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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Addiction. Author manuscript; available in PMC Jan 1, 2013.
Published in final edited form as:
PMCID: PMC3237873
NIHMSID: NIHMS316479

Psychological dysregulation, white matter disorganization and substance use disorders in adolescence

Abstract

Aims

Adolescents with substance use disorders (SUD) have difficulties with cognitive, behavioral and affective regulation. White matter (WM) disorganization has been observed in adolescents with SUD and may be related to psychological dysregulation. This study compared adolescents with SUD and control adolescents to investigate relationships among psychological dysregulation, WM disorganization, and SUD symptoms.

Design

Cross-sectional observation.

Setting

Adolescents with SUD were recruited from SUD treatment programs. Controls were recruited from the community.

Participants

The 55 participants were ages 14–19; 35 with SUD, 20 controls without SUD.

Measurements

Psychological dysregulation was characterized by the Behavior Rating Inventory of Executive Function. WM disorganization was measured by diffusion tensor imaging, and fractional anisotropy, radial diffusivity and axial diffusivity were examined within cortical regions of interest.

Findings

Compared to controls, SUD adolescents showed significantly greater psychological dysregulation and prefrontal and parietal WM disorganization. WM disorganization was positively correlated with psychological dysregulation and cannabis-related symptoms. In multivariate mediation models, the results were consistent with both the Neurodevelopmental Immaturity model, in which WM disorganization leads to psychological dysregulation and cannabis-related symptoms, and with the Substance Effects Model, in which cannabis-related symptoms lead to WM disorganization and psychological dysregulation.

Conclusions

In adolescents, substance use disorder and psychological dysregulation appear to be associated with reduced frontoparietal network white matter maturation.

Keywords: adolescents, substance use disorders, neuroimaging

INTRODUCTION

Adolescents with substance use disorders (SUD) have difficulties with cognitive, behavior and affective regulation and evidence of neurodevelopmental immaturity [see 1 for review; 2,3]. Such difficulties may indicate pre-existing deficits that contribute to SUD or adverse substance use effects. Children and adolescents with psychopathology reflecting psychological dysregulation, such as disruptive behavior disorders (DBD: i.e., attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder) have been found to have white matter (WM) disorganization [4,5,6]. WM disorganization may indicate neurodevelopmental immaturity contributing to both psychological dysregulation and SUD (i.e., Neurodevelopmental Immaturity Model). Alternatively, neurotoxic substance effects may lead to neurobiological deficits (i.e., Substance Effects Model). In this study, we examined relationships among adolescent SUD symptoms, psychological dysregulation, and WM disorganization to advance understanding of microstructural brain characteristics and SUD in adolescence.

Psychological dysregulation refers to deficiencies in cognitive functioning, behavioral inhibition, and emotional regulation that constitute a trait essentially synonymous with executive functioning, broadly defined, and neurobehavioral dysregulation [reviews:1,7]. Some mental disorders, including DBD and major depressive disorder (MDD), are thought to constitute clinical manifestations of psychological dysregulation [1]. DBD and MDD tend to cluster in adolescents with SUD [8, see 9 for review]. Dimensional constructs representing attentional, conduct and mood problems have been found to provide psychometrically valid representations of psychological dysregulation that predict adolescent SUD [10, 11]. Psychological dysregulation scales have been found to be elevated in adolescents with SUD compared to reference adolescents [12]. Prior studies have not examined psychological dysregulation in relation to WM disorganization and adolescent SUD.

According to the Neurodevelopmental Immaturity Model, the psychological dysregulation phenotype reflects delays or deficits in neuromaturation that precede and contribute to SUD. The prefrontal cortex (PFC) plays an important role in psychological regulation during adolescence, and its interaction with other functionally specialized brain regions via white matter tracts is critical for integrative brain functions [review:13]. WM tracts projecting to PFC continue to develop throughout adolescence [14,15,16]. The PFC is connected to the parietal cortex by the superior longitudinal fasciculus (SLF) to form the frontoparietal network [17]. Maturation of the frontoparietal network has been theorized to be critical for optimal psychological regulation [17, 18]. Frontoparietal network immaturity during adolescence may contribute to disinhibited, high risk behaviors including SUD.

According to the Substance Effects Model, SUD disrupts WM. WM volume loss has been documented in SUD adults [19,20] and smaller PFC WM volumes have been reported in SUD adolescents [21]. However, regional cerebral volumes have not shown a consistent relationship with psychological dysregulation and substance use [22]. Compared to WM volumes, WM microstructure determined by diffusion tensor imaging (DTI) may be more sensitive in detecting SUD effects [23]. DTI quantifies water diffusion in WM, which reflects axonal organization as well as axonal caliber and myelin thickness [24]. Fractional anisotropy (FA) is a summary directional diffusion indicator calculated using variables that comprise more specific diffusion indices, including axial diffusivity (AD; diffusion along the axon) and radial diffusivity (RD; diffusion perpendicular to the axon) [25]. (For brevity, these DTI indicators will be interpreted as representing WM organization.) In adults, substance-related WM disorganization, more so than volume changes, have been found to correlate with cognitive performance [26]. Actively developing PFC WM in adolescents may be particularly vulnerable to SUD effects [1]. WM disorganization has been reported among adults with adolescent-onset cannabis use [27], a finding interpreted to indicate long-term cannabis effects [28]. Other studies have not observed cannabis effects [29].

A few studies have examined the relationship between DTI indicators and adolescent substance use or SUD. Bava and colleagues [2] compared adolescents with heavy marijuana and alcohol use with controls using Tract Based Spatial Statistics (TBSS), and found that substance using adolescents exhibited lower FA in 10 brain regions, including the SLF. Ashtari and colleagues [30] found that adolescent heavy marijuana use was associated with reduced FA and increased RD in the arcuate fasciculus, which is relevant for frontotemporal integration. Alcohol-specific effects were suggested in a study by McQueeney and colleagues [31], in which adolescent binge drinkers, compared to non-binge drinking controls, had lower FA in 18 TBSS identified regions, including seven tracts projecting to the PFC (i.e., four corpus callosum regions, the left and right corona radiata, and the right SLF). In contrast, De Bellis and colleagues [32] compared adolescents with alcohol use disorder to control adolescents on corpus callosum FA and mean diffusivity (MD) and found no evidence for alcohol associated WM disorganization.

In a report on a subset of the subjects described here [3], we compared 24 SUD adolescents with 12 matched controls using TBSS, and found a large SLF cluster of significantly lower FA values in SUD adolescents, with similar group differences for AD and RD. A significant SUD group by gender interaction was observed for SLF, with the difference between SUD adolescents and control adolescents being greater among females than among males. In sum, TBSS-based research has most consistently indicated that SUD adolescents have WM disorganization in areas subserving the frontoparietal network (e.g., SLF). However, prior studies have not specifically tested theoretically derived hypotheses examining adolescent SUD characteristics and WM disorganization in specific regions of interest (ROI).

Statistical analyses with DTI variables use voxel-based or ROI approaches [33]. Voxel-based analyses, including TBSS, examine the entire brain in a model-free manner. The ROI approach used here, compared to TBSS, tests specific hypotheses involving ROIs with increased sensitivity. We used an automated segmentation method to distinguish grey and white matter and to identify cortical regions. By utilizing individual subject brain features to identify regions, this method avoids the distortion that may occur in TBSS with alignment to a common registration target [33].

The present study is the first examination of relationships among ROI WM disorganization, psychological dysregulation, and adolescent SUD. The hypotheses were: (1) psychological dysregulation will be elevated in SUD adolescents compared to controls; (2) PFC and parietal WM FA, indicating frontoparietal network WM maturity, will be lower in SUD adolescents; and (3) PFC and parietal FA will be inversely correlated with psychological dysregulation. While acknowledging the limitations of these cross-sectional data, the relationships among these variables were examined to explore the Neurodevelopmental Immaturity Model and the Substance Effects Model.

METHOD

Participants

Adolescents with SUD (n=35), ages 14–19, were recruited from SUD intensive out-patient treatment programs (see 34 for details). The controls (n=20) were adolescents without SUD recruited from the community. Control subjects were identified by random digit dialing and were screened for eligibility. Exclusion criteria for both groups included an inability to communicate in English, developmental disorders impairing participation, and a history of significant brain injury or concussion. Control subjects were excluded if they had any SUD history. This study was approved by the University of Pittsburgh Human Subjects Institutional Review Board and was conducted in accordance with the approved protocol. Written assent from the minor adolescent and written consent from a parent was obtained. Adolescents age ≥18 provided their own written informed consent.

SUD adolescents were typically assessed within one month of initiating outpatient SUD treatment. The SUD and control groups (Table 1) were not significantly different on demographic characteristics. The average participating family was middle class according to the Hollingshead Two-factor Index [35]. The SUD (left-handed: n=2) and control (left-handed: n=2) were not significantly different on Edinburgh Handedness Inventory [36,37,38] score (χ2=.3, d.f.=1, p=.6). Compared to the control group, the SUD group was significantly lower on IQ (Table 1). The most common current SUD was cannabis use disorders (n=31 or 89%), followed by alcohol use disorders (n=18 or 51%), and other drug use disorders (n=16 or 46%). Among 16 subjects with other drug disorders, the most common were opioid-related (n=12) and cocaine-related (n=9). The majority of SUD adolescents had diagnoses involving two or more substances (n=19), with the remaining involving only cannabis (n=11), alcohol (n=4) or opiates (n=1). SUD subjects were more likely than controls to have used substances in the 30 days prior to the imaging session (Table 1). The most frequently used substances were nicotine, followed by cannabis and alcohol. Other drugs were combined and included opioid use (6 subjects) and cocaine use (3 subjects). The average SUD onset age was 14.7 years old (s.d.:1.6; range 11 to 18). A lifetime history of mental disorders was significantly more common in the SUD group than in the Control group (Table 1), including conduct disorder (CD), attention deficit hyperactivity disorder (ADHD), and MDD. The groups were not significantly different on oppositional defiant disorders (ODD). (Other less common mental disorders were not examined.)

Table 1
Demographic and clinical characteristics for adolescents with SUD and control groups.

Clinical Measures

DSM-IV SUD diagnoses

A version of the Structured Clinical Interview for DSM-IV SUDs [SCID: 39] adapted for adolescents was used to determine SUD diagnoses and symptom counts. Adaptations included providing examples of adolescent-relevant symptoms (i.e., school grades dropping due to substance use) and developmentally specific probes. The adapted SCID demonstrated moderate to high inter-rater reliability for symptom ratings and acceptable concurrent validity in adolescents [39]. Substance use in the prior 30 days was determined by a structured interview using the Time Line Follow Back approach [40].

Mental disorders

The Schedule for Affective Disorders and Schizophrenia [K-SADS; 8,41] was used to assess lifetime occurrence of other DSM-IV Axis I psychopathology. Psychopathology Symptoms was the sum of DBD and MDD symptoms in the prior six months, considered indicative of psychological dysregulation. The K-SADS has demonstrated acceptable inter-rater reliability [8].

Psychological dysregulation

The Behavior Rating Inventory of Executive Function-Self-Report Version [BRIEF-SR; 42,43] is an 80-item self-report executive functioning rating scale. The items reference cognitive (e.g., “I forget instructions easily;” “I have a short attention span”), behavioral (i.e., “I have trouble sitting still;” “I talk at the wrong time”) and mood (i.e. “I get upset easily”) difficulties. The instrument yields a summary t-score, used here, and eight subscales. A higher score indicates more difficulties. As defined by scoring instructions [42], BRIEF t-scores over 2 standard deviations above the normative mean were classified as clinically relevant.

MRI Procedures

Image Acquisition

MRI images were acquired on a Siemens 3T Allegra Scanner (Siemens Medical Solutions, Erlangen, Germany). T1 weighted magnetization-prepared rapid gradient echo (MPRAGE) images were acquired for morphometric analyses (scan parameters: TR=1400ms; TE=2.48ms; FOV=256×256; 176 1mm slices × 2; matrix 256 × 256). In addition to the structural scan, diffusion images were acquired using standard fast echo-planar imaging (TR=6500ms; TE=88ms; FOV=205×205; b=1000s/mm2; 46 3mm slices × 12 directions in addition to b=0). To optimize signal to noise ratio, the sequence was collected twice.

Image Processing and Analysis

Volumetric and ROI analyses

Cortical reconstruction processing and volumetric segmentation were performed with Freesurfer [4451]. Processing included motion correction and averaging of volumetric T1 weighted images, removal of non-brain tissue, automated Talairach transformation, tessellation of the gray/white matter boundary, automated topology correction, and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders. Freesurfer procedures have been validated against histological analysis and manual measurements [52,53].

DTI

DTI images were processed with Freesurfer. Diffusion data was motion and eddy current corrected. Tensors were calculated using DTI GLM Fit and registered to individual subject anatomical features using FLIRT [54] and to standard Talairach space. Since DTI is susceptible to motion artifacts, we evaluated images visually and by motion parameters. Rotational and translational motions were quantified using FSL [54]. On the resulting displacement index, SUD subjects (mean: 0.44, s.d.: 0.10) and control subjects (0.45; s.d.: 0.09) were not significantly different (t=−0.2, d.f.=53; p=.8).

Regions of interest analyses

ROIs were created by adding bilateral Freesurfer regions. These regions were constructed so as to define areas functionally relevant for psychological regulation (e.g., prefrontal and parietal) while sufficiently inclusive so that mean FA values were anatomically meaningful. The WM ROIs were as follows: PFC: frontal pole, frontal superior, frontal caudalmiddle, frontal rostralmiddle, parsopercularis and parstriangularis; Parietal: parietal inferior and parietal superior; Orbitofrontal: frontal lateral orbital, frontal medial orbital, parsorbitalis; Temporal: temporal inferior, temporal middle, temporal pole, temporal superior, temporal transverse; Cingulate: cingulate caudal-anterior, cingulate rostral-anterior, isthmus and posterior. FA ROI analyses were done by resampling FA data into each individual subject's anatomical space and then extracting mean FA values for WM ROIs.

Statistical analyses and models

The SUD and control groups were compared on psychological dysregulation indicators and regional WM FA. These group comparisons included demographic covariates (i.e., age and race) and group-by-gender interactions. For hypothesized relationships, specifically those involving PFC and parietal ROIs, tests where p<.05 were considered statistically significant. For exploratory ROI analyses, interpretations including multiple comparison corrections with adjusted p values (i.e., .05/3) have been included. Variables showing significant group differences were further explored in correlational models. Using multivariate regression models with covariates, selected regional white matter FA variables were correlated with psychological dysregulation characteristics, SUD symptoms involving specific substance classes (i.e., alcohol, cannabis, or other illicit drugs), and recent substance use (i.e., the number of days of alcohol, cannabis, nicotine or other drug use in the past 30 days). The selection of variables for subsequent model testing was informed by these correlations. Finally, two multivariate mediation models using step-wise multiple regression were used to examine relationships among psychological dysregulation, WM disorganization and SUD symptoms. In addition to examining FA, these models included AD and RD. A mediation model was considered supported where the previously significant relationship between the independent and dependent variables was no longer significant in the adjusted model [see 5557 for reviews].

RESULTS

Psychological Dysregulation

On the BRIEF score, the groups were significantly different (Table 2; partial Eta2 =.99), and the demographic covariates and group-by-gender interaction did not account for significant variance. Categorizing subjects with BRIEF t-scores ≥70 as clinically relevant, 29% (n=10) of the SUD group and none (0%) of the Control group had clinically relevant scores (χ2=7.2, d.f.=1, p=.007). The SUD and Control groups were also significantly different on Psychopathology Symptoms (partial Eta2=.98), and the covariates and group-by-gender interaction did not account for significant variance. BRIEF scores were significantly correlated with Psychopathology Symptoms (r=.67, d.f.=54, p<.001).

Table 2
Psychological dysregulation and white matter variables for adolescents with substance use disorders (SUD) and control adolescents.

White Matter Volumes and FA

The comparisons of SUD and Control group on WM ROI volumes are presented in Table 2. The statistical tests included demographic covariates and group-by-gender interactions. None of these group comparisons were statistically significant, and the group-by-gender interactions did not account for significant variance.

In the PFC WM ROI, the SUD group compared to the control group showed significantly lower mean FA (Table 2; partial Eta2=.83). Similarly, in the parietal WM ROI, the SUD group showed significantly lower mean FA (partial Eta2=.90). For orbitofrontal, cingulate and temporal ROIs, the groups did not significantly differ. For all these group comparisons, the main effects of demographic variables examined as covariates (i.e., age, gender and race) and the SUD group by gender interaction did not account for significant variance. Excluding left hand dominant subjects (n=4), the results were essentially unchanged. The significant group differences for PFC and parietal FA supported their inclusion in subsequent analyses.

Psychological dysregulation, SUD symptoms and WM disorganization relationships

BRIEF score accounted for significant variance in PFC and parietal FA (Table 3), BRIEF score and IQ were significant correlated (r=−41, p=.003) and IQ did not account for significant additional variance in PFC FA (r=0.14, p=0.3) or parietal FA (r=0.19, p=0.2). PFC and parietal FA were significantly correlated with the cannabis-related symptom count (i.e., Cannabis Symptoms), and were not significantly correlated with other substance-related symptoms or substance use frequencies. Consequently, multivariate mediational models utilized Cannabis Symptoms in subsequent analyses.

Table 3
Correlations for PFC and parietal regional fractional anisotropy with BRIEF score, substance use disorder symptoms, and recent substance use frequency.

Testing the Neurodevelopmental Immaturity Model in multivariate mediation models, after accounting for demographic covariates, PFC and parietal WM disorganization indices (i.e., FA, AD and RD) accounted for significant additional variance in Cannabis Symptoms (R2 change=.33, F change=3.8, d.f.=6,45, p=.004). After including BRIEF as a mediating variable, PFC and parietal WM disorganization indices did not account for significant additional variance in Cannabis Symptoms (R2 change=.06, F change=1.3, d.f.=3,46, p=.3). The results indicated, consistent with the Neurodevelopmental Immaturity Model, that BRIEF mediated the relationship between WM disorganization (independent variable) and Cannabis Symptoms (dependent variable).

Testing the Substance Effects Model, after accounting for the effects of demographic covariates, Cannabis Symptoms accounted for significant additional variance in BRIEF (R2 change=.24, F change=16.0, d.f.=1,49, p<.001). After including PFC and parietal WM disorganization indices as a mediating variable, Cannabis Symptoms did not account for additional variance in BRIEF scores (R2 change=.05, F change =3.8, d.f.=1,43, p=.06). These results indicated that, consistent with the Substance Effects Model, WM disorganization mediated the relationship between Cannabis Symptoms (independent variable) and BRIEF (dependent variable).

DISCUSSION

Prior studies have not examined relationships among SUD severity, WM disorganization and psychological dysregulation in adolescents. Consistent with prior research [12], adolescents with SUD showed elevated psychological dysregulation indicated by BRIEF and psychopathology symptoms. Adolescents with SUD, compared to control adolescents, showed PFC and parietal WM disorganization, with large effect size estimates. Since the SUD and control groups were not significantly different on WM ROI volumes, these positive findings are consistent with prior research suggesting that DTI indices provide more sensitive indicators of SUD effects. While the anatomical location of WM disorganization in SUD adolescents using the TBSS approach [3] and this ROI method differed, the common involvement of the SLF in both results points to a unifying neuroanatomical interpretation. In contrast to the TBSS study [3], however, the present analyses did not find significant SUD group by gender interactions on regional FA. Since both these studies involved relatively small samples, clarification of differential relationships by gender will require further research. Having established these group differences, subsequent analyses were conducted to provide additional explanatory information.

Consistent with the Neurodevelopmental Immaturity Model, the multivariate mediation models could be interpreted to indicate that immature PFC and parietal WM predicted psychological dysregulation which, in turn, predicted cannabis-related symptoms. A small effect size was noted. These findings support the hypothesis that the frontoparietal network, with the SLF as an important constituent, provides a neurobiological foundation for psychological regulation [17]. The SLF, the largest of the long association fibers, is the tract that contributes most to the PFC and parietal WM ROIs [58,59]. It is important to emphasize, however, that this cross-sectional study could not provide critical temporal information implied by this interpretation.

The findings were also consistent with the Substance Effects Model. That is, the alternative multivariate mediation analyses could be interpreted to indicate that cannabis-related symptoms predicted WM disorganization, and WM disorganization predicted psychological dysregulation. Here too, a small effect size was noted. These correlations are consistent with prior studies examining cannabis effects on component skills required for psychological regulation, such as working memory [60,61,62]. The PFC has been noted to have a relatively high density of cannabinoid receptors and may be particularly susceptible to cannabis effects [28].

The examination of specific substance-related variables in relationship to PFC and parietal FA indicated a particularly strong association with cannabis-related symptoms, and not with alcohol-related symptoms. This finding contrasts with prior research showing a relationship between alcohol use and WM disorganization [31]. The effects of cannabis and alcohol use on regional cerebral activation and responses on neuropsychological testing have also been studied [63,64,65]. The relative effects of alcohol and cannabis on adolescent functioning and neuromaturation remain unclear. We caution against over-interpretation of the differential correlations noted on substance-related variables. As in other similar studies, the SUD subjects in this study may have had idiosyncratic features. The generalizability of these findings will need to be tested in larger studies with refined methods. More importantly, these cross-sectional studies have not been designed to distinguish the Neurodevelopmental Immaturity Model from the Substance Effects Model.

In addition to the cross-sectional design, this study had additional limitations. Although comparable to prior studies, optimal sample sizes would be considerably larger than those available here. The relatively small samples limited the extent to which exploratory analyses could be reasonably supported while taking into consideration correction for multiple comparisons. The examination of psychopathology dimensions distinct from the psychological dysregulation construct, for example, may be of interest in future studies. Laboratory alcohol and drug testing for recent use was not conducted and would have refined this methodology [66]. The examination of objective psychological regulation indicators by neuropsychological testing may have provided information distinct from the self-report BRIEF scale [67]. Prenatal substance exposure may influence brain development [see 68 for review] and was not examined here.

In summary, this study found significant relationships among WM disorganization, psychological dysregulation, and SUD in adolescents. The PFC actively develops during adolescence and the integration of the PFC with other brain areas through WM tracts is thought to be critical in cognitive, behavioral and affective regulation. With cannabis use common in adolescence and cannabis use disorders occurring in a substantial portion of adolescents and young adults, the possibility that cannabis involvement may lead to WM disorganization and psychological dysregulation is worrisome. A larger, systematically recruited sample assessed in early adolescence prior to the initiation of substance use and followed over several years would constitute an approach that may advance understanding of the relationships studied here.

Acknowledgements

This research was supported by grants R21AA016272, R01AA04357, K02AA00291, K02AA018195, K01DA018698, P50DA05605.

Footnotes

Declarations of interest None.

References

1. Clark DB, Thatcher DL, Tapert SF. Alcohol, psychological dysregulation, and adolescent brain development. Alcohol Clin Exp Res. 2008;32(1):375–85. [PubMed]
2. Bava S, Frank LR, McQueeny T, Schweinsburg BC, Schweinsburg AD, Tapert SF. Altered white matter microstructure in adolescent substance users. Psychiatry Res. Neuroimaging. 2009;173:228–37. [PMC free article] [PubMed]
3. Thatcher DL, Pajtek S, Chung T, Terwilliger RA, Clark DB. Gender differences in the relationship between white matter organization and adolescent substance use disorders. Drug Alcohol Depend. 2010;110:55–61. [PMC free article] [PubMed]
4. Lipton ML, Gulko E, Zimmerman ME, Friedman BW, Kim M, Gellella E, et al. Diffusion-tensor imaging implicates prefrontal axonal injury in executive function impairment following very mild traumatic brain injury. Radiology. 2009;252(3):816–24. [PubMed]
5. Skranes J, Lohaugen GC, Martinussen M, Indredavik MS, Dale AM, Haraldseth O, et al. White matter abnormalities and executive function in children with very low birth weight. Neuroreport. 2009;20(3):263–6. [PubMed]
6. Li T, Mathews VP, Wang Y, Dunn D, Kronenberger W. Adolescents with disruptive behavior disorder investigated using an optimized MR diffusion tensor imaging protocol. Ann NY Acad Sci. 2005;1064:184–92. [PubMed]
7. Clark DB, Winters KC. Measuring risks and outcomes in substance use disorders prevention research. J Consult Clin Psychol. 2002;70(6):1207–23. [PubMed]
8. Clark DB, Pollock N, Bukstein OG, Mezzich AC, Bromberger JT, Donovan JE. Gender and comorbid psychopathology in adolescents with alcohol dependence. J Am Acad Child Adolesc. Psychiatry. 1997;36:1195–1203. [PubMed]
9. Clark DB. The natural history of adolescent alcohol use disorders. Addiction. 2004;99:5–22. [PubMed]
10. Tarter RE, Kirisci L, Mezzich A, Cornelius J, Pajer K, Vanyukov M, et al. Neurobehavioral disinhibition in childhood predicts early age onset substance use disorder. Am J Psychiatry. 2003;160:1078–85. [PubMed]
11. Vanyukov MM, Kirisci L, Tarter RE, Ridenour TA, Reynolds MD, Maher B, et al. Measurement of the risk for substance use disorders: phenotypic and genetic analysis of an index of common liability. Behav Genet. 2009;39:233–44. [PMC free article] [PubMed]
12. Martin CS, Lynch KG, Pollock NK, Clark DB. Gender differences and similarities in the personality correlates of adolescent alcohol problems. Psychol Addict Behav. 2000;14:121–33. [PubMed]
13. Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev. 2000;24(4):417–63. [PubMed]
14. Ashtari M, Cervellione KL, Hasan KM, Wu J, McIlree C, Kester H, et al. White matter development during late adolescence in healthy males: a cross-sectional diffusion tensor imaging study. Neuroimage. 2007;35:501–10. [PubMed]
15. Klingberg T, Vaidya CJ, Gabrieli JD, Moseley ME, Hedehus M. Myelination and organization of the frontal white matter in children: a diffusion tensor MRI study. Neuroreport. 1999;10(13):2817–21. [PubMed]
16. Lenroot RK, Giedd JN. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev. 2006;30(6):718–29. [PubMed]
17. Olesen PJ, Nagy Z, Westerberg H, Klingberg T. Combined analysis of DTI and fMRI data reveals a joint maturation of white and grey matter in a fronto-parietal network. Brain Res Cogn Brain Res. 2003;18:48–57. [PubMed]
18. Fassbender C, Simoes-Franklin C, Murphy K, Hester R, Meaney J, Robertson IH, et al. The role of a right fronto-parietal network in cognitive control: common activations for “cues-to-attend” and response inhibition. J Psychophysiol. 2006;20(4):286–96.
19. Agartz I, Brag S, Franck J, Hammarberg A, Okugawa G, Svinhufvud K, et al. MR volumetry during acute alcohol withdrawal and abstinence: a descriptive study. Alcohol and Alcoholism. 2003;38(1):71–8. [PubMed]
20. Rosenbloom M, Sullivan EV, Pfefferbaum A. Using magnetic resonance imaging and diffusion tensor imaging to assess brain damage in alcoholics. Alcohol Res Health. 2003;27(2):146–52. [PubMed]
21. DeBellis MD, Narasimhan A, Thatcher DL, Keshavan MS, Soloff P, Clark DB. Prefrontal cortex, thalamus and cerebellar volumes in adolesent onset alcohol use disorders. Alcohol Clin Exp Res. 2005;29:1590–1600. [PubMed]
22. Medina KL, McQueeny T, Nagel BJ, Hanson KL, Yang TT, Tapert SF. Prefrontal cortex morphometry in abstinent adolescent marijuana users: subtle gender effects. Addict Biol. 2009;14:457–68. [PMC free article] [PubMed]
23. Pfefferbaum A, Sullivan EV. Microstructural but not macrostructural disruption of white matter in women with chronic alcoholism. Neuroimage. 2002;15(3):708–18. [PubMed]
24. Paus T. Growth of white mater in the adolescent brain: myelin or axon? Brain Cogn. 2010;72:26–35. [PubMed]
25. Qiu D, Tan L, Zhou K, Khong P. Diffusion tensor imaging of normal white matter maturation from late childhood to young adulthood: voxel-wise evaluation of mean diffusivity, fractional anisotropy, radial and axial diffusivities, and correlation with reading development. Neuroimage. 2008;41:223–32. [PubMed]
26. Pfefferbaum A, Sullivan EV, Hedehus M, Adalsteinsson E, Lim KO, Moseley M. In vivo detection and functional correlates of white matter microstructural disruption in chronic alcoholism. Alcohol Clin Exp Res. 2000;24(8):1214–21. [PubMed]
27. Arnone D, Barrick TR, Chengappa S, Mackay CE, Clark CA, Abou-Saleh MT. Corpus callosum damage in heavy marijuana use: preliminary evidence from diffusion tensor tractography and tract-based spatial statistics. Neuroimage. 2008;41:1067–74. [PubMed]
28. Jacobus J, Bava S, Cohen-Zion M, Mahmood O, Tapert SF. Functional consequences of marijuana use in adolescents. Pharmacol Biochem Behav. 2009;92:559–565. [PMC free article] [PubMed]
29. Delisi LE, Bertisch HC, Szulc KU, Majcher M, Brown K, Bappal A, et al. A preliminary DTI study showing no brain structural change associated with adolescent cannabis use. Harm Reduct J. 2006;3:17. [PMC free article] [PubMed]
30. Ashtari M, Cervellione K, Cottone J, Ardekani BA, Kumra S. Diffusion abnormalities in adolescents and young adults with a history of heavy cannabis use. J Psychiatr Res. 2008;43:189–204. [PMC free article] [PubMed]
31. McQueeny T, Schweinsburg BC, Schweinsburg AD, Jacobus J, Bava S, Frank LR, et al. Altered white matter integrity in adolescent binge drinkers. Alcohol Clin Exp Res. 2009;33(7):1278–85. [PMC free article] [PubMed]
32. DeBellis MD, Van Voorhees E, Hooper SR, Gibler N, Nelson L, Hege SG, et al. Diffusion tensor measures of the corpus callosum in adolescents with adolescent onset alcohol use disorders. Alcohol Clin Exp Res. 2008;32(1):395–413. [PMC free article] [PubMed]
33. Niogi SN, Mukherjee P, McCandliss BD. Diffusion tensor imaging segmentation of white matter structures using a Reproducible Objective Quantification Scheme (RPQS) Neuroimage. 2007;35:166–174. [PMC free article] [PubMed]
34. Chung T, Geier C, Luna B, Pajtek S, Terwilliger R, Thatcher D, Clark D. Enhancing response inhibition by incentive: comparison of adolescents with and without substance use disorder. Drug Alcohol Depend. Forthcoming. [PMC free article] [PubMed]
35. Hollingshead AB, Redlich FC. Social class and mental illness: a community study. Wiley; New York: 1958. [PMC free article] [PubMed]
36. Isaacs KL, Barr WB, Nelson PK, Devinsky O. Degree of handedness and cerebral dominance. Neurol. 2006;66(12):1855–58. [PubMed]
37. Knect S, Drager B, Deppe M, Bobe L, Lohmann H, Floel A, et al. Handedness and hemispheric language dominance in healthy humans. Brain. 2000;123(12):2512–18. [PubMed]
38. Oldfield RC. The assessment and analysis of handedness: the Edinburgh Inventory. Neuropsychologia. 1971;9:97–113. [PubMed]
39. Martin CS, Pollock NK, Bukstein OG, Lynch KG. Inter-rater reliability of the SCID alcohol and substance use disorders section among adolescents. Drug Alcohol Depend. 2000;59(2):173–76. [PubMed]
40. Sobell LC, Sobell MB. Assessment of drinking behavior. In: Allen JP, Columbus M, editors. Assessing alcohol problems. Humana Press; Bethesda, MD: 1995. pp. 55–74.
41. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, et al. 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. 1997;36(7):980–88. [PubMed]
42. Guy SC, Isquith PK, Gioia GA. Behavior Rating Inventory of Executive Function – Self Report version. Psychological Assessment Resources; Lutz, FL: 2004.
43. Walker JM, D'Amato RC. Test review: Behvior Rating Inventory of Executive Function – Self Report version. J Psychoeduc Assess. 2006;24:394–98.
44. [Accessed: 2011-02-28]; 2011-02-28. URL: http://surfer.nmr.mgh.harvard.edu/. (Archived by WebCite® at http://www.webcitation.org/5wqDkMnuT)
45. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179–94. [PubMed]
46. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80. [PubMed]
47. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207. [PubMed]
48. Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8:272–84. [PubMed]
49. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–55. [PubMed]
50. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11–22. [PubMed]
51. Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, et al. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage. 2006;30:436–43. [PubMed]
52. Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, et al. Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurol. 2002;58:695–701. [PubMed]
53. Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan R, Busa E, et al. Thinning of the cerebral cortex in aging. Cereb Cortex. 2004;14:721–30. [PubMed]
54. [Accessed: 2011-02-28]; 2011-02-28. URL: http://www.fmrib.ox.ac.uk/fsl/index.html. (Archived by WebCite® at http://www.webcitation.org/5wqDObIbQ)
55. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82. [PubMed]
56. James LR, Brett JM. Mediators, moderators and tests for mediation. J Appl Psychol. 1984;69:307–21.
57. MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593–614. [PMC free article] [PubMed]
58. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S. Fiber tract-based atlas of human white matter anatomy. Radiology. 2004;230(1):77–87. [PubMed]
59. Fjell AM, Westlye LT, Greve DN, Fischl B, Benner T, van der Kouwe AJ, et al. The relationship between diffusion tensor imaging and volumetry as measures of white matter properties. Neuroimage. 2008;42(4):1654–68. [PMC free article] [PubMed]
60. Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, et al. Cognitive functioning of long-term heavy cannabis users seeking treatment. JAMA. 2002;287:1123–31. [PubMed]
61. Pope HG, Gruber AJ, Hudson JI, Cohane G, Huestis MA, Yurgelun-Todd D. Early onset cannabis use and cognitive deficits: what is the nature of the association? Drug Alcohol Depend. 2003;69:303–10. [PubMed]
62. Harvey MA, Sellman JD, Porter RJ, Frampton CM. The relationship between non-acute adolescent cannabis use and cognition. Drug Alcohol Rev. 2007;26:309–19. [PubMed]
63. Jager G, Block RI, Luijten M, Ramsey NF. Cannabis use and memory brain function in adolescent boys: a cross-sectional multicenter functional magnetic resonance imaging study. J Am Acad Child Adolesc Psychiatry. 2010;49(6):561–72. [PMC free article] [PubMed]
64. Schweinsburg AD, Schweinsburg BC, Nagel BJ, Eyler LT, Tapert SF. Neural correlates of verbal learning in adolescent alcohol and marijuana users. Addiction. 2010;106:564–73. [PMC free article] [PubMed]
65. Squeglia LM, Schweinsburg AD, Pulido C, Tapert SF. Adolescent binge drinking linked to abnormal spatial working memory brain activation: differential gender effects. Alcohol Clin Exp Res. Forthcoming. [PMC free article] [PubMed]
66. Hanson KL, Winward JL, Schweinsburg AD, Medina KL, Brown SA, Tapert SF. Longitudinal study of cognition among adolescent marijuana users over three weeks of abstinence. Addict Behav. 2010;35:970–76. [PMC free article] [PubMed]
67. Bava S, Jacobus J, Mahmood O, Yang TT, Tapert SF. Neurocognitive correlates of white matter quality in adolescent substance users. Brain and Cogn. 2010;72:347–54. [PMC free article] [PubMed]
68. Thompson BL, Levitt P, Stanwood GS. Prenatal exposure to drugs: effects on brain development and implications for policy and education. Nat Rev Neurosci. 2009;10(4):303–12. [PMC free article] [PubMed]

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