment: Screening interviews were completed separately with each adolescent and his/her parent/guardian. The computerizedDISC-IV Predictive Scales( Lucas et al., 2001 ) were administered to youth and parents to exclude participants with DSM-IV Axis I mood, anxiety, psychotic, and attention deficit hyperactivity disorders. Parallel modules of the computerizedDiagnostic Interview Schedule( Robins et al., 1996 ) were used for 18- and 19-year-olds. The Family History Assessment Module (FHAM; Rice et al., 1995 ) assessed family history (FH) of substance use disorders. For each participant, an index of FH density was calculated: each parent with a history of AUD contributed 0.5 and each grandparent with AUD history added 0.25 to the score (range=0–2) ( Zucker et al., 1994 ). TheStructured Clinical Interviewmeasured psychosocial functioning and handedness. ThePuberty Development Scalewas given to assess puberty stage ( Petersen et al., 1988 ). TheBeck Depression Inventory(BDI; Beck et al., 1988 ) assessed depressive symptoms in the past 2 weeks. Weight and height were collected to calculate body-mass index (BMI).
Premorbid IQ estimates: TheWide Range Achievement Test-3rd Edition(WRAT-3; Wilkinson, 1993 ) reading subtest was administered to estimate verbal intelligence.
Alcohol and substance use: Current (past 3 months) and lifetime alcohol experiences were collected using theCustomary Drinking and Drug Use Record, which assesses DSM-IV abuse and dependence symptoms, symptoms of withdrawal, and substance-related adverse life events ( Stewart and Brown, 1995 ). Peak number of drinks during a binge in the past 3 months, typical drinks per occasion, and total lifetime alcohol use were measured. A modifiedTimeline Followback( Sobell and Sobell, 1992 ) was also administered to youth and parents, providing detailed information in a calendar format about the type, quantity, and frequency of recent use for the past 30 days covering alcohol along with several other drugs including: marijuana, nicotine, stimulants (e.g., amphetamine, methamphetamine, MDMA/ecstasy, cocaine), opiates (e.g., heroin, Vicodin), hallucinogens, barbiturates, benzodiazepines, and misuse of other prescription and over-the-counter medications.
Trained research associates administered screening interviews to adolescents and parents to assess eligibility. If eligible, prospective participants and their parent/guardians were individually administered a detailed interview. To facilitate open and honest disclosure, confidentiality of provided information and toxicology results was ensured for youths and parents within ethical and legal guidelines. Data from adolescents with self-reported drug or alcohol use within 3 days or positive urine toxicology screens or breath samples (AlcoSensor IV, Intoximeter, Inc., St. Louis, MO) at the time of evaluation were not included in these analyses. Parents and teens received financial compensation for participation upon completion of the study.
Participants completed a 1-h brain-imaging session at the UCSD Keck fMRI Center in a 3-Tesla CXK4 short bore Excite-2 MR system (General Electric, Milwaukee, WI) with an 8-channel phase-array head coil. Participants were placed comfortably on the scanner table and the head was stabilized within the head coil using foam cushions (NoMoCo Pillow, La Jolla, CA). Scan sessions involved a 10-s scout scan to assure good head placement and slice selection covering the whole brain. A sagittally acquired high-resolution 3D T1-weighted anatomical scan was obtained with the following parameters: field of view 24 cm, 256×256×192 matrix, 0.94×0.94×1 mm voxels, 176 slices, TR=20 ms, TE=4.8 ms; flip angle 12°, acquisition time 7 min 26 s.
2.4. Data processing and analysis
MRI preprocessing and intracranial volume calculation: To obtain overall intracranial volume (ICV), a hybrid watershed and deformable surface semi-automated skull-stripping program, followed by manual editing in AFNI ( Cox, 1996 ), was utilized to remove non-brain materials from each 3D T1-weighted anatomical dataset.
Cerebellar region of interest (ROI): All 3d T1-weighted datasets were aligned and transformed into standard (Talairach) space, corrected for inhomogeneities, and right and left gray and white matter cerebellar volumes were automatically parcellated using FreeSurfer (v5.0.0) software ( http://surfer.nmr.mgh.harvard.edu ; Fischl et al., 2002 ). Briefly, each image was rigid-body registered and non-linearly morphed to a probabilistic brain atlas, and then each voxel was labeled as a distinct anatomical area based on Bayesian modeling derived from manually segmented images. This morphometric technique has shown reliability and validity against manual tracing ( Fischl et al., 2002 ). Each individual’s cerebellar segmentations were visually inspected and hand-edited if necessary by KL, who was blind to group status and demonstrated reliability in cerebellar tracing ( Medina et al., 2010 ). Both right and left hemisphere gray and white matter volumes were measured (see Fig. 1 ).
Statistical analyses: Statistical analyses were conducted in SPSS 18.0. Analyses of variance (ANOVAs) andχ2tests compared groups on demographic and drug use variables (see Table 1 ). Interpretations of statistical significance were made ifp<0.05. To examine whether binge drinking was related to cerebellar morphometry, a series of linear regressions were run. Peak number of drinks in the past 3 months was used to predict right and left gray and white matter cerebellar volumes after controlling for potentially confounding variables (ICV, gender, recent tobacco use, lifetime marijuana use, lifetime other drug use, depressive symptoms, conduct disorder diagnosis, and family history of substance use disorder;N=106). Next, gender-by-peak binge interactions were examined in regressions similar to those described above.
|Binge drinkers (n=46)% or M (S.D.)||Controls (n=60)% or M (S.D.)|
|Age (range: 16–19 years)||18.0 (0.8)||17.7 (1.0)|
|Puberty development scale (females)||19.0 (0.8)||19.1 (1.0)|
|Puberty development scale (males)||17.7 (1.7)||17.0 (2.3)|
|Gender (% male)||67||58|
|Ethnicity (% Caucasian)||67||73|
|Parent socioeconomic status (Hollingshead)||27.6 (15.2)||26.1 (14.1)|
|% Completed 12th grade||54||37|
|% Familial history of substance use disorder||39||15|
|% Conduct disorder (mild only)||11||0|
|WRAT-3 reading standard score||109.2 (7.6)||108.1 (8.8)|
|Body-mass index||23.1 (3.5)||22.4 (4.1)|
|Beck Depression Inventory total||3.3 (3.9)||2.0 (2.5)|
|Peak drinks, past 3 months||9.2 (3.7)||0.3 (.7)|
|Peak BAC, past 3 months||24 (.09)||01 (.02)|
|Total binge drinking days, past month||2.5 (5.4)||0.0 (0)|
|% Lifetime history of alcohol use disorder||8||0|
|Days since last alcohol, a||26.7 (28.6)||211.7 (319.7)|
|Days since last binge drinking episode, a||28.7 (18.9)||167.9 (67.5)|
|Lifetime marijuana use days||7.6 (19.3)||0.7 (1.8)|
|Days since last marijuana use, a||161.6 (259.5)||483.2 (436.4)|
|Lifetime other drug use episodes||2.5 (5.3)||0.0 (0)|
|Past month cigarettes smoked||0.8 (2.2)||0.0 (0)|
|Intracranial volume (cc3)||1544.8 (142.7)||1525.3 (140.4)|
|Right hemisphere cerebellar gray matter (cc3)||54.8 (6.1)||56.4 (6.6)|
|Right hemisphere cerebellar white matter (cc3)||14.7 (2.0)||14.9 (1.9)|
|Left hemisphere cerebellar gray matter (cc3)||55.4 (5.5)||55.7 (6.7)|
|Left hemisphere cerebellar white matter (cc3)||14.8 (2.1)||14.9 (1.8)|
a Calculated only for those who used the substance within their lifetime.
Groups did not significantly differ in age (range 16–19) [F(1,105)=2.46,p=0.12], puberty stage [girls:F(1,37)=0.16,p=0.69; boys:F(1,65)=1.84,p=0.18], gender [x2(1)=0.91,p=0.34], parental socioeconomic status [F(1,105)=0.21,p=0.57], parental income [F(1,105)=0.25,p=0.62], grades completed [x2(5)=10.1,p=0.07], reading ability [F(1,105)=0.41,p=0.53], ethnicity [x2(7)=4.87,p=0.56], Hispanic/Latino composition [x2(2)=3.7,p=0.15], extent of family history of SUD (range: 0–2.25) [x2(7)=6.03,p=0.54], body-mass index [F(1,105)=1.09,p=0.30], or intracranial volume [F(1,105)=0.81,p=0.37]. The groups did differ in prevalence of mild conduct disorder [x2(1)=6.85,p=0.009], with five bingers (three males, two females) and no controls meeting diagnostic criteria. Of those, only one had elevated scores of externalizing symptoms on the Child Behavior Checklist ( Achenbach, 1991 ). The binge group had a higher prevalence of family history of SUD (negative, mild, positive) [x2(2)=8.1,p=0.02] and depressive symptoms, although only 4% of those in the binge group fell in the mildly depressed range [F(1,105)=4.76,p=0.03]. All variables that differed by group were controlled for in subsequent multivariate analyses (see Table 1 ).
3.2. Substance use
All participants were abstinent from alcohol and any other drug for at least 3 days before scanning. As expected, bingers had fewer days of abstinence from any alcohol [F(1,72)=15.00,p=0.001] and binge drinking [F(1,50)=134.03,p=0.0001; range 108–301 days in the controls who drank in past year and 3–63 days in the bingers] than controls. Additionally, recent binge drinkers had significantly greater recent peak binge drinks [F(1,105)=325.45,p=0.0001], greater maximum BAC in past 3 months [F(1,105)=355.03,p=0.0001] and greater total heavy drinking days [F(1,105)=9.27,p=0.003] than controls. Binge drinkers also reported greater past month tobacco use than controls [F(1,105)=8.61,p=0.004], although all participants had smoked fewer than 10 cigarettes per month and no more than one cigarette per day. Binge drinkers had greater lifetime marijuana use [F(1,105)=7.60,p=0.004] and fewer days of abstinence from marijuana [F(1,37)=8.16,p=0.007] than controls. Recent bingers (8%) and controls (0%) marginally differed in their lifetime history of an alcohol use disorder [x2(2)=5.4,p=0.07]. Other drug use was relatively low for binge drinkers, and the 10 bingers who had used other drugs were abstinent from them for an average of 186 days (S.D.=133, range 8–428 days); no control had used any other illicit drugs. Still, given these differences, past-month cigarette use, lifetime marijuana, and lifetime other drug use were controlled for in all multivariate analyses.
3.3. Cerebellar volumes and peak binge drinking
In order to describe the simple bivariate relationship between binge drinking and cerebellar volume, correlations were run between recent binge drinking and cerebellar volume in the 106 teens. It is also important to note that, as hypothesized, recent peak binge drinks was the most robust correlate (Spearman’s rank order) of total left hemisphere gray (rs(46)=−0.28,p=0.06) and white (rs(46)=−0.36,p=0.01) and right hemisphere gray (rs(46)=−0.28,p=0.008) and white matter (rs(46)=−0.26,p=0.02) cerebellar volumes in the binge drinkers. Age of regular alcohol use, age at first drink, hangover symptoms, and total number of drinking days were not significantly correlated with left or right cerebellar volumes in the binge drinkers. Therefore, recent binge drinking was the primary independent variable of interest.
After controlling for ICV, gender, depressive symptoms, conduct disorder diagnosis, family history of substance use disorder, recent cigarette use, lifetime marijuana use, and lifetime other drug use, peak number of binge drinks in past 3 months was inversely associated with left [beta=−0.20,p=0.02,f2=0.06] and right [beta=−0.22,p=0.006,f2=0.08; see Fig. 2 ] hemisphere gray matter, left hemisphere white matter [beta=−0.20,p=0.02,f2=0.08], and marginally associated with right hemisphere white matter [beta=−0.16,p=0.09,f2=0.05] cerebellar volumes in the full sample of 106 teens (see Fig. 2 for simple bivariate relationships according to subgroups). Gender did not significantly interact with peak binge drinks in predicting cerebellar volumes (ps >0.05).
Males showed larger left [beta=0.32,p=0.001] and right [beta=0.32p=0.001] cerebellar gray matter and marginallysmallerright hemisphere white matter [beta=−0.17,p=0.10] volumes than females. As expected, greater ICV was associated with larger left [beta=0.50,p=0.0001] and right [beta=0.49,p=0.0001] cerebellar gray matter, and left [beta=0.68,p=0.0001] and right [beta=0.63,p=0.0001] cerebellar white matter volumes. Higher levels of past-month tobacco use were associated with larger left cerebellar white matter volumes [beta=0.22,p=0.04]. The other covariates included in the above regressions were not linked to any cerebellar compartment volume.
Our first hypothesis was supported: higher peak drinks in the past 3 months predicted smaller cerebellar volumes, for left gray and white matter and right gray matter, and marginally for right white matter compartments in a sample of 106 teens with a range of binge drinking exposure. Differences in volume were seen after controlling for the effects of gender, intracranial volume, depressive symptoms, conduct disorder diagnosis, family history of substance use disorder, recent tobacco use, lifetime marijuana use, and lifetime other drug use. However, contrary to our second hypothesis, this relationship was of a similar magnitude for males as for females.
Alcohol exerts its effects on GABAergic neurons and glutamergic granule cells (e.g., Valenzuela et al., 2010 ) and may influence cerebellar structure by disrupting Purkinje neuron plasticity ( Pierce et al., 2011 ) and upregulating inflammatory mediators (e.g., COX-2 and iNOS), resulting in neuronal death, atrophy or reduced synaptic refinement (Pascual et al, 2007 and Valles et al, 2004). Binge drinking in adolescents may also reduce cholinergic and dopaminergic neurotransmitter gene signaling, resulting in smaller brain volumes and neuronal density ( Coleman et al., 2011 ). Additionally, intense doses of alcohol may impact white matter functioning, as we found moderately reduced cerebellar white matter volumes in the current study and reduced white matter integrity in the superior cerebellar peduncle ( McQueeny et al., 2009 ).
Few studies have examined the relationship between abstinence duration and brain structure in binge drinking teens. Our group recently reported that young adults who demonstrated greater durations of abstinence from alcohol and drugs at a 10-year follow-up had significantly improved executive functioning compared to recent users ( Hanson, et al., 2011 ), providing some hope that sustained abstinence may reverse negative consequences of binge drinking in youth. Recovery in brain structure following binge drinking abstinence has been reported in binge drinking rodents, who demonstrated relatively rapid recovery in ventricular volume and biomarkers of neuronal viability following 7 day of abstinence ( Zahr et al., 2010 ). Future studies are needed to examine whether complete recovery of neurocognitive functioning occurs in adolescents with increasing lengths of abstinence.
It is important to consider that risk factors for binge drinking, such as family history of substance use disorders, conduct disorder, or subclinical symptoms of attention deficit hyperactivity or mood disorder, may lead to abnormal neurodevelopmental trajectories or abnormal cerebellar morphometry, traits that could predispose an adolescent to binge drinking (Hanson et al, 2010, Hill et al, 2007, and Tapert et al, 2002). Of note, the current study statistically controlled for conduct disorder and family history of substance use disorder (neither of which were associated with cerebellar volume in this sample) and excluded individuals with premorbid psychiatric disorders such as attention deficit hyperactivity disorder. Perhaps most striking,(Hill et al, 2007) and (Hill et al, 2011)found that family history of substance use disorder was associated withlargercerebellar volumes in youth, suggesting that our current dose-dependent findings associated withsmallercerebellar volumes may be more related to alcohol use. It is also notable that the cerebellum appears particularly vulnerable to environmental impact, as its additive genetic factor is only 0.49, compared to 0.77–0.89 in cortical areas such as the prefrontal cortex ( Wallace et al., 2006 ). Therefore, environmental factors such as substance exposure, rather than familial traits, may have a greater impact in this region. Additional studies examining the trajectory of cerebellar structure in FH-positive youth before and after the onset of binge drinking are necessary to confirm whether they initially demonstrate larger volumes followed by more drastic reductions in cerebellar volumes with the onset and continuation of hazardous binge drinking patterns.
In contrast to previous studies showing a higher degree of abnormality in female adolescent drinkers than male counterparts ( Squeglia et al., 2009 ; 2011 ), we saw no moderation effect and thus similar alcohol-cerebellum effects across the genders. A potential explanation is that gender moderates the effects of alcohol on the cerebellum in the earlier teen years, but the effects are not as evident into late adolescence and early adulthood ( Tiemeier et al., 2010 ). Consistent with prior research ( Tiemeier et al., 2010 ), boys (binge drinkers and controls) had larger gray but smaller white matter cerebellar volumes than girls of the same age. Although alcohol-cerebellum effects were similar for males and females, the female bingers had significantly fewer drinks per binge (females=6.73 drinks and males=10.35 drinks on average;p=0.001); therefore, it is possible that with continued drinking the female bingers will decline more rapidly than males despite less alcohol exposure. Longitudinal studies are needed to confirm this hypothesis.
Limitations of the current study include that, while we controlled for family history of substance use disorder, conduct disorder, and mood disorder, and excluded subjects with other Axis I comorbid psychiatric disorders, it is impossible to determine whether brain and cognitive abnormalities may have predated the onset of binge drinking with a cross-sectional design. Longitudinal research following teenagers from prior to the onset of alcohol use is needed to explore the influence of early binge drinking and abstinence. In addition, adolescents with recent histories of binge drinking had more tobacco, cannabis, and other drug use than controls; although we controlled statistically for these variables, it is possible that synergistic effects may still exist. Future studies examining the unique and interactive impact of these drugs on cerebellar morphometry are needed. Finally, although relatively unstudied in youth, another possible confound in this sample is thiamine deficiency associated with alcohol consumption and cerebellar structure (e.g.,Baker et al, 1999, Martin et al, 2003, and Pitel et al, 2011).
Given the prevalence of binge drinking and mounting suggestion of its negative impact on brain health, it is critical to disseminate these findings to high school and college students, young military enlistees, therapists, teachers, pediatricians, and parents to help minimize heavy alcohol use in teens. Providing personalized feedback about the negative effects of binge drinking to youth has been shown to be effective at reducing harmful drinking patterns ( Larimer and Cronce, 2007 ). For example, adolescents who engage in heavy drinking could be told that, “Teens who drank more than nine alcohol drinks in one occasion had 1.8 cm3less cerebellar brain volume than teens who drank three or fewer drinks when drinking, on average. The cerebellum is important for coordination and thinking skills.” It could also be imparted that this deficit is less prominent in teens who have not engaged in binge drinking in the past 2 or so months. Therapists could also utilize this information during brief motivational interviewing sessions to help reduce negative effects of binge drinking on the brain.
In summary, we found that increased peak binge drinks during the past 3 months significantly predicted smaller bilateral cerebellar gray matter and left hemisphere cerebellar white matter volumes, and marginally predicted smaller right hemisphere white matter cerebellar volume in a sample of 106 normally developing 16- to 19-year olds. Given the high rates of binge drinking use in teens and emerging adults, these findings highlight an important public health concern. Normative feedback regarding effects of binge drinking on brain health need to be integrated into current prevention, harm-reduction, and treatment programs. More globally, interventions geared towards lowering damaging patterns of binge alcohol use in teens and young adults that have shown evidence of efficacy need to be implemented more aggressively to prevent long-term neuronal damage, and to ensure optimal brain health and cognitive functioning in youth.
This article and much of the research presented within it were made possible through funding by the National Institute on Alcohol Abuse and Alcoholism grant R01 AA13419 and National Institute on Drug Abuse grant R01 DA021182 (PI: Tapert). During manuscript preparation, Dr. Lisdahl was supported by NIDA-funded grants R03DA027457 and R01DA030354. We would like to thank Anthony Scarlett, Diane Goldenberg, and Alejandra Infante for their assistance in establishing intracranial volumes in this sample. Finally, the authors would like to acknowledge the vast scientific contributions of our collaborators and graduate and undergraduate research assistants in the UWM Brain Imaging and Neuropsychology (BraIN) Laboratory and UCSD Adolescent Brain Imaging Project (ABIP), our research participants, and participating schools.
- Achenbach, 1991 T.M. Achenbach. Manual for the Child Behavior Checklist/4–18 and 1991 Profile. (University of Vermont, Department of Psychiatry, Burlington, VT, 1991)
- Baker et al., 1999 K.G. Baker, A.J. Harding, G.M. Halliday, J.J. Kril, C.G. Harper. Neuronal loss infunctional zones of the cerebellum of chronic alcoholics with and without Wernicke's encephalopathy. Neuroscience. 1999;91(2):429-438 Crossref
- Beck et al., 1988 A.T. Beck, R.A. Steer, M.G. Garbin. Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clinical Psychology Review. 1988;8(1):77-100 Crossref
- Castellanos et al., 2002 F.X. Castellanos, P.P. Lee, W. Sharp, N.O. Jeffries, D.K. Greenstein, L.S. Clasen, J.D. Blumenthal, R.S. James, C.L. Ebens, J.M. Walter, A. Zijdenbos, A.C. Evans, J.N. Giedd, J.L. Rapoport. Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. Journal of the American Medical Association. 2002;288(14):1740-1748 Crossref
- Coleman et al., 2011 L.G. Coleman Jr., J. He, J. Lee, M. Styner, F.T. Crews. Adolescent binge drinking alters adult brain neurotransmitter gene expression, behavior, brain regional volumes, and neurochemistry in mice. Alcoholism: Clinical and Experimental Research. 2011;35(4):671-688
- Cox, 1996 R.W. Cox. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research. 1996;29(3):162-173 Crossref
- De Bellis et al., 2000 M.D. De Bellis, D.B. Clark, S.R. Beers, P.H. Soloff, A.M. Boring, J. Hall, A. Kersh, M.S. Keshavan. Hippocampal volume in adolescent-onset alcohol use disorders. American Journal of Psychiatry. 2000;157(5):737-744 Crossref
- De Bellis et al., 2005 M.D. De Bellis, A. Narasimhan, D.L. Thatcher, M.S. Keshavan, P. Soloff, D.B. Clark. Prefrontal cortex, thalamus and cerebellar volumes in adolescents and young adults with adolescent onset alcohol use disorders and co-morbid mental disorders. Alcoholism: Clinical and Experimental Research. 2005;29(9):1590-1600
- Fischl et al., 2002 B. Fischl, D.H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, A.M. Dale. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341-355 Crossref
- Hanson et al., 2010 K.L. Hanson, K.L. Medina, B.J. Nagel, A.D. Spadoni, A. Gorlick, S.F. Tapert. Hippocampal volumes in adolescents with and without a family history of alcoholism. The American Journal of Drug and Alcohol Abuse. 2010;36:161-167 Crossref
- Hanson et al., 2011 K.L. Hanson, K.L. Medina, C.B. Padula, S.F. Tapert, S.A. Brown. How does adolescent alcohol and drug use affect neuropsychological functioning in young adulthood? 10-year outcomes. Journal of Child and Adolescent Substance Abuse. 2011;20:135-154 Crossref
- Hill et al., 2007 S.Y. Hill, S. Muddasani, K. Prasad, J. Nutche, S.R. Steinhauer, J. Scanlon, M. McDermott, M. Keshavan. Cerebellar volume in offspring from multiplex alcohol dependence families. Biological Psychiatry. 2007;61(1):41-47 Crossref
- Hill et al., 2011 S.Y. Hill, S. Wang, H. Carter, K. Tessner, B. Holmes, M. McDermott, N. Zezza, S. Stiffler. Cerebellum volume in high-risk offspring from multiplex alcohol dependence families: association with allelic variation in GABRA2 and BDNF. Psychiatry Research: Neuroimaging. 2011;194(3):304-313
- Jernigan and Gamst, 2005 T. Jernigan, A. Gamst. Changes in volume with age: consistency and interpretation of observed effects. Neurobiology of Aging. 2005;26(9):1271-1274 Crossref
- Johnston et al., 2011 Johnston, L.D., O’Malley, P.M., Bachman, J.G., Schulenberg, J.E., 2011. Monitoring the Future National Results on Adolescent Drug Use: Overview of Key Findings, 2010 (NIH Publication No. 10-7583), National Institute on Drug Abuse, Bethesda, MD.
- Larimer and Cronce, 2007 M.E. Larimer, J.M. Cronce. Identification, prevention, and treatment revisited: individual-focused college drinking prevention strategies 1999–2006. Addictive Behaviors. 2007;32(11):2439-2468 Crossref
- Lenroot and Giedd, 2006 R.K. Lenroot, J.N. Giedd. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews. 2006;30:718-729 Crossref
- Lenroot and Giedd, 2010 R.K. Lenroot, J.N. Giedd. Sex differences in the adolescent brain. Brain and Cognition. 2010;72(1):46-55 Crossref
- Lucas et al., 2001 C.P. Lucas, H. Zhang, P.W. Fisher, D. Shaffer, D.A. Regier, W.E. Narrow, K. Bourdon, M.K. Dulcan, G. Canino, M. Rubio-Stipec, B.B. Lahey, P. Friman. The DISC Predictive Scales (DPS): efficiently screening for diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry. 2001;40(4):443-449 Crossref
- Luna et al., 2001 B. Luna, K.R. Thulborn, D.P. Munoz, E.P. Merriam, K.E. Garver, N.J. Minshew, M.S. Keshavan, C.R. Genovese, W.F. Eddy, J.A. Sweeney. Maturation of widely distributed brain function subserves cognitive development. Neuroimage. 2001;13(5):786-793 Crossref
- Martin et al., 2003 P.R. Martin, C.K. Singleton, S. Hiller-Sturmhöfel. The role of thiamine deficiency in alcoholic brain disease. Alcohol Research and Health. 2003;27(2):134-142
- McQueeny et al., 2009 T. McQueeny, B.C. Schweinsburg, A.D. Schweinsburg, J. Jacobus, S. Bava, L.R. Frank, S.F. Tapert. Altered white matter integrity in adolescent binge drinkers. Alcoholism: Clinical and Experimental Research. 2009;33(7):1278-1285 Crossref
- Medina et al., 2008 K.L. Medina, T. McQueeny, B.J. Nagel, K. Hanson, A.D. Schweinsburg, S.F. Tapert. Prefrontal cortex volumes in adolescents with alcohol use disorders: unique gender effects. Alcoholism: Clinical and Experimental Research. 2008;32:386-394 Crossref
- Medina et al., 2010 K.L. Medina, B.J. Nagel, S.F. Tapert. Cerebellar vermis abnormality in adolescent marijuana users. Psychiatry Research: Neuroimaging. 2010;182(2):152-159 Crossref
- Medina et al., 2007 K.L. Medina, A.D. Schweinsburg, M. Cohen-Zion, B.J. Nagel, S.F. Tapert. Effects of alcohol and combined marijuana and alcohol use during adolescence on hippocampal asymmetry. Neurotoxicology and Teratology. 2007;29:141-152 Crossref
- Nagel et al., 2005 B.J. Nagel, A.D. Schweinsburg, V. Phan, S.F. Tapert. Reduced hippocampal volume among adolescents with alcohol use disorders without psychiatric comorbidity. Psychiatry Research: Neuroimaging. 2005;139(3):181-190 Crossref
- Pascual et al., 2007 M. Pascual, A.M. Blanco, O. Cauli, J. Minarro, C. Guerri. Intermittent ethanol exposure induces inflammatory brain damage and causes long-term behavioural alterations in adolescent rats. European Journal of Neuroscience. 2007;25(2):541-550 Crossref
- Petersen et al., 1988 A.C. Petersen, L. Crockett, M. Richards, A. Boxer. A self-report measure of pubertal status: reliability, validity, and initial norms. Journal of Youth and Adolescence. 1988;17:117-133 Crossref
- Pierce et al., 2011 D.R. Pierce, A. Hayar, D.K. Williams, K.E. Light. Olivary climbing fiber alterations in PN40 rat cerebellum following postnatal ethanol exposure. Brain Research. 2011;1378:54-65 Crossref
- Pitel et al., 2011 A.L. Pitel, N.M. Zahr, K. Jackson, S.A. Sassoon, M.J. Rosenbloom, A. Pfefferbaum, E.V. Sullivan. Signs of preclinical Wernicke's encephalopathy and thiamine levels as predictors of neuropsychological deficits in alcoholism without Korsakoff's syndrome. Neuropsychopharmacology. 2011;36(3):580-588
- Rice et al., 1995 J.P. Rice, T. Reich, K.K. Bucholz, R.J. Neuman, R. Fishman, N. Rochberg, V.M. Hesselbrock, J.I. Nurnberger Jr., M.A. Schuckit, H. Begleiter. Comparison of direct interview and family history diagnoses of alcohol dependence. Alcoholism: Clinical and Experimental Research. 1995;19:1018-1023 Crossref
- Robins et al., 1996 Robins, L., Cottler, L., Bucholz, K., Compton, W., 1996. The Diagnostic Interview Schedule, Version 4.0.(DIS 4.0). Washington University, St. Louis, MO.
- Schmahmann and Sherman, 1998 J.D. Schmahmann, J.C. Sherman. The cerebellar cognitive affective syndrome. Brain. 1998;121(Pt 4):561-579 Crossref
- Sobell and Sobell, 1992 Sobell, L.C., Sobell, M.B., 1992. Timeline follow-back: a technique for assessing self-reported alcohol consumption. In: Litten, R.Z., Allen, J.P. (Eds.), Measuring Alcohol Consumption: Psychosocial and Biochemical Method. Humana Press, Totowa, NJ, pp. 41-72.
- Spear, 2010 L.P. Spear. The behavioral neuroscience of adolescence. Neuroscience and Biobehavioral Reviews. 2010;24:417-463
- Squeglia et al., Squeglia, L.M., Dager Schweinsburg, A., Pulido, C., Tapert, S.F., 2011. Adolescent binge drinking linked to abnormal spatial working memory brain activation: differential gender effects. Clinical and Experimental Research 35(10), 1831–1841
- Squeglia et al., 2009 L.M. Squeglia, A.D. Spadoni, M.A. Infante, M.G. Myers, S.F. Tapert. Initiating moderate to heavy alcohol use predicts changes in neuropsychological functioning for adolescent girls and boys. Psychology of Addictive Behaviors. 2009;23(4):715-722 Crossref
- Stewart and Brown, 1995 D.G. Stewart, S.A. Brown. Withdrawal and dependency symptoms among adolescent alcohol and drug abusers. Addiction. 1995;90:627-635 Crossref
- Sullivan and Pfefferbaum, 2005 E.V. Sullivan, A. Pfefferbaum. Neurocircuitry in alcoholism: a substrate of disruption and repair. Psychopharmacology. 2005;180:583-594 Crossref
- Tapert et al., 2002 S.F. Tapert, M.V. Baratta, A.M. Abrantes, S.A. Brown. Attention dysfunction predicts substance involvement in community youths. Journal of the American Academy of Child and Adolescent Psychiatry. 2002;41(6):680-686 Crossref
- Tiemeier et al., 2010 H. Tiemeier, R.K. Lenroot, D.K. Greenstein, L. Tran, R. Pierson, J.N. Giedd. Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study. Neuroimage. 2010;1(49(1)):63-70 Crossref
- Valenzuela et al., 2010 C.F. Valenzuela, B. Lindquist, P.A. Zamudio-Bulcock. A review of synaptic plasticity at Purkinje neurons with a focus on ethanol-induced cerebellar dysfunction. International Review of Neurobiology. 2010;91:339-372 Crossref
- Valles et al., 2004 S.L. Valles, A.M. Blanco, M. Pascual, C. Guerri. Chronic ethanol treatment enhances inflammatory mediators and cell death in the brain and in astrocytes. Brain Pathology. 2004;14(4):365-371
- Wallace et al., 2006 G.L. Wallace, J. Eric Schmitt, R. Lenroot, E. Viding, S. Ordaz, M.A. Rosenthal, E.A. Molloy, L.S. Clasen, K.S. Kendler, M.C. Neale, J.N. Giedd. A pediatric twin study of brain morphometry. Journal of Child Psychology and Psychiatry. 2006;47(10):987-993 Crossref
- Wilkinson, 1993 Wilkinson, G.S., 1993. The Wide Range Achievement Test-3 Administration Manual. Jastak Associates, Wilmington, DE.
- Zahr et al., 2010 N.M. Zahr, D. Mayer, T. Rohlfing, M.P. Hasak, O. Hsu, S. Vinco, J. Orduna, R. Luong, E.V. Sullivan, A. Pfefferbaum. Brain injury and recovery following binge ethanol: evidence from in vivo magnetic resonance spectroscopy. Biological Psychiatry. 2010;67(9):846-854 Crossref
- Zucker et al., 1994 R.A. Zucker, D.A. Ellis, H.E. Fitzgerald. Developmental evidence for at least two alcoholisms: I. Biopsychosocial variation among pathways into symptomatic difficulty. T.F. Babor, V. Hesselbrock, R.E. Meyer, W. Shoemaker (Eds.) Types of Alcoholics: Evidence from Clinical, Experimental, and Genetic Research (The New York Academy of Sciences, New York, 1994) 134-146 Crossref
a Department of Psychology, University of Wisconsin-Milwaukee, 2241 E. Hartford Ave, Milwaukee, WI 53211, United States
b Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
c VA San Diego Healthcare System, San Diego, CA, United States
d Department of Psychiatry, University of California, San Diego, CA, United States
e San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
f Department of Psychology, University of Cincinnati, Cincinnati, OH, United States
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