The goal of this preliminary study was to identify regional GMV associated with future development of substance use problems. We assessed relationships between GMV measures on high‐resolution structural magnetic resonance imaging (sMRI) scans of adolescents with BD who had minimal to no prior alcohol or other substance exposure at baseline, but reported substance use at follow‐up on average 6 years later. Specific substances that were reported at follow‐up were tobacco, alcohol, and cannabis. We also conducted a secondary analysis exploring patterns of regional GMV involvement in females and males. We hypothesized an inverse association between baseline GMV in PFC, insular and temporopolar cortices, and amygdala and severity of future substance use problems, with females showing more associations with future substance use in ventral system components subserving emotional regulation (Blond et al., 2012 ) and males in rostral and dorsal system components subserving impulse regulation (Bari and Robbins, 2013 ).

Findings in groups at increased risk for BD and SUDs (e.g., with early life adversity and family history of SUDs [Edmiston et al., 2011 ; DeVito et al., 2013 ]) support sex distinctions in brain regions implicated in BD and in SUDs (e.g., greater abnormalities within ventral regions subserving emotional regulation in females vs. dorsal regions subserving impulse control in males). Sex differences in neurochemical abnormalities in the dorsal PFC in adults with BD and comorbid alcohol abuse/dependence have also been found. Specifically, one study suggested that glutamatergic abnormalities in the dorsal PFC are associated with comorbidity in males, but not females, while myo‐inositol abnormalities in dorsal PFC are associated with comorbidity in females, but not males (Nery et al., 2010 ). Although research suggests sex‐specific mechanisms in the development of SUDs (Holmila and Raitasalo, 2005 ; Ceylan‐Isik et al., 2010 ; Kuhn, 2015 ; Verplaetse et al., 2015 ), how sexual dimorphism may contribute to development of SUDs in BD is unknown.

It is well established that there are fundamental sex differences in brain—for example, extending from in utero fetal hormone programming (Goldstein et al., 2014 ) through subsequent brain structure, function, and chemistry (Cosgrove et al., 2007 ). Most brain‐based diseases have sex differences in either prevalence, susceptibility, age of onset, presentation, course, prognosis, medication response, treatment outcome, and/or mortality (Institute of Medicine Forum on Neuroscience and Nervous System Disorders, 2011 ). Sex differences also are reported in addiction processes (Becker and Hu, 2008 ; Fattore et al., 2008 ; Becker and Koob, 2016 ). For example, in females, greater internalizing symptoms (e.g., depression and anxiety) have been associated with tobacco, alcohol, and cannabis use (McChargue et al., 2004 ; Husky et al., 2008 ; Weinberger et al., 2009, 2013a ; Saraceno et al., 2012 ; Bekman et al., 2013 ; Moitra et al., 2016 ), while in males, associations with externalizing behaviors (e.g., aggressive actions) and substance use have been found (Steinhausen et al., 2007 ; Tarter et al., 2009 ; Heron et al., 2013 ). Sex differences have also been demonstrated in prevalence, risk, and clinical correlates of alcoholism in adults with BD (Frye et al., 2003 ), including greater number of depressive episodes in females. It is unclear what neuroanatomical factors are associated with this sexual dimorphism.

A pilot study of adolescents with BD, following subjects over 2 years, showed lower GMV in temporal cortex in those diagnosed with comorbid cannabis abuse/dependence before or after scan (n = 7), compared with adolescents with BD without comorbid diagnoses (n = 7) (Jarvis et al., 2008 ). In another study, adolescents/young adults (12–21 years) with BD and comorbid cannabis use disorders (n = 25; 7 of whom also had comorbid alcohol abuse/dependence) were observed to have decreases in functional responses of the amygdala, shown to be excessive in adolescents with BD without comorbidity (Bitter et al., 2014 ). However, tobacco use was not examined in either of these studies, and GMV was not assessed in the latter study. While the identification of altered GMV and functional responses in regions subserving emotional regulation in adolescents with BD and substance comorbidity may indicate regions involved in comorbidity development, the subjects were not studied exclusively before the development of comorbidity, so a direct effect of drug exposure cannot be ruled out.

Few neuroimaging studies have examined SUD comorbidity in BD. Adults with BD and comorbid alcohol abuse/dependence compared with adults with BD without SUDs were found to have lower gray matter volume (GMV) (Nery et al., 2011 ), functional abnormalities (Hassel et al., 2009 ), and glutamatergic system deficits in dorsal PFC (Nery et al., 2010 ). However, tobacco use by these subjects was not reported and could have affected findings (Epstein, 1990 ) as an association of reduced PFC and insula cortical thickness and tobacco use in adults with BD has been reported (Jorgensen et al., 2015 ). In young adults (18–30 years) with BD, oxidative stress in frontotemporal cortices is exacerbated by risky alcohol consumption and tobacco use (Chitty et al., 2013, 2014 ), but it is unclear how preexisting differences in frontotemporal cortices may contribute to this effect. To our knowledge, no studies have examined alcohol abuse/dependence or tobacco use during adolescence in BD with a focus on neuroanatomical factors involved in comorbidity development.

Neuroimaging studies suggest that abnormalities in prefrontal cortex (PFC) neural systems are central in BD. These systems subserve emotion and impulse regulation and include ventral, rostral, and dorsal PFC regions, as well as PFC projection sites, including insular and temporopolar cortices and amygdala (Blond et al., 2012 ; Strakowski et al., 2012 ). Evidence also suggests abnormalities in PFC system developmental trajectories in adolescents/young adults with BD (Blumberg et al., 2004 ; Gogtay et al., 2007 ; Kalmar et al., 2009 ; Blond et al., 2012 ; Najt et al., 2015 ). The neural systems believed to be involved in BD have substantial overlap with neural systems implicated in SUDs (Adinoff, 2004 ; Sullivan and Pfefferbaum, 2005 ; Koob and Volkow, 2010 ; Goldstein and Volkow, 2011 ; Goodkind et al., 2015 ), suggesting that vulnerability for comorbidity may be related to anatomically overlapping brain regions.

Comorbid SUD in BD is associated with more severe illness outcomes, including increased impulsivity (Swann et al., 2004, 2008 ; Heffner et al., 2012 ), more severe mood episodes (Strakowski and DelBello, 2000 ; Nolen et al., 2004 ; Strakowski et al., 2007 ), cognitive deficits (Levy et al., 2008 ; Marshall et al., 2012 ), and an increase in the already high risk of suicide attempts (Dalton et al., 2003 ; Swann et al., 2005 ). Despite the importance of understanding this comorbidity, there has been a paucity of study on the neural mechanisms underlying its development.

Mood disorders and substance use disorders (SUDs) have most often been studied as distinct conditions, yet evidence suggests that most individuals with a mood disorder or SUD develop comorbidity (Kessler et al., 1994 ). Bipolar disorder (BD) is associated with an especially high rate of comorbid SUDs (DelBello et al., 1999 ; Goldberg, 2001 ; Krishnan, 2005 ; Heffner et al., 2011 ). An estimated 60% of individuals with BD present with lifetime prevalence of substance abuse (Cassidy et al., 2001 ). In adolescents/young adults with BD, especially high rates of alcohol, cannabis, and tobacco use are reported (Leweke and Koethe, 2008 ), with at least 40%, 39%, and 12% of adolescents/young adults with BD reporting at least weekly use of nicotine, alcohol, or cannabis, respectively (Hermens et al., 2013 ).

We performed t tests to explore associations between mean GMV in significant clusters with clinical and medication subclass factors present and absent at baseline in N ≥ 5 subjects, including mood state (euthymic vs. elevated); history (yes/no) of hospitalizations; rapid cycling; lifetime psychosis; and if taking (off/on) an antipsychotic, anticonvulsant, stimulant, lithium, antidepressant, or benzodiazepine. Analyses were repeated stratified by sex for clinical factors present or absent at baseline in N ≥ 5 female or male subjects, including history (yes/no) at baseline of rapid cycling, lifetime psychosis, and if taking (off/on) an antipsychotic at baseline. In females, we assessed effects of taking an anticonvulsant and differences between subjects transitioning to smoking tobacco between baseline and follow‐up assessments (N = 7 females) compared with those reporting no history of smoking at either assessment (N = 6 females). In males, we assessed effects of comorbid ADHD and taking a stimulant or lithium at baseline assessment, but did not assess for smoking conversion because only 4 males converted. These post hoc analyses were considered significant at P < 0.05. All significant results are reported below.

A t test was used to explore differences in extracted GMV, from significant regions identified in the CRAFFT group analysis above, between individuals who never smoked tobacco at either assessment (N = 16) and those who transitioned to smoking tobacco between baseline and follow‐up assessment (N = 11). Regional GMV identified as being associated with transitioning to smoking tobacco was explored across all subjects who transitioned and were currently smoking tobacco at follow‐up using Pearson correlations to determine relationships with total FTND scores.

A two‐sample t test was conducted in SPM12 to assess CRAFFT HIGH vs. CRAFFT LOW group differences in baseline GMV, including data from all subjects. For hypothesized regions, PFC, insular and temporopolar cortices, and amygdala, results were considered significant at P ≤ 0.005 (uncorrected) and clusters ≥ 20 voxels. This threshold was chosen to balance for type I and type II errors in preliminary studies (Forman et al., 1995 ; Lieberman and Cunningham, 2009 ). For remaining brain regions, findings were considered as significant with P < 0.05 family‐wise error corrected and a threshold of 10 voxels for multiple comparisons. Mean GMV, extracted from clusters showing significant differences between CRAFFT groups, were calculated. Post hoc analysis was performed to assess effect of CRAFFT group when covarying age or IQ at baseline, with GMV from extracted clusters as the dependent variables. To further confirm regional GMV association with CRAFFT outcome, the relationship between mean GMV in significant clusters with total CRAFFT score was assessed with Pearson correlations across all subjects. Correlations were repeated stratified by sex to explore sex‐related patterns in volumetric features associated with the development of substance use problems. Correlation analyses were also performed across all subjects, and stratified by sex, to assess relationship between GMV and baseline CDRS or BIS (total and subscale) scores. Based on our a priori hypotheses, GMV from clusters within rostral PFC (rPFC) and dorsolateral PFC (dlPFC) showing a significant difference between CRAFFT groups was assessed for relationship with BIS (total and subscales) scores; GMV from clusters within orbitofrontal cortex (OFC) and insular and temporopolar cortices was assessed for relationship with baseline CDRS scores. Post hoc analyses above were repeated after removing three subjects with a history of tobacco use at baseline.

Subjects were categorized into those scoring at high ( ≥ 2: CRAFFT HIGH ) vs. low ( < 2: CRAFFT LOW ) risk for alcohol and substance use problems. Independent t tests were performed to assess group differences in age at baseline scans, age at follow‐up, interval between baseline scan and follow‐up, baseline IQ, years of education, Young Mania Rating Scale, CDRS, and BIS (total and subscale) scores. Chi‐square (or Fisher exact) tests were used to examine whether clinical factors differed by CRAFFT group at baseline (see Tables 2 and 3 ). These included mood state (euthymic, depressed, elevated); history (yes/no) of hospitalization; rapid cycling; psychosis; suicide attempt; smoking tobacco; comorbid diagnosis (yes/no) of simple/specific phobia, attention‐deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, or separation anxiety; and medication subclasses (on/off). Analyses were repeated stratified by sex. Additionally, a chi‐square test was used to assess if the number of individuals transitioning to smoking tobacco between baseline and follow‐up assessment differed by CRAFFT group (excluding three individuals with baseline history of tobacco use). A Fisher exact test was used to assess whether CRAFFT HIGH males and females differed in the number of individuals who transitioned to smoking tobacco between baseline and follow‐up assessment. Results were considered significant at P < 0.05.

Participants were assessed with the Young Mania Rating Scale (Young et al., 1978 ; Gracious et al., 2002 ) and the two‐subtest version (Matrix Reasoning and Vocabulary) of the Wechsler Abbreviated Scales of Intelligence (Psychological Corporation, 1999 ). Additionally, at baseline assessment, 27 participants (14 female [51%]) completed the Child Depression Rating Scale (CDRS) (Emslie et al., 1990 ), and 25 participants (12 female [48%]) completed the Barratt Impulsiveness Scale (BIS)‐11 or BIS‐11a. The BIS is a self‐reported measure of trait impulsivity. The total BIS score is the sum of three subscale scores: nonplanning impulsivity, cognitive–attentional impulsivity, and motor impulsivity. BIS‐11a scores were prorated to BIS‐11 scores as previously described (Patton et al., 1995 ; Gilbert et al., 2011 ).

Subjects were administered the CRAFFT interview (Knight et al., 1999 ), which consists of six yes/no questions inquiring about risk indicators or problems experienced from alcohol or drug use. CRAFFT is an acronym with each letter representing one of six items. C relates to history of driving or riding in a Car driven by someone who had been using alcohol/drugs, R if used alcohol/drugs to Relax, A if used alcohol/drugs while Alone, F if Forgotten things one did while using alcohol/drugs, F whether told by Family/Friends to cut down on alcohol/drug use, and T whether gotten into Trouble while using alcohol/drugs. The questions are equally weighted (one point for each yes answer). The CRAFFT has substantial empirical support as a substance use screening instrument for adolescents in multiple settings, including outpatient general medical and inpatient psychiatric settings (Knight et al., 1999, 2002, 2003 ; Cummins et al., 2003 ; Dhalla et al., 2011 ; Pilowsky and Wu, 2013 ; Oesterle et al., 2015 ). A score of ≥ 2 has been used as the threshold optimal for identifying alcohol/substance problems (Knight et al., 1999, 2002 ). At follow‐up, 19 (63%) participants had a CRAFFT score of ≥ 2 (CRAFFT mean ± SD = 2.3 ± 2.1; scores ranged from 0 to 6 and showed a normal distribution in both males and females).

High‐resolution sMRI data were acquired for each subject with a 3‐Tesla Siemens Trio MR scanner (Siemens, Erlangen, Germany). The sMRI sagittal images were acquired with a three‐dimensional magnetization prepared rapid acquisition gradient echo T 1 ‐weighted sequence with parameters: repetition time = 1500 msec, echo time = 2.83 msec, matrix 256 × 256, field of view = 256 mm × 256 mm 2 , and 160 one millimeter slices without gap and two averages. Images were processed with the DARTEL toolbox within Statistical and Parametric Mapping (SPM) 12 ( http://www.fil.ion.ucl.ac.uk/spm ). The SPM segmentation function and SPM tissue probability maps for gray matter, white matter, and cerebral spinal fluid were implemented for bias correction and segmentation and used to create DARTEL templates using the “Run Dartel (create Templates)” command under DARTEL tools (Henley et al., 2014 ; Malone et al., 2015 ). Data were normalized to Montreal Neurological Institute (MNI) space and smoothed with an 8 mm full‐width‐at‐half‐maximum isotropic kernel.

On the neuroimaging day, urine toxicology screens for substances of abuse (cannabis, cocaine, amphetamine, methamphetamine, methadone, opiates, phencyclidine, barbiturates, and benzodiazepines) were negative for all subjects. Exclusion criteria included history of neurological illness, including head trauma with loss of consciousness for ≥ 5 min, or major medical illness. Subjects were not excluded for comorbidities or family history of psychiatric disorders as there are high rates of these in individuals with BD, and excluding decreases generalizability. After complete description of the study, written informed consent was obtained from subjects ≥ 18 years, and assent and parent/guardian permission from subjects < 18, in accordance with the Yale School of Medicine human investigation committee.

As the aim of this study was to examine baseline GMV as a predictor of future alcohol and substance use problems, subjects were excluded if at baseline they self‐reported more than minimal alcohol and/or cannabis exposure or ever having used cocaine, opioids, phencyclidine, hallucinogens, or solvents/inhalants. Sixty‐three percent of subjects (11 males, 8 females) reported never trying alcohol or any other illicit substances or having tried a sip of alcohol once at a family gathering. Remaining subjects reported having tried alcohol or cannabis once or on a few occasions with peers. Subjects were not excluded for tobacco use. At baseline and follow‐up assessment, tobacco use was assessed as smoking using the Fagerstrom Test for Nicotine Dependence (FTND) (Heatherton et al., 1991 ). At baseline, 2 subjects (7%) reported current smoking and 1 subject (3%) reported past history of, but not current, smoking. Transition to smoking was studied in the remaining subjects. In addition to the 3 subjects with a history of smoking tobacco at baseline, 11 individuals (41%; 7 females) with no tobacco use at baseline reported history of smoking at follow‐up (8 were currently smoking at follow‐up assessment; 4 female current smokers at follow‐up). Sixteen individuals (53%; 6 females) reported no history of smoking tobacco at either time point.

Subjects who were assessed and scanned in a cross‐sectional study and consented to being recontacted were recontacted and recruited if they met inclusion and did not meet exclusion criteria. Participants included 30 adolescents/young adults diagnosed with BD (mean age at baseline ± standard deviation [SD] = 16 ± 2 years; 29 [97%] bipolar I, 1 [3%] bipolar II; 50% female; mean age at follow‐up = 22 ± 3 years) (see Table 1 for participant characterization). The presence/absence of psychiatric diagnoses and mood state at time of neuroimaging were confirmed with the Structured Clinical Interview for DSM‐IV Diagnosis (First et al., 1995 ) for participants ≥ 18 years and the Kiddie‐Schedule for Affective Disorders and Schizophrenia (Kaufman et al., 1997 ) for participants < 18 years. At baseline assessment, all participants completed sMRI. At follow‐up assessment, on average 6 ± 2 years after baseline assessment, subjects completed the CRAFFT interview to assess alcohol and substance use problems since baseline assessment.

Lower GMV within the OFC ( t 25 = 2.98, P = 0.006) and insular ( t 25 = 2.46, P = 0.021) and temporopolar ( t 25 = 2.54, P = 0.018) cortices was observed in individuals who transitioned to smoking tobacco between baseline and follow‐up assessment, compared with individuals who reported no history of smoking tobacco at either assessment. Temporopolar GMV was negatively associated with follow‐up FTND scores ( r = −0.76, n = 8, P = 0.03) in individuals who transitioned to smoking tobacco after their baseline assessment and were currently smoking at follow‐up. No other significant effects of clinical factors were observed on GMV when looking across all subjects or when investigating within females and males separately.

Significance remained in hypothesized regions when covarying age or when covarying IQ. Across all subjects, total CRAFFT scores were negatively correlated with extracted mean GMV of clusters within the OFC ( r = −0.44, n = 30, P = 0.016), rPFC ( r = −0.51, n = 30, P = 0.004), dlPFC ( r = −0.63, n = 30, P = 0.0002), and insula ( r = −0.45, n = 30, P = 0.013). Insula GMV was negatively correlated with baseline CDRS scores ( r = −0.46, n = 27, P = 0.015). Both females and males showed a negative correlation between total CRAFFT scores and dlPFC GMV (females: r = −0.69, n = 15, P = 0.004; males: r = −0.67, n = 15, P = 0.008). Additionally, females, but not males, showed a negative correlation between total CRAFFT scores and OFC ( r = −0.74, n = 15, P = 0.002) and insula ( r = −0.69, n = 15, P = 0.004) GMV. Males, but not females, showed a negative correlation between total CRAFFT scores and rPFC GMV ( r = −0.62, n = 15, P = 0.01). Within females, a trend for a negative correlation between insula GMV and baseline CDRS scores ( r = −0.50, n = 14, P = 0.07) was observed. When excluding the three subjects with baseline history of tobacco use, these results were still observed (not shown).

Gray matter volume decreases in bipolar disorder with prospective substance use problems. The images show the regions of gray matter volume decrease in the bipolar disorder (BD) group with prospective substance use problems (CRAFFT HIGH ) compared with the group with BD without prospective substance use problems (CRAFFT LOW ). No regions of gray matter volume increases were observed in the BD CRAFFT HIGH group compared with the BD CRAFFT LOW group. Significance threshold is P < 0.005; cluster ≥ 20 voxels. L on left of figure denotes left side of brain. The color bar represents the range of T values. BD CRAFFT HIGH N = 19, BD CRAFFT LOW N = 11.

Across all subjects, the CRAFFT HIGH group had higher CDRS scores ( t 25 = 2.11, P = 0.045) at baseline assessment. Within females, the CRAFFT HIGH group had a trend towards higher CDRS scores ( t 13 = 2.14, P = 0.054). No significant differences in BIS (total or subscales) scores were observed (see Table 3 for summary of CDRS and BIS scores). More CRAFFT HIGH males, compared with CRAFFT LOW males, had comorbid diagnoses of ADHD (78% vs. 17%, respectively, P = 0.04, Fisher exact test) and were taking a stimulant at baseline (78% vs. 17%, respectively, P = 0.04, Fisher exact test). Overall, the CRAFFT HIGH group had more individuals transition to smoking tobacco at follow‐up assessment (56% vs. 18%, respectively; χ 2 = 3.9, df = 1, P = 0.048). There was no difference between CRAFFT HIGH females and males in number of individuals who transitioned to smoking tobacco between baseline and follow‐up assessment (63% CRAFFT HIGH females vs. 50% CRAFFT HIGH males, respectively; χ 2 = 0.25, df = 1, P = 0.61). There were no other differences in demographic/clinical factors between CRAFFT groups overall or within females or males (see Table 2 ).

DISCUSSION

Lower baseline GMV in the PFC, including dlPFC, OFC, and rPFC, and insular and temporopolar cortices, was observed among adolescents with BD who subsequently reported substance use problems with alcohol and cannabis on the CRAFFT interview compared with adolescents with BD who did not. Exploratory analyses supported both common and different patterns of regional GMV associated with substance use development among females and males. Decreased baseline dlPFC GMV was associated with substance use problems in both females and males. Lower baseline OFC and insula GMV was associated with substance use problems in females; lower baseline rPFC GMV was associated with substance use problems in males. Greater depressive symptoms at baseline were associated with greater substance use problems at follow‐up, with depressive symptoms related to lower insula GMV, with these associations driven by the female data. Additionally, lower OFC, insular, and temporopolar GMV was observed in individuals who transitioned to smoking tobacco, with temporopolar GMV inversely associated with severity of nicotine dependence at follow‐up.

Regions in which GMV abnormalities were associated with development of substance use problems in BD are consistent with previous findings in adolescents in the absence of BD with alcohol and substance abuse/dependence (Bava and Tapert, 2010) and drug‐related processes, including craving, motivational changes, withdrawal symptoms, and relapse/treatment outcomes (Adinoff, 2004; Sinha and Li, 2007; Koob and Volkow, 2010; Goldstein and Volkow, 2011; Naqvi et al., 2014). Abnormalities in behaviors subserved by these regions may contribute to vulnerability/risk for substance use problems (Baker et al., 2004; Adinoff et al., 2007; Li and Sinha, 2008; Claus and Hutchison, 2012; Peeters et al., 2014). Dorsal system components (i.e., the dlPFC and rPFC) are associated with higher‐order executive functions and behavioral control (Levy and Goldman‐Rakic, 2000; Aron et al., 2004; Ramnani and Owen, 2004; Gilbert et al., 2006; Burgess et al., 2007; Koechlin and Hyafil 2007; Petrides and Pandya, 2007; Dumontheil et al., 2008). Ventral and paralimbic cortical regions implicated (i.e., OFC, insular and temporopolar cortices) are associated with affective processing and regulation, self‐awareness, stimulus‐reinforcement associations, behavioral control, and risky decision making (Craig, 2002; Manes et al., 2002; Kringelbach and Rolls, 2004; Fellows, 2007; Clark et al., 2008; Modinos et al., 2009; Rolls, 2004; Van Leijenhorst et al., 2010; Xue et al., 2010; Etkin et al., 2011; Crowley et al., 2015; Muhlert and Lawrence, 2015).

Circuitry Associated With Sex‐Related Risk for Substance Problems Exploratory findings in regions subserving affective and internal monitoring processes in females are consistent with literature supporting associations between disturbances in affective processing and internalizing symptoms and substance use problems. This has been found in the absence of BD (Boden and Fergusson, 2011; Hussong et al., 2011; Garfield et al., 2015) and suggested to be especially salient in the development of substance use problems in females (Saraceno et al., 2012; Bekman et al., 2013; Moitra et al., 2016). The insula has been associated with addictive behavior, postulated to be through involvement in interoceptive aspects of drug craving and seeking (Naqvi et al., 2007, 2014; Gaznick et al., 2014). The right posterior insula is suggested to be involved in self‐awareness and emergence of a sense of self (Craig, 2002; Farrer et al., 2003; Avery et al., 2014). Decreased awareness/insight is implicated in addicted individuals failing to seek treatment (Goldstein et al., 2009). Differences in insula development and related differences in development of awareness/insight during adolescence may contribute to an inability to recognize and relate negative consequences to early drug use, increasing the risk of problematic use. Depression, a risk factor for substance use especially in females (Saraceno et al., 2012), is associated with differences in awareness/insight (Schwartz‐Stav et al., 2006; Wiebking et al., 2014; Wiebking and Northoff, 2015). An inverse association between insula GMV and number of depressive episodes in BD has been reported (Takahashi et al., 2010). We report associations between greater depression symptomatology, lower insula GMV, and greater substance use problems at follow‐up, largely due to data from females. More research is needed in adolescents on the relationships between the insula, depression, constructs of awareness/insight, and the development of substance use–related problems, especially in females. Literature also supports associations between externalizing symptoms and substance use problems. This has been found in the absence of BD (Rogosch et al., 2010; Griffith‐Lendering et al., 2011; Oshri et al., 2011; Miettunen et al., 2014) and is suggested to be especially salient in the development of substance use problems in males (Steinhausen et al., 2007; Tarter et al., 2009; Heron et al., 2013). Impulsiveness has been suggested as a trait feature of BD that might increase vulnerability for substance use problems (Swann et al., 2004; Gilbert et al., 2011; Nery et al., 2013). We did not detect associations between BIS impulsivity scores and subsequent substance use problems. It is possible that this is due to limited power to detect associations, particularly in the small male subsample, or that differences in impulsiveness may not have emerged yet in this young sample, and future studies examining behavioral trajectories associated with transitioning to SUDs may reveal differences. It is also possible that the BIS does not capture relevant impulsivity constructs. This is supported by work showing that risk‐taking and novelty seeking may distinguish adults with BD and comorbid SUDs better than BIS impulsiveness scores (Haro et al., 2007; Kathleen Holmes et al., 2009; Bauer et al., 2015). More males with BD and prospective substance use problems were diagnosed with comorbid ADHD at baseline. Recent work suggests that ADHD may increase risk of substance use problems in the absence of BD (Urcelay and Dalley, 2012; Bidwell et al., 2014; Kolla et al., 2016) and when comorbid with BD (Perroud et al., 2014). The neuroanatomical factors underlying this association are unclear. Adults with BD and comorbid ADHD, compared with those without, showed greater rPFC dysfunction (Adler et al., 2005). While an association between comorbid ADHD and GMV findings was not detected in this study, possibly owing to sample size, males but not females did show an inverse association between rPFC GMV and CRAFFT scores. More work is needed to identify whether behavioral constructs associated with rPFC, such as executive functions, decision making, and response processes (Burgess et al., 2007; Koechlin and Hyafil 2007; Dumontheil et al., 2008), may be particularly salient in development of substance use problems in males with BD. Regions of GMV differences reported here are associated with hot and cold cognition (Prencipe et al., 2011; Zelazo and Carlson, 2012). Adults with BD perform worse on hot and cold cognitive tasks compared with healthy controls (Roiser et al., 2009). Studies suggest that females may initiate substance use later than males (Brady and Randall, 1999), and it is suggested that hot cognition develops more slowly than cold cognition (Prencipe et al., 2011; Zelazo and Carlson, 2012). Given that we report that regions associated with hot cognition are predictive in females, while regions associated with cold cognition are predictive in males, differences in speed of hot and cold cognitive development may be associated with sex differences in age of risk for substance use initiation. More work is needed to understand sex differences in the development of these processes during adolescence and whether these are disrupted in BD. Lower baseline OFC, insular, and temporopolar GMV in adolescents with BD was also associated with transitioning to smoking tobacco. As above, these regions are involved in affective processes, including depression symptomatology (Hulvershorn et al., 2011; Wang et al., 2011; Arnsten and Rubia, 2012; Pfeifer and Peake, 2012). Findings in these regions are consistent with literature supporting associations between depression and tobacco use in the absence of BD (Kassel et al., 2003; Graham et al., 2007; Weinberger et al., 2012, 2013a, 2013b; Cheetham et al., 2015). It has been suggested that this relationship is especially salient for tobacco use in females (McChargue et al., 2004; Husky et al., 2008; Weinberger et al., 2009). We did not observe sex‐related associations between GMV in these regions and transitioning to smoking tobacco; however, ability to detect associations was limited by sample size. While we did not detect an association between temporopolar GMV and follow‐up CRAFFT scores, temporopolar GMV was inversely related to follow‐up Fagerstrom scores for nicotine dependence severity, suggesting its involvement in development of smoking. Future work is warranted to investigate neuroanatomical specificity for vulnerability/risk for certain drug types in BD.

Neuroanatomical Factors Associated with Substance Use Problems in Other Populations Studies of adolescents recruited from school systems, the majority of whom had no history of psychopathology at baseline, report lower dorsal PFC and OFC GMV and less OFC gyrification associated with subsequent alcohol‐related problems and initiation of cannabis use (Cheetham et al., 2012, 2014; Churchwell et al., 2012; Kuhn et al., 2015). Functional MRI studies of adolescents having no history of psychopathology but genetic risk for substance use problems have shown associations between regional responses during response inhibition or working memory tasks in dorsal PFC, including dlPFC and rPFC, with subsequent substance use problems (Norman et al., 2011; Squeglia et al., 2012; Wetherill et al., 2013; Heitzeg et al., 2014). We are not aware of a study examining adolescents with minimal to no alcohol or substance use at baseline demonstrating associations between baseline insular and temporopolar GMV with prospective substance use problems. As above, a growing body of work suggests insula involvement in drug‐craving and ‐seeking behavior (Naqvi et al., 2014). One report did show that young adults with moderate alcohol use who then transitioned to heavy alcohol use had greater alcohol cue reactivity in the insula (Dager et al., 2014). However, as that study consisted of individuals with moderate alcohol use at baseline, brain effects of alcohol exposure cannot be ruled out. We did not observe an association between amygdala GMV and subsequent substance use problems in BD. In previous studies examining adolescents who predominantly had no history of psychopathology, associations between amygdala volumes with prospective alcohol/cannabis use were also not detected (Cheetham et al., 2012, 2014). This suggests that previous observations of amygdala abnormalities as observed in adolescents/young adults without BD but with cannabis (Gilman et al., 2014; Padula et al., 2015) or alcohol use (Dager et al., 2013) as well as in adolescents with BD and comorbid cannabis use (Bitter et al., 2014) may be related to substance exposure. Additionally, altered amygdala morphology and function in association with substance use has shown genetic and sex‐related associations (Hill et al., 2001, 2013; Benegal et al., 2007; McQueeny et al., 2011; Cacciaglia et al., 2013). It is therefore possible that genetic heterogeneity and limited power to detect sex‐related associations may have confounded ability to detect findings. To our knowledge, there have not been other reports demonstrating neuroanatomical predictors of subsequent transitions to smoking tobacco. Adolescents/young adult tobacco smokers without BD, compared with nonsmokers, show cortical thinning in the OFC (Li et al., 2015) and a negative association between OFC and insula cortical thickness and magnitude of lifetime exposure to tobacco smoke (Morales et al., 2014; Li et al., 2015). Further study on the involvement of the OFC, insular, and temporopolar cortices in elevating risk for smoking tobacco is warranted.