Introduction

Diffusion tensor imaging (DTI) measures reflect molecular water diffusion in brain tissue, specifically alterations in density, coherence, compactness, and fiber diameter (Le Bihan et al., 2001; Suzuki, Matsuzawa, Kwee, & Nakada, 2003; Taylor, Hsu, Krishnan, & MacFall, 2004). Early studies of white matter development through adolescence have shown increased myelination and organization of fiber tracts into coherent bundles (Benes, Turtle, Khan, & Farol, 1994; Brody, Kinney, Kloman, & Gilles, 1987; Yakovlev & Lecuors 1967). More recent studies have focused on DTI parameters, particularly fractional anisotropy (FA), a measure of the directionality of water movement within axons (Barnea-Goraly et al., 2005; Fryer et al., 2008; Klingberg, Vaidya, Gabrieli, Moseley, & Hedehus, 1999; Muetzel et al., 2008; Schmithorst, Wilke, Dardzinski, & Holland, 2002; Snook, Paulson, Roy, Phillips, & Beaulieu, 2005). White matter maturation appears to improve conductivity between brain regions, so subtle alterations in fibers may affect neurocognitive capabilities and complex behavior (Cascio, Gerig & Piven, 2007). A recent study from our lab (Bava et al., 2010) yielded relatively large effect sizes for increased FA over 1.5 years during late adolescent development in long-association fiber bundles and projection fibers, including the corona radiata, superior longitudinal fasciculus, and fronto-occipital fasciculus, tracts that connect frontal and frontal-posterior cortices, as well as subcortical projections such as cortico-thalamic and cortical-limbic fiber tracts.

Adolescents tend to show gradual improvements in executive functioning and subtle increases in cognitive control capacity at the same time as macrostructural and microstructural tissue changes occur in prefrontal cortical and subcortical areas (Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Conklin, Luciana, Hooper, & Yarger, 2007; Levin et al., 1991; Luna, Padmanabhan, & O’Hearn, 2010; Rubia et al., 2006; Tamm, Menon, & Reiss, 2002; Van Leifenhorst et al., 2010). Research has emphasized the importance of efficient projections from subcortical regions to frontal cortices as contributors to executive functioning capabilities (Anderson et al., 2001; Cascio et al., 2007; Conklin et al., 2007; Levin et al., 1991; Liston et al., 2006; Luna et al., 2010; Qiu, Tan, Zhou, & Khong, 2008; Royall et al., 2002; Rubia et al., 2006; Tamm et al., 2002; Van Leifenhorst et al., 2010). Continuous development of projections among prefrontal, striatal, and limbic regions during adolescence likely reflects efficiency of neuronal communication and brain circuitry, coinciding with top-down control of problem solving strategies, response inhibition, mental flexibility, set-shifting, and tests of sustained attention (Barnea-Goraly et al., 2005; Ben Bashat et al., 2005; Eluvathingal, Hasan, Kramer, Fletcher & Ewing-Cobbs, 2007; Giorgio et al., 2010; Lebel, Walker, Leemans, Phillips, & Beaulieu, 2008; Liston et al., 2006; Paus et al., 2001; Schmithorst et al., 2002).

Risk taking (both substance use and non-substance use behaviors) is a complex and dynamic construct, defined as engaging in behaviors with a significant probability of negative consequences or unintentional injury (Boyer, 2006; Centers for Disease Control and Prevention, 2010). It is influenced by individual state and trait factors such as mood, cognitive status, personality, sex, and culture (Boyer, 2006; Casey, Getz, & Galvan, 2008; Galvan, Hare, Voss, Glover, & Casey, 2007; Steinberg, 2004; Spear, 2010) From a neurobiological perspective of adolescent risk taking, the progressive development of two neural systems during adolescence, prefrontal (control) and limbic (reward), are postulated to leave all adolescents distinctly vulnerable to engaging in risk taking behaviors (Casey et al., 2008; Ernst et al., 2005; Galvan et al., 2006, 2007; Steinberg, 2004, 2008). Increases in subcortical activation as well as more diffuse prefrontal recruitment have been shown when adolescents make risky choices (Brown et al., 2005; Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Casey et al., 1997, 2002; Crone, Donohue, Honomichl, Wendelken,, & Bunge, 2006; Galvan et al., 2006;; Moses et al., 2002; Tamm et al., 2002). The prefrontal cortex may not yet provide sufficient top down control, and delayed (or altered) white matter maturation in association fiber tracts (e.g., superior longitudinal fasciculus) and cortico-limbic tracts (e.g., anterior/superior corona radiata), and therefore cross-talk, between prefrontal and limbic areas may lead to adult-like, yet impulsive behavioral selections (Casey, Getz, & Galvan, 2008; Steinberg, 2008; Van Leifenhorst et al., 2010). To some extent, adolescent drug taking may be a consequence of poor functional connectivity, while at the same time substance use during adolescence may alter such neurodevelopmental processes. Therefore, neuroimaging biomarkers may help us identify individuals likely to engage in not only substance use behaviors, but risky behaviors in general during late adolescence and early adulthood (Squeglia, Spadoni, Infante, Myers, & Tapert, 2009; Tapert et al., 2004, 2007).

Risk taking, including adolescent substance use, follows a nonlinear trajectory, as adolescents engage in more risky behaviors than younger children or adults. Externalizing behaviors are often assessed throughout childhood and adolescence as a way to identify teens engaging in excessive rule breaking. While internalizing behaviors have been described as internal emotional states that are “overcontrolled,” externalizing is suggested as “undercontrol,” (Achenbach & Rescorla, 2001; Achenbach & Edelbrock, 1978), and assessing this construct has shown to have utility in predicting future engagement in risk taking behaviors (Thompson et al., 2011). In a comprehensive review by Boyer (2006), risk taking is discussed as taking on many labels throughout the literature (e.g., externalizing, norm-breaking, problem behaviors), however Boyer describes that generally these labels are attempting to look at similar behavior that may cause inherent harm or danger to the adolescent (2006). It is likely that personality (e.g., sensation seeking), biology, and culture all contribute to acting in these riskier ways (e.g., substance misuse), regardless of construct label. In fact, in the United States nearly 50% of adolescents (high school age students) have had a drink in the past month and 20% have used marijuana in the past month (Centers for Disease Control and Prevention, 2010). Among 8^th, 10^th, and 12^th grade adolescents, alcohol continues to be the most widely used intoxicant, and marijuana use during this time period is on the rise after several years of decline, including an increase in daily marijuana use (Johnston, O’Malley, Bachman, & Schulenberg, 2011; Substance Abuse and Mental Health Services Administration, 2010). While the tendency to engage in some degree of risk taking and novelty seeking can increase opportunities for success and social status (Spear, 2010), risky behaviors can have acute and chronic consequences on health and quality of life. Few studies have explored the relationship between brain microstructure and risk taking; one recent cross-sectional study suggests a link between more coherent white matter in superior/anterior corona radiata fiber tracts and increased engagement in substance use-related risk taking behaviors (Berns, Moore, & Capra, 2009); however this finding is in the opposite directions of neurodevelopmental models of adolescent risk taking and therefore warrants further investigation.

Early identification of adolescents at risk for problematic behaviors may help target prevention efforts, and the degree to which neuroimaging indices can help predict future risky behaviors has not been adequately explored. The present study examined the influence of white matter microstructural integrity in mid-adolescence on real-world risk taking behaviors measured 1.5 years later as youth transitioned into the time of lifetime peak incidence of substance use and dependence (Substance Abuse and Mental Health Services Administration, 2010). It was expected that poorer white matter fiber integrity in association fiber tracts with prefrontal connections and cortico-limbic fiber tracts, measured using DTI at a baseline time point, would predict greater risk taking at 18-month follow-up in both heavy substance using teens at baseline and adolescents with limited substance use histories at baseline. We hypothesized that the relationship between white matter integrity in anterior association and projection fiber tracts and future risk taking behaviors (particularly substance use related risk taking) would be more pronounced in the sub-sample of heavy substance using teens due to increased vulnerability for even poorer fronto-subcortical connectivity associated with not only typical adolescent developmental trajectories, but also substance related white matter alterations and neurotoxicity.

Methods and Materials

Participants

Ninety-six adolescents from an ongoing longitudinal research project examining the effects of marijuana use on adolescent neurodevelopment (Bava et al., 2010; Mahmood, Jacobus, Bava, Scarlett, & Tapert, 2010; Tapert et al., 2007; Schweinsburg, Schweinsburg, Nagel, Eyler, & Tapert, 2011) were included in the present study. This prospective investigation includes participant overlap from our previous publications, including non-substance using controls from Bava et al., 2010 (N = 22) and substance users and demographically matched controls (N = 72; Bava et al., 2009) from a cross-sectional study comparing teens that engaged in marijuana and alcohol use with those who had limited substance use histories. The current sample of 96 adolescents includes all subjects (both controls and substance users) in our baseline dataset meeting eligibility criteria with valid DTI data, and complete risk taking data at the 18-month follow-up. Informed consent and assent were obtained from all youths and parents at project baseline and each follow-up using procedures approved by the University of California, San Diego Human Research Protections Program.

Participants were recruited from local schools when they were 16–19 years of age, and classified as substance users (SU) if they had >200 lifetime experiences with cannabinoids and as normal control youth (CON) if they had <10 lifetime experiences with cannabinoids at project baseline. Subjects were selected from an ongoing longitudinal study examining the effects of cannabis use on adolescent development, however as teens reported both significant marijuana and alcohol use, they are referred to as substance users throughout this manuscript; the present study includes 47 SU and 49 CON teens. A detailed screening procedure at baseline eliminated participants with the following potential confounds: history of a lifetime DSM-IV Axis I disorder (other than cannabis or alcohol abuse or dependence), history of learning disability, history of neurological disorder or head trauma with loss of consciousness >2 minutes, history of a serious physical health problem, complicated or premature birth; uncorrectable sensory impairments; left handedness; MRI contraindications, and use of psychoactive medications at project intake. Approximately 85% of subjects who were screened and completed the baseline assessment as part of the longitudinal project returned to complete the follow-up assessment 1.5 years later; while it is possible that individuals who returned for follow-up differ from those who did not, no between-group demographic differences between these individuals were found at baseline (e.g., age, gender, ethnicity, emotional functioning; ps > .05) SU and CON groups were similar demographically except substance users were about 5 months older than controls on average, more likely to have one or more first-degree biological relatives with a history of substance use disorder, had poorer academic grades at baseline and follow-up, and had less self-reported state anxiety on the day of the scan (ps < 05). Substance use was substantially higher for substance users than controls at baseline and 18-month follow-up, as expected (see Table 1).

Measures

Substance Use

Drug use histories were assessed with the Customary Drinking and Drug Use Record (CDDR) (Brown et al., 1998). The CDDR, administered at baseline, obtained lifetime and past 3-month information on use of tobacco, alcohol, and other drugs, as well as withdrawal symptomatology and DSM-IV abuse/dependence criteria (DSM-IV), and was administered at baseline. In the sample of 47 substance users, 84% met criteria for a DSM-IV lifetime marijuana dependence and/or abuse diagnosis in their lifetime, 81% met criteria for a lifetime alcohol dependence and/or abuse diagnosis in their lifetime, and 10% met criteria for a lifetime tobacco use disorder at baseline. The follow-up version of the CDDR replaced lifetime items with a past 18-month follow-up interval. In the 47 substance users at follow-up, 70% met criteria for a marijuana use disorder (i.e., abuse or dependence), 45% met criteria for an alcohol use disorder, and 10% met criteria for nicotine dependence. The Timeline Followback (Sobell & Sobell, 1992) assessed self-reported substance use (excluding nicotine or caffeine) in the 28 days prior to initiating abstinence (described below), and in the 28 days prior to the 18-month follow-up. A substance use T-score was calculated for each individual at baseline and 18-month follow-up based on the means and standard deviations of the Timeline Followback for the sample (N=96).

Other Risk Taking Behaviors

The Child Behavior Checklist (CBCL) was given to all participants’ parents at baseline for reports on youth behavior (Achenbach & Rescorla, 2001). All participants completed the Youth Self Report (YSR; for ages ≤18) or Adult Self Report (ASR; for ages ≥19) at follow-up (Achenbach & Rescorla, 2001); this resulted in 21 individuals completing the YSR and 75 individuals completing the ASR. This well validated assessment system has shown excellent reliability and validity between assessment instruments. Cross-informant agreement (e.g., child/parent) on reports of externalizing behaviors show large effect sizes (Pearson rs= .48–.56) (Achenbach et al., 1987; Achenbach & Rescorla, 2001) and studies have shown consistency between youth and adult self report instruments over time (Hofstra et al., 2001).Individual items (largely from the Aggression and Delinquency/Rule Breaking scales), were selected a priori to best reflect real-world risk taking (Thompson et al., 2011), defined as engaging in behaviors with a significant probability of negative consequence or unintentional injury (Boyer, 2006; Centers for Disease Control and Prevention, 2010). Internal consistency, measured with standardized Cronbach’s α coefficients, was assessed for all items that comprised other risk taking, including items from the CBCL (parent report) used at baseline (α = .91), items from the YSR used at follow-up if the participant was 18 or younger (adolescent self-report), (α = .90), and items from the ASR used at follow-up if the participant was over 18 (adolescent self-report) (α = .80); all three coefficient levels were ≥.80 suggesting excellent internal consistency. Correlations between raw composite scores of aggression/delinquency at both timepoints (e.g., CBCL, ASR/YSR) demonstrated consistency over time (r = .35, p < .01). Scores on these items were used to compute T-scores based on sample means and standard deviations to index aggressive and delinquent related risk taking behavior at baseline and 18-month follow-up.

Additional Baseline Measures

The Wechsler Abbreviated Scale of Intelligence Vocabulary subtest was administered to briefly estimate intellectual functioning (Wechsler, 1999). The Beck Depression Inventory-II and Spielberger State-Trait Anxiety Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961; Spielberger, Gorsuch, & Lushene, 1970)assessed depression and anxiety state on the day of imaging. The Family History Assessment Module (FHAM) assessed family history of substance use disorders (SUD) and major mental health problems (Rice et al., 1995). The Pubertal Development Scale (PDS) assessed physical development stage (Peterson, Crockett, Richards, & Boxer, 1988). Data on psychosocial functioning (e.g., hours worked at a job) was collected during a general clinical interview for all participants.

Procedures

At baseline, eligible adolescents agreed to remain abstinent from substances for 28 days, monitored by semi-weekly urine drug toxicology screens and breathalyzers (9 times over 28 days). Participants whose drug screen results indicated compliance with the protocol (Smith, Barnes, & Huestis, 2009) received a brain scan including DTI on Day 28. Participants were imaged in a 3.0-Tesla General Electric CXK4 short bore Excite-2 magnetic resonance system with an 8-channel phase-array head coil. A high-resolution 3d T1 anatomical MRI was acquired sagittally using a weighted spoiled gradient recalled acquisition sequence (repetition time = 7.784 ms, echo time = 2.988 ms, flip angle = 12°, field of view = 24 cm, resolution = 1 mm³, 176 continuous slices, acquisition time = 7:19 minutes). The DTI sequence was optimized for minimum echo time and included a single-shot dual spin echo excitation (Reese, Heid, Weisskoff, & Wedeen, 2003) to reduce eddy current artifacts (repetition time = 12400 ms, echo time = 93.4 ms, 4 averages, 15 directions, b ≈ 2000s/mm², field of view = 24 cm, matrix 128 × 128, voxel resolution = 1.875 mm × 1.875 × 3 mm³, 36 slices, acquisition time = 13:39 minutes) (Frank, 2001). Two field map sequences were collected for unwarping (repetition time = 1000 ms, echo time = minimum full for the first and 5.5 ms for the second, same spatial dimensions as the DTI acquisition, acquisition time = 2:12 minutes for each).

Datasets were processed with Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Package (FSL) (Parker, 2004) and Analysis of Functional NeuroImages (AFNI) library (Cox, 1996). DTI acquisitions were unwarped with two field maps using FMRIB’s Phase Region Expanding Labeler for Unwrapping Discrete Estimates (PRELUDE) (Jenkinson, 2003) and FMRIB’s Utility for Geometrically Unwarping EPIs (FUGUE) (Jenkinson & Smith, 2001). A six-degree of freedom affine motion correction for head motion and a linear alignment to reduce the effects of gradient coil eddy currents were conducted using FMRIB’s Diffusion Toolbox (FDT) and Linear Image Registration Tool (FLIRT) (Smith et al., 2004; Jenkinson, Bannister, Brady, & Smith, 2002). Each image was visually inspected for quality, and non-brain voxels were removed from analyses by the AFNI program 3dAutomask. FA values were calculated using a log-linear estimation procedure that fits a diffusion tensor model at each voxel via FDT (Smith et al., 2004).

Given evidence for microstructural white matter tissue changes in frontal brain regions during adolescent development from our laboratory and others, data linking impulsivity and risk behavior to white matter integrity (Asato, Terwillinger, Woo, & Luna, 2010; Bava et al., 2010; Berns, et al., 2009; Li, Mathews, Wang, Dunn, & Kronenberger, 2005; Liston et al., 2006; Silveri et al., 2006;) and neurobiological models of adolescent risk taking (Casey et al., 2008; Ernst et al., 2005; Galvan et al., 2006; Steinberg, 2008) we chose to examine bilateral white matter regions containing fiber tracts associated with higher-order cognitive functioning and reciprocal frontal-subcortical and cortico-limbic projections in four regions of interest (ROIs) a priori. Regions of interest are: (1) body of the fornix, a large white matter bundle within the limbic system; (2) superior corona radiata, to capture cortico-limbic and cortico-thalamic fiber tracts; and two association fiber tracts identified in our previous prospective investigation as white matter tracts with ongoing development from ages 17.5 – 19 in healthy controls (Bava et al., 2010), namely (3) superior longitudinal fasciculus, and (4) the superior fronto-occipital fasciculus (see Figure 1). Regions were averaged across brain hemispheres in order to reduce the number of linear regression equations examined. The ICBM-DTI-81 stereotaxic white matter parcellation map (Mori et al., 2008) pre-defined the bilateral white matter ROIs in each subject’s FA dataset. Diffusion and anatomical images were linearly transformed into MNI-152 space using FLIRT (Jenkinson, Bannister, Brady & Smith, 2002; Evans, Collins, & Milner, 1992). FSL’s Automated Segmentation Tool (FAST) (Zhang, Brady, & Smith, 2001) was used to create and apply white matter masks to ROIs, to ensure that only white matter voxels were considered. All white matter ROIs were visually inspected with careful reference to landmarks and coordinates identified by the white matter parcellation map, and further verified by white matter anatomical atlases (Mori et al., 2008; Mori, Wakana, Nagae-Poetscher, & van Zijl, 2005; Schmahmann & Pandya, 2006). Datasets were multiplied by the binary parcellation map to obtain average FA coefficients for each ROI.

Participants were interviewed about substance use, other behaviors, and psychopathological syndromes 18 months after their baseline visit with the protocol modified to accommodate an 18-month follow-up assessment interval and developmental changes suitable to 17–20 year-olds (e.g., YSR or ASR, Timeline Followback, CDDR). Valid baseline DTI following 28 days of monitored abstinence, and baseline and 18-month follow-up interviews data were available for all 96 participants.

Data Analysis

ANOVAs and chi-square tests examined between-group differences, and Pearson’s rcorrelation coefficients identified demographic variables related to 18-month risk taking (p< .05) for inclusion as covariates where appropriate. The following variables were assessed for contribution to variance in 18-month risk taking scores: age, pubertal development, gender, intellectual functioning, Hollingshead SES, family history of substance use disorders, and mood. BDI total (r = .30, p<.01) and family history of an alcohol use disorder (point-biserial r = .21, p<.05) were found to be related to 18-month risk taking. These variables, in addition to baseline risk taking, were included as covariates in follow-up regression analysis to determine the utility of white matter integrity as a significant predictor above and beyond baseline risk taking, family history, and emotional functioning in both groups of adolescents.

Hierarchical linear regressions examining the influence of white matter integrity on both follow-up substance use frequency and aggression/delinquency were conducted separately for each of the 4 regions of interest. Given expected between-group differences, the regression models were first examined in the whole sample to identify if relationships between white matter integrity and prospective risk taking differed based on substance use group status (i.e., moderation effect). If a significant interaction was found, models were re-examined within each group separately to determine the degree to which baseline DTI indices predicted future risk behaviors at follow-up, above and beyond identified covariates, in adolescent substance users compared to adolescents with more limited substance use histories.

Results

The sample consisted of 96 adolescents (66% male, 65% Caucasian) with a mean age of 17 (SD 1; range 16 to 19) at project intake (see Table 1). No between-group differences were found for diffusion indices in the four ROIs (ps > .05), and this did not change after controlling for age.

Hierarchical linear regressions were first conducted in the full sample (N = 96) to determine the influence of baseline white matter integrity on follow-up substance use and aggressive/delinquent behaviors 1.5 years later, and to identify if associations were dependent on group status. Preliminary regression models included the baseline substance use or aggressive/delinquent behaviors variable on step 1, FA averaged bilaterally across each ROI on step 2, and the corresponding interaction term (i.e., cross product of SU versus CON group by FA for the ROI) on step 3. Baseline FA significantly predicted 18-month substance use and, as anticipated, this relationship was found to be dependent on group status (ps < .03). Significant predictors were: FA in the fornix (β = −.22, p =.01) and SCR (β = −.21, p =.01), with lower FA values in these tracts associated with more days of substance use 1.5 years later. Similarly, lower FA values in the fornix (β = −.21, p =.04) predicted more delinquent and aggressive risk taking at follow-up.

Follow-up regression models, to explore the significant interaction, were conducted separately in each group. Baseline substance use or aggressive/delinquent behavior, total depressive symptoms, and family history of an alcohol use disorder were entered on step 1, and baseline white matter integrity (FA) for each respective ROI (averaged bilaterally) on step 2. For the substance users group (n=47), baseline white matter integrity measures significantly predicted 18-month substance use frequency (in the past month), above and beyond covariates and baseline past-month substance use days. Significant predictors were: FA in the fornix (r = −.49, β = −.37, p =.01) and SCR ( r = −.34, β = −.31, p =.03), with lower FA values in these tracts associated with more days of substance use at 18-month follow-up. Similarly, lower FA values in the fornix r = −.30, β = −.32, p=.02) predicted more delinquent and aggressive risk taking at follow-up. To identify if the diffusion indices contributed to a significant model change above and beyond the covariates, an F-change statistic was calculated. FA in the fornix (ΔR² = .12, F(1,42) = 4.4, p < .01) and SCR (ΔR² = .10, F(1, 42) =3.9, p <.01) contributed to a significant model change in substance use risk taking. FA in the fornix also accounted for variance in 18-month aggressive and deliquent risk taking behaviors beyond the covariates (ΔR² = .10, F(1,42) = 4.8, p < .01) (see Figure 2). For controls (n=49), no direct relationship between FA and follow-up substance use or other risk taking behavior was seen.

Age and gender are known contributors to adolescent neurodevelopment; therefore, we reexamined the hierarchical linear regression models controlling for age and gender in step 1, along with family history of an alcohol use disorder and depressive symptoms. In the substance users, lower FA in the fornix (β = −.35, p =.03, ΔR² =.09, F(1,40) = 2.9, p = .02) and SCR (β = −.30, p =.04, ΔR² = .08, F(1,40) = 2.7, p = .03) were still found to predict more 18-month substance use frequency. Likewise, lower FA in the fornix accounted for variance in 18-month aggressive and delinquent risk taking (β = −.39, p < .01, ΔR² = .14, F(1,40) = 4.3, p < .01) with age and gender included as covariates. Thus, results were unchanged after controlling for age and gender.

To evaluate if risk taking was related to psychosocial functioning at follow-up, both risk-taking constructs were examined in relationship to number of hours worked at a job, involvement in recreational activities, grade point average, and long-term career plans. In the substance user group, greater aggressive and deliquent related risk taking behaviors was linked to lower school grade point average (r = −.33, p=.02). In the control group, greater substance use risk taking was associated with fewer hours worked at a job (r = −.36, p=.01).

Discussion

White matter integrity was found to be a potential predictor of risk taking behaviors in substance using youth. Evidence for a direct relationship between white matter integrity and risk taking was found in adolescents who were already heavy substance users by mid-adolescence, but not in those with very limited substance use histories by mid-adolescence. This predictive relationship was above and beyond variability accounted for by baseline risk taking, emotional functioning (i.e., depressive symptoms), and family history of an alcohol use disorder.

In the user group, greater microstructural coherence significantly predicted less risky behaviors at follow-up. Specifically, higher FA values in the fornix and superior corona radiata were associated with less risky substance use and delinquent behaviors. The fornix has been implicated in memory (D’Esposito, Verfaellie, Alexander, & Katz, 1995; Gaffan, Gaffan, & Hodges, 1991; Papanicolaou, Hasan, Boake, Eluvathingal, & Kramer, 2007; Poreh et al., 2006; Rudebeck et al., 2009; Tsivilis et al., 2008) and plays a large role in the limbic system, as it connects the hippocampus to regions such as the mammillary bodies and hypothalamus, and projects to prefrontal cortical areas (Haines, 2008). The limbic system and connections to later-developing striatal and prefrontal circuits have important influences on the propensity to engage in risk taking behaviors (Ernst & Fudge, 2009). Likewise, the corona radiata contains reciprocal prefrontal-striatal connections and projection fibers central to detection of salient rewarding cues, positive affect related to cues, and higher order processing necessary to effectively monitor cues and resist impulsive action. These findings provide evidence that decreased white matter microstructural integrity in subcortical limbic and projection fiber circuits may be related to more risky behaviors, above and beyond indicators of emotional functioning.

Overall, this study supported the predictive relationships between risk taking behaviors and limbic and projection fiber pathways in the fornix and superior corona radiata, above and beyond commonly used measures (e.g., family history) shown to be related to white matter integrity in teens (Herting, Schwartz, Mitchell, & Nagel, 2011). Findings underscore the role of white matter connectivity in biological models of risk taking and support previous studies (Berns et al. 2009; Casey et al., 2008; Sturman & Moghaddam, 2011) suggesting distinct pathways for neurocognitive control, risk taking, and reward-related behavior. The dual-systems biological model suggests that adolescent risk taking results from an imbalance in two systems with unique biological underpinnings, a more structurally mature subcortical limbic system operating in conjunction with a less mature cognitive control system. Given that findings were less remarkable between white matter integrity in association fiber tracts compared to limbic and subcortical projection fibers and risk taking behaviors, it is possible that immature cortical association fiber connections, such as the fronto-occipital fasciculus, and more rapidly maturing subcortical limbic connections found to be associated with risk taking behaviors in our sample, represent an imbalance in two distinct neural systems (i.e., higher-order executive functioning/cognitive control and reward processing), further supporting this dual-systems neurobiological model (Asato et al., 2010; Casey, 2008; Steinberg, 2004, 2008, 2010; Van Leifenhorst et al., 2010). Nevertheless, in an important cross sectional study by Berns and colleagues (2009) the authors found that better white matter integrity (i.e., increased FA along with decreased transverse diffusivity) predicted increased rebelliousness (i.e., drinking, smoking, taking drugs) as measured by the Adolescent Risk Questionnaire in a sample of 60 adolescents ages 14 to 18 years old; these findings are in the opposite direction as we found that poorer white mater integrity was related to moresubstance use behaviors and aggressive/delinquent behaviors. Our prospective sample was imaged 2–3 years later on average, and the former study may reflect more adaptive risk taking as teens explore their environment throughout earlier adolescence. The discrepant findings may reflect different methodological approaches (longitudinal region of interest approach as compared to cross sectional voxelwise and clusterwise statistical correlations). It is important to point out that findings within our substance using group may also reflect pre-existing alterations in white matter tract integrity (e.g., remodeling of the myelin or axons) related to heavier alcohol and marijuana use leaving our group vulnerable to engage in high risk behavior as opposed to the sample examined by Berns and colleagues. Notably, both cross sectional and prospective studies suggest that subcortical white matter may contribute to likelihood to engage in risk taking. However, the differential finding is clearly interesting and emphasizes the need for further research in this area.

The link between limbic and subcortical white matter tracts and risk taking behavior may be more pronounced in the user group due to the purported neurotoxic effects of alcohol and marijuana on white matter tissue development (Ashtari, Cervellione, Cottone, Ardekani, & Kumra, 2009; Bava et al., 2009; Jacobus et al., 2009). It is possible that heavy alcohol and marijuana use may have altered subcortical fibers (specifically, tracts thought to develop earlier in neurodevelopment) necessary to efficiently monitor and regulate risk taking behavior (Asato et al., 2010; Liston et al., 2006; Luna, 2009). For instance, the literature suggests that heavy alcohol use (including binge drinking behaviors) during adolescence may alter fiber integrity via neurotoxic effects on ongoing myelination, axonal development, and neural circuitry (De Bellis et al., 2008; McQueeny et al., 2009; Tapert et al., 2003) While the effects of marijuana use remain more inconclusive (Delisi et al., 2006) there is some evidence to suggest changes in tract coherence associated with adolescent marijuana users (Arnone et al., 2008; Ashtari et al., 2009); the underlying mechanisms of these two commonly used substances may also interact in the microstructural development of the adolescent brain (e.g., excitotoxic cell death, oxidative stress) and be attributable to additive and/or synergistic effects that alter adolescent neurodevelopment (Bava et al., 2009; Jacobus et al., 2009). Taken together, adolescent alcohol and drug use likely affects fiber tract coherence within cortico-limbic regions important for quick signal communication between anatomical regions (still undergoing maturation at various stages) that require efficient transmission to regulate urges to engage in risky behaviors. Poor neural integrity may not only put substance-using teens at increased risk for poor resistance to impulses and future risk taking (particularly in the presence of distracters and emotionally salient cues), but also lead to problems with emotional regulation, psychopathology (e.g., anxiety disorders), and addiction later in life (Casey & Jones, 2010; Casey et al., 2010).

An inherent limitation to this study is that all contributing factors to risk taking cannot be controlled; however, the purpose of this investigation was to examine one potential biological component of risk taking. While this was a prospective study, any conclusion about causal effects must be interpreted with caution. The age range was limited to late adolescence and focused on risks related to substance use, delinquency, and aggressive behaviors. Risky sexual activity, gambling, and tobacco use also have serious consequences and should be the focus of future research. Further, while the vast majority of controls reported engaging in some risky behaviors (84%) and over half (approximately 60%) reported increasing their substance use at follow-up, it is possible that fewer risky behaviors in this group contributed to a lack of significant findings; it is still possible that similar associations exist in a sample of non-substance using teens despite the limited findings of our investigation. We utilized a theory-driven region of interest approach as opposed to a whole brain voxelwise approach used in previous investigations in our laboratory; while we did not find users and non-users to differ in integrity of these large bilateral white matter areas that were selected on the basis of linkage to risk and ongoing development, voxelwise analysis may be more sensitive to such effects and will be the focus of future longitudinal investigations. Finally, because the overall effect sizes were relatively modest and averaged across brain hemispheres replication would be helpful.

The project excluded individuals unable to remain abstinent prior to testing, as well as teens with histories of traumatic brain injury, psychiatric disorders, and learning disabilities. Risk taking should be examined closely in these higher-risk individuals. Identification of teens at increased risk for dangerous behaviors could help mental health providers design prevention programs and select intervention strategies that limit risk taking opportunity and encourage healthier outlets requiring less effective utilization of cognitive control (e.g., community activism, recreational activities). In the future, multi-domain risk taking assessment (e.g., imaging, personality) may be used to identify and guide high-risk teenagers. Future projects will examine interrelationships between structural predictors of brain maturation, such as white matter integrity and gray matter cortical thickness, to better understand if changes in multiple aspects of tissue architecture can be used together to predict prospective substance use behaviors in teens.

Link to Article

White Matter Integrity in Mid-Adolescence Predicts Future Substance Use and Aggressive/Delinquent Behavior in Heavy-Using, But Not Abstinent, Teens

Introduction

Diffusion tensor imaging (DTI) allows for the investigation of water diffusion within brain tissue, providing insights into white matter microstructure, including density, coherence, compactness, and fiber diameter (Le Bihan et al., 2001; Suzuki et al., 2003; Taylor et al., 2004). Studies have demonstrated that white matter undergoes significant development during adolescence, characterized by increased myelination and organization of fiber tracts (Benes et al., 1994; Brody et al., 1987; Yakovlev & Lecuors, 1967). Fractional anisotropy (FA), a key DTI parameter, reflects the directionality of water movement within axons, serving as an indicator of white matter integrity (Barnea-Goraly et al., 2005; Fryer et al., 2008; Klingberg et al., 1999; Muetzel et al., 2008; Schmithorst et al., 2002; Snook et al., 2005). Improved white matter maturation is thought to enhance inter-regional brain communication, with even subtle alterations potentially impacting neurocognitive abilities and complex behaviors (Cascio et al., 2007). Our prior work has revealed substantial FA increases in long-association and projection fibers, such as the corona radiata, superior longitudinal fasciculus, and fronto-occipital fasciculus, during late adolescence (Bava et al., 2010). These tracts are crucial for connecting frontal and posterior cortical regions, as well as subcortical structures.

Adolescence is marked by concurrent advancements in executive function, cognitive control, and structural and microstructural changes in prefrontal and subcortical brain regions (Anderson et al., 2001; Conklin et al., 2007; Levin et al., 1991; Luna et al., 2010; Rubia et al., 2006; Tamm et al., 2002; Van Leifenhorst et al., 2010). Efficient subcortical-frontal projections are particularly vital for executive functioning (Anderson et al., 2001; Cascio et al., 2007; Conklin et al., 2007; Levin et al., 1991; Liston et al., 2006; Luna et al., 2010; Qiu et al., 2008; Royall et al., 2002; Rubia et al., 2006; Tamm et al., 2002; Van Leifenhorst et al., 2010). Continuous development of these projections during adolescence likely underpins the refinement of neuronal communication and brain circuitry, coinciding with improved problem-solving, response inhibition, mental flexibility, set-shifting, and sustained attention (Barnea-Goraly et al., 2005; Ben Bashat et al., 2005; Eluvathingal et al., 2007; Giorgio et al., 2010; Lebel et al., 2008; Liston et al., 2006; Paus et al., 2001; Schmithorst et al., 2002).

Risk-taking, encompassing both substance use and non-substance use behaviors, is a complex construct involving engagement in activities with a significant probability of negative outcomes or harm (Boyer, 2006; Centers for Disease Control and Prevention, 2010). Individual factors such as mood, cognitive status, personality, sex, and culture influence risk-taking propensity (Boyer, 2006; Casey et al., 2008; Galvan et al., 2007; Steinberg, 2004; Spear, 2010). A neurobiological perspective posits that the differential developmental trajectories of the prefrontal cortex (control) and limbic system (reward) during adolescence contribute to increased risk-taking vulnerability (Casey et al., 2008; Ernst et al., 2005; Galvan et al., 2006, 2007; Steinberg, 2004, 2008). Studies have shown heightened subcortical activation and more diffuse prefrontal recruitment in adolescents during risky decision-making (Brown et al., 2005; Bunge et al., 2002; Casey et al., 1997, 2002; Crone et al., 2006; Galvan et al., 2006; Moses et al., 2002; Tamm et al., 2002). This suggests that the still-maturing prefrontal cortex may not exert sufficient top-down control, while delayed or altered white matter maturation in association and cortico-limbic tracts (e.g., superior longitudinal fasciculus, anterior/superior corona radiata) may disrupt prefrontal-limbic communication, contributing to impulsive behavior (Casey et al., 2008; Steinberg, 2008; Van Leifenhorst et al., 2010). Substance use during this period may further exacerbate these neurodevelopmental processes, potentially hindering functional connectivity and increasing the likelihood of engaging in risky behaviors. Neuroimaging biomarkers hold promise for identifying individuals at risk for substance use and other risky behaviors during late adolescence and early adulthood (Squeglia et al., 2009; Tapert et al., 2004, 2007).

Adolescent risk-taking follows a nonlinear trajectory, peaking during this developmental period. Externalizing behaviors, often characterized as "undercontrolled" and reflecting difficulty in inhibiting impulsive actions, are commonly assessed throughout childhood and adolescence to identify individuals prone to excessive rule-breaking (Achenbach & Edelbrock, 1978; Achenbach & Rescorla, 2001). These assessments have proven useful in predicting future risk-taking (Thompson et al., 2011). As noted by Boyer (2006), risk-taking encompasses various labels in the literature (e.g., externalizing, norm-breaking, problem behaviors), all generally referring to behaviors that pose potential harm or danger to adolescents. It is highly probable that a confluence of personality traits (e.g., sensation seeking), biological factors, and cultural influences contribute to such risky behaviors. Indeed, in the United States, a substantial proportion of adolescents report recent alcohol and marijuana use (Centers for Disease Control and Prevention, 2010). Despite the potential benefits of moderate risk-taking and novelty-seeking during adolescence (Spear, 2010), excessive engagement in risky behaviors can have severe and long-lasting consequences on health and well-being. Research exploring the association between brain microstructure and risk-taking remains limited. A recent cross-sectional study by Berns et al. (2009) suggests a link between greater white matter coherence in the superior/anterior corona radiata and increased substance use-related risk-taking. However, this finding contradicts prevailing neurodevelopmental models of adolescent risk-taking and warrants further investigation.

Early identification of at-risk adolescents is crucial for effective prevention efforts. However, the predictive value of neuroimaging indices in this context remains underexplored. This study aimed to examine the relationship between white matter microstructural integrity in mid-adolescence and real-world risk-taking behaviors 1.5 years later, a period marked by a peak incidence of substance use and dependence (Substance Abuse and Mental Health Services Administration, 2010). We hypothesized that poorer white matter integrity in association and cortico-limbic fiber tracts, measured using DTI at baseline, would predict greater risk-taking at 18-month follow-up in both heavy substance users and adolescents with limited substance use histories. We further hypothesized that this relationship would be more pronounced in the heavy substance-using group due to their increased vulnerability to compromised fronto-subcortical connectivity, stemming from both typical adolescent developmental trajectories and potential substance-related neurotoxic effects.

Methods and Materials

Participants

The study included 96 adolescents (66% male, 65% Caucasian) from an ongoing longitudinal study investigating the impact of marijuana use on neurodevelopment (Bava et al., 2010; Mahmood et al., 2010; Tapert et al., 2007; Schweinsburg et al., 2011). This sample, with a mean age of 17 years (SD = 1; range 16-19) at baseline, comprised individuals from previous publications, including controls (n = 22) and substance users (n = 72) who were matched on demographic variables (Bava et al., 2009, 2010). All participants had valid baseline DTI data and complete risk-taking data at the 18-month follow-up. Informed consent and assent were obtained from all participants and their parents at both time points.

Participants were recruited from local schools and categorized as substance users (SU; n = 47) if they reported >200 lifetime cannabis use experiences or as controls (CON; n = 49) if they had <10 lifetime cannabis use experiences at baseline. The SU group was, on average, 5 months older than the CON group and reported a greater familial history of substance use disorders. The SU group also exhibited lower academic performance at both baseline and follow-up and reported less state anxiety on the day of the scan (ps < .05). As expected, substance use was significantly higher in the SU group at both time points (Table 1).

Measures

Substance Use

The Customary Drinking and Drug Use Record (CDDR; Brown et al., 1998) assessed lifetime and past 3-month use of tobacco, alcohol, and other drugs, as well as withdrawal symptoms and DSM-IV abuse/dependence criteria. The Timeline Followback (Sobell & Sobell, 1992) was used to assess recent substance use (excluding nicotine and caffeine) in the 28 days prior to abstinence initiation and the 18-month follow-up. Substance use T-scores were calculated for each participant at both time points based on the sample means and standard deviations.

Other Risk-Taking Behaviors

Parents completed the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2001) at baseline, while participants completed either the Youth Self-Report (YSR; for ages ≤18) or Adult Self-Report (ASR; for ages ≥19) at follow-up. These measures provided a comprehensive assessment of various behavioral and emotional problems. Specific items from the Aggression and Delinquency/Rule Breaking scales were selected a priori to reflect real-world risk-taking. Internal consistency for these items was high across the CBCL (α = .91), YSR (α = .90), and ASR (α = .80). T-scores were calculated for aggressive/delinquent behaviors based on sample means and standard deviations.

Additional Baseline Measures

The Wechsler Abbreviated Scale of Intelligence Vocabulary subtest (Wechsler, 1999) was used to estimate intellectual functioning. The Beck Depression Inventory-II (BDI-II; Beck et al., 1961) and Spielberger State-Trait Anxiety Inventory (Spielberger et al., 1970) assessed depression and anxiety on the day of imaging. The Family History Assessment Module (FHAM; Rice et al., 1995) gathered information on family history of substance use and mental health problems. The Pubertal Development Scale (PDS; Peterson et al., 1988) assessed physical development stage.

Procedures

Participants were required to abstain from substances for 28 days prior to baseline DTI scanning, with compliance monitored through semi-weekly urine drug toxicology screens and breathalyzers. A 3.0-Tesla General Electric CXK4 MRI scanner was used to acquire high-resolution 3D T1-weighted anatomical images and DTI data. The DTI sequence was optimized for minimal echo time and utilized a single-shot dual spin echo excitation (Reese et al., 2003) to reduce artifacts.

DTI data were processed using FSL (Parker, 2004) and AFNI (Cox, 1996) software packages. Preprocessing steps included unwarping, motion correction, eddy current correction, and brain extraction. FA values were calculated using a log-linear estimation procedure.

Based on previous research, four bilateral white matter regions of interest (ROIs) were selected a priori: (1) body of the fornix, (2) superior corona radiata, (3) superior longitudinal fasciculus, and (4) superior fronto-occipital fasciculus. These ROIs were chosen based on their involvement in higher-order cognitive function, frontal-subcortical projections, and previously observed developmental changes during adolescence. The ICBM-DTI-81 white matter atlas (Mori et al., 2008) was used to define the ROIs. FA values were averaged across hemispheres for each ROI.

Data Analysis

ANOVAs and chi-square tests were used to examine group differences. Pearson’s correlations identified demographic variables related to 18-month risk-taking for inclusion as covariates. Hierarchical linear regressions assessed the relationship between baseline white matter integrity (FA in each ROI) and follow-up substance use and aggressive/delinquent behaviors, controlling for baseline risk-taking, depressive symptoms, and family history of alcohol use disorder. Separate regressions were conducted for each ROI in the full sample and within each group.

Results

No group differences were observed for FA in any of the ROIs at baseline (ps > .05). In the full sample, baseline FA in the fornix (β = −.22, p = .01) and superior corona radiata (β = −.21, p = .01) significantly predicted 18-month substance use, with lower FA associated with more substance use days. Similarly, lower FA in the fornix (β = −.21, p = .04) predicted more aggressive/delinquent behaviors at follow-up. These relationships were dependent on group status (ps < .03).

In the SU group, lower FA in the fornix (r = −.49, β = −.37, p = .01) and superior corona radiata (r = −.34, β = −.31, p = .03) predicted more substance use days at follow-up, controlling for covariates. Lower FA in the fornix (r = −.30, β = −.32, p = .02) also predicted more aggressive/delinquent behaviors. These findings remained significant after controlling for age and gender. No significant relationships were found between FA and risk-taking in the CON group.

Greater aggressive/delinquent behaviors were associated with lower grade point average in the SU group (r = −.33, p = .02), while greater substance use risk-taking was associated with fewer hours worked in the CON group (r = −.36, p = .01).

Discussion

Our findings suggest that white matter integrity in mid-adolescence may serve as a predictor of future risk-taking behaviors, particularly in youth with established patterns of heavy substance use. Notably, greater microstructural coherence in the fornix and superior corona radiata was associated with reduced risk-taking at 18-month follow-up, even after accounting for baseline risk-taking, emotional functioning, and family history of alcohol use disorder.

The fornix, a crucial component of the limbic system, plays a vital role in memory processes (D'Esposito et al., 1995; Gaffan et al., 1991; Papanicolaou et al., 2007; Poreh et al., 2006; Rudebeck et al., 2009; Tsivilis et al., 2008). This structure connects the hippocampus to various regions, including the mammillary bodies, hypothalamus, and prefrontal cortex (Haines, 2008). Notably, the limbic system and its connections to the developing striatal and prefrontal circuits are implicated in risk-taking behavior (Ernst & Fudge, 2009). The corona radiata, containing reciprocal prefrontal-striatal projections, is essential for processing reward-related cues and regulating impulsive actions. Our findings underscore the importance of white matter integrity within these limbic and subcortical projection circuits in predicting risk-taking behaviors, above and beyond other contributing factors.

Our findings align with previous research highlighting the role of white matter connectivity in risk-taking behavior (Berns et al., 2009; Casey et al., 2008; Sturman & Moghaddam, 2011). The observed relationships between risk-taking and limbic/subcortical tracts (fornix, superior corona radiata), compared to association fibers, support the dual-systems model of adolescent risk-taking. This model posits that an imbalance between a more mature subcortical reward system and a less mature cognitive control system contributes to increased risk-taking during adolescence (Asato et al., 2010; Casey, 2008; Steinberg, 2004, 2008, 2010; Van Leifenhorst et al., 2010).

The stronger association between white matter integrity and risk-taking in the substance-using group may reflect the neurotoxic effects of alcohol and marijuana on white matter development (Ashtari et al., 2009; Bava et al., 2009; Jacobus et al., 2009). Heavy alcohol use during adolescence has been linked to altered fiber integrity due to disruptions in myelination, axonal development, and neural circuitry (De Bellis et al., 2008; McQueeny et al., 2009; Tapert et al., 2003). While the effects of marijuana are less conclusive, studies suggest potential alterations in white matter coherence associated with adolescent use (Arnone et al., 2008; Ashtari et al., 2009). It is plausible that the combined effects of alcohol and marijuana use may exacerbate disruptions in cortico-limbic white matter development, potentially increasing vulnerability to risk-taking behaviors.

It is important to note that our findings do not establish a causal relationship between white matter integrity and risk-taking. The observed associations may be influenced by other unmeasured factors. Furthermore, our sample excluded individuals with a history of traumatic brain injury, psychiatric disorders, and learning disabilities. Future studies should examine the relationship between white matter integrity and risk-taking in these populations.

Despite these limitations, our study provides valuable insights into the neurobiological underpinnings of adolescent risk-taking. The identification of white matter integrity as a potential predictor of future risk-taking, particularly in heavy substance users, highlights the importance of early intervention and prevention efforts. Future research should explore the interplay between structural brain development, functional connectivity, and individual differences in risk-taking propensity to develop targeted interventions aimed at mitigating risk and promoting healthy decision-making during adolescence.

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White Matter Integrity in Mid-Adolescence Predicts Future Substance Use and Aggressive/Delinquent Behavior in Teens with Histories of Heavy Substance Use

Introduction

Diffusion Tensor Imaging (DTI) is a neuroimaging technique that allows us to see how water moves through the white matter of the brain. This movement of water can tell us about the structure of the white matter, which is made up of bundles of nerve fibers that connect different parts of the brain. As adolescents grow, their brains continue to develop, and this includes the white matter. In particular, the connections between the front part of the brain (prefrontal cortex), which is important for planning and decision-making, and other brain areas improve during adolescence. These improved connections are thought to be important for the development of skills like self-control and decision-making.

As teenagers' brains are still developing, they are often more likely to engage in risky behaviors, such as substance use, compared to adults. This is thought to be because the parts of the brain responsible for controlling impulses and making decisions are not yet fully developed. Previous research has shown that differences in the structure of white matter in the brain, particularly in the connections between the prefrontal cortex and other areas, are associated with individual differences in impulsivity and risk-taking.

This study aimed to investigate whether DTI measures of white matter integrity in mid-adolescence could predict future risky behaviors, including substance use and aggressive or delinquent behavior, 1.5 years later. The researchers were particularly interested in whether this relationship would be stronger in adolescents who were already using substances heavily, as these individuals might be more vulnerable to problems with brain development due to the potential negative effects of substance use on the brain.

Methods and Materials

Participants

Ninety-six adolescents (ages 16-19) participating in a larger longitudinal study on the effects of marijuana use on brain development were included in this study. Participants were divided into two groups based on their history of cannabis use: a substance user (SU) group (n=47) who reported using cannabis more than 200 times in their life and a control (CON) group (n=49) with minimal cannabis use (less than 10 times). The groups were similar in terms of age, gender, and ethnicity but differed in terms of substance use history, academic performance, and family history of substance use disorders.

Measures

Substance Use

Participants’ use of alcohol, tobacco, and other drugs was assessed at the beginning of the study (baseline) and again 1.5 years later (follow-up) using standardized interviews and questionnaires.

Other Risk Taking Behaviors

Parents rated their child's behavior at baseline using the Child Behavior Checklist (CBCL), while participants rated their own behavior at follow-up using the Youth Self Report (YSR) or Adult Self Report (ASR). These measures assessed behaviors related to aggression and rule-breaking, which are considered indicators of real-world risk-taking.

Brain Imaging

At baseline, participants underwent DTI scans to assess the white matter integrity of their brains. The researchers focused on four specific white matter tracts that connect the prefrontal cortex to other brain regions and are known to be important for cognitive control, emotional regulation, and reward processing.

Data Analysis

Statistical analyses were conducted to investigate whether white matter integrity at baseline could predict future risk-taking behaviors at follow-up, taking into account other factors like baseline risk-taking, depression, and family history of substance use disorders.

Results

The study found that the relationship between white matter integrity at baseline and risk-taking behaviors 1.5 years later differed depending on the adolescent's substance use group.

• Substance Users (SU): Poorer white matter integrity in two specific white matter tracts, the fornix (involved in memory and emotional processing) and the superior corona radiata (involved in communication between the prefrontal cortex and other brain areas), at baseline was associated with a greater number of days of substance use and more aggressive/delinquent behaviors at follow-up. This means that those with less organized white matter in these tracts were more likely to engage in risky behaviors.

• Control Group (CON): There was no significant relationship between white matter integrity at baseline and risk-taking behaviors at follow-up in the control group.

Discussion

This study provides evidence that the structure of white matter in the brain, particularly in areas connecting to the prefrontal cortex, may be an important factor in understanding why some adolescents are more likely to engage in risky behaviors. Specifically, poorer white matter integrity in these areas was associated with an increased likelihood of substance use and aggressive/delinquent behavior in adolescents who were already heavy substance users, suggesting that existing substance use may exacerbate vulnerabilities related to brain development and increase the risk of future problems.

Importantly, these findings were observed even after accounting for other known risk factors for these behaviors, like family history and depression. This suggests that white matter integrity may be a unique and potentially useful predictor of future risk-taking.

It is important to note that this study has some limitations. First, the sample size was relatively small, and the findings need to be replicated in larger, more diverse groups of adolescents. Second, while the study was longitudinal, meaning it followed participants over time, it is still not possible to say for certain that poorer white matter integrity caused the increased risk-taking behaviors. It is possible that other factors, such as genetics or environmental influences, contribute to both white matter development and risk-taking.

Despite these limitations, this study highlights the potential of brain imaging techniques like DTI to improve our understanding of adolescent risk-taking. In the future, such techniques could potentially be used to identify adolescents who may be at higher risk for developing problems with substance use or other risky behaviors, allowing for early intervention and prevention efforts.

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Teenage Brains and Risky Behavior: What's the Connection?

Introduction

Our brains are constantly developing, especially during our teenage years. One important change happening in the brain is something called "myelination." Think of myelination like insulation for electrical wires. It helps messages travel faster and more efficiently between different parts of the brain. Diffusion tensor imaging (DTI) is like a special brain scan that lets scientists see how well-insulated (myelinated) the connections in the brain are.

Scientists have found that as teenagers grow up, the connections between the front part of the brain (the prefrontal cortex) and other areas become better insulated. The prefrontal cortex is really important because it helps us make good decisions, control our impulses, and plan for the future. Basically, it's like the "boss" of our brain.

But here's the thing: teenagers are also known for taking risks. This might mean trying drugs and alcohol, breaking rules, or making decisions without thinking about the consequences. So, what's going on? Why would teenagers, whose brains are developing so much, still engage in risky behaviors?

Well, some scientists believe that even though the brain is developing, the connections between the prefrontal cortex and the parts of the brain that control emotions and rewards aren't fully developed yet. This means that teenagers might feel strong urges and emotions without the prefrontal cortex being able to step in and say, "Hold on a second, is this really a good idea?" It's like having a powerful engine in a car but not having a good braking system.

This study wanted to see if there was a connection between how well-insulated the connections in the brain are (especially those connected to the prefrontal cortex) and whether or not teenagers were likely to take risks. They also wanted to know if this connection was stronger in teenagers who already used drugs and alcohol a lot.

Methods and Materials

Participants

Ninety-six teenagers, aged 16 to 19, participated in this study. These teenagers were already part of a larger study looking at the effects of marijuana use on the brain. The researchers divided the teenagers into two groups: those who used marijuana and alcohol a lot (the substance users or "SU" group) and those who didn't (the control or "CON" group). The researchers made sure that both groups were similar in terms of age, gender, and race.

Measures

Substance Use

The researchers asked the teenagers a lot of questions about their substance use, like how often they used drugs and alcohol, what kinds of drugs they used, and if they felt like they had a problem with substance use.

Other Risky Behaviors

The researchers also asked the teenagers and their parents about other risky behaviors, like getting in trouble at school, fighting, or breaking rules. They used special questionnaires to measure these behaviors.

Brain Scans

All of the teenagers had special brain scans called DTI scans. These scans allowed the researchers to see how well-insulated the connections in their brains were. They focused on specific areas of the brain that are important for decision-making, impulse control, and processing emotions and rewards.

Procedures

The teenagers had their brain scans after not using any drugs or alcohol for 28 days. The researchers made sure the teenagers weren't using drugs or alcohol by having them take urine tests and breathalyzer tests. Then, the researchers compared the teenagers' brain scans to their answers on the questionnaires about substance use and other risky behaviors.

Results

The researchers found that teenagers who used drugs and alcohol a lot and had less well-insulated connections between the prefrontal cortex and other parts of the brain were more likely to engage in risky behaviors 1.5 years later. This means that teenagers with less efficient connections in these areas might have a harder time controlling their impulses and making good decisions, especially when it comes to substance use and rule-breaking.

However, the researchers didn't find this connection in the teenagers who didn't use drugs and alcohol very much. This could be because using drugs and alcohol a lot might actually damage the connections in the brain, making it even harder to control risky behaviors.

Discussion

This study tells us that the way our brains are wired, especially the connections between the prefrontal cortex and other areas, might play a role in whether or not teenagers engage in risky behaviors. It also suggests that using drugs and alcohol a lot during adolescence might actually damage these important connections, making it even harder to make good decisions and avoid risky behaviors.

It's important to remember that this study doesn't mean that all teenagers who use drugs and alcohol are going to have problems with risky behavior, or that all teenagers with less well-insulated connections in their brains are destined to make bad choices. But it does suggest that these biological factors might be important to consider when trying to understand why teenagers take risks.

This study also highlights the importance of preventing teenage substance use. By helping teenagers avoid drugs and alcohol, we might be able to protect their developing brains and reduce their risk for problems later in life.

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Can we predict risky behavior? A look at teenagers' brains

Introduction

Scientists use a special brain imaging technique called diffusion tensor imaging (DTI) to see how water moves in the brain. This helps them understand how well different parts of the brain are connected. Think of it like looking at the wires that connect different parts of a computer! As teenagers grow, these connections get stronger, especially in the front part of the brain, which helps with planning and making decisions. Better connections in the brain are linked to better thinking and decision-making skills.

Just like how a computer works better with good connections, teenagers' brains work better when the connections are strong. This helps them make better choices and avoid risky behaviors, like using drugs or acting out. Sometimes, these connections aren't as strong, which might make it harder for teenagers to control their impulses and make safe decisions.

We know that teenagers sometimes make risky choices, like trying drugs or breaking rules. This happens partly because their brains are still developing. We wanted to see if the strength of brain connections in teenagers could predict if they would engage in risky behaviors later on. We thought that weaker connections in certain parts of the brain might mean teenagers would be more likely to take risks. We also thought this might be even more true for teenagers who already use drugs, because drugs can affect how the brain develops.

Methods and Materials

Participants

We invited 96 teenagers, aged 16 to 19, to be a part of our study. These teenagers were already part of a bigger study looking at how marijuana use affects the brain. We split the teenagers into two groups: those who used a lot of marijuana (we'll call them the SU group) and those who rarely used it (we'll call them the CON group). We made sure that nobody in the study had any other brain injuries or disorders that might affect the results.

Measures

Substance Use

We asked teenagers about their drug use, including marijuana, alcohol, and tobacco. We wanted to know how often they used and whether they had problems controlling their use.

Other Risky Behaviors

We also asked teenagers and their parents about other risky behaviors, like getting into fights, breaking rules, or acting aggressively. We used special questionnaires to see how often teenagers engaged in these behaviors.

Brain Scans

We used DTI to look at the strength of connections in the brains of all the teenagers. We focused on specific areas of the brain that we know are important for making decisions and controlling impulses.

Procedures

First, we asked all the teenagers to stop using any drugs for 28 days. We checked their urine to make sure they were not using drugs. Then, we took pictures of their brains using the DTI machine. Eighteen months later, we asked the teenagers about their substance use and other risky behaviors again.

Data Analysis

We used special computer programs to see if there was a relationship between the strength of brain connections in the teenagers at the beginning of the study and their risky behaviors 18 months later. We also wanted to see if this relationship was different for the SU group and the CON group.

Results

We found that teenagers who used a lot of marijuana and had weaker connections in certain parts of their brains at the beginning of the study were more likely to engage in risky behaviors 18 months later. This was true even when we considered other things that could affect risky behavior, like mood, family history of drug use, and how much they were already engaging in risky behaviors at the beginning of the study.

These brain areas are important for controlling impulses and making decisions, especially when it comes to things that are rewarding or exciting. This means that teenagers with weaker connections in these areas might have a harder time resisting the urge to engage in risky behaviors.

We did not see the same relationship in teenagers who didn't use a lot of marijuana at the beginning of the study. This could be because drug use itself affects brain development, making those connections weaker.

Discussion

This study shows that the strength of connections in certain parts of the brain might be able to predict which teenagers are more likely to engage in risky behaviors, like drug use and aggression. This is especially true for teenagers who already use drugs heavily.

It's important to remember that this study doesn't mean that all teenagers with weaker connections in these brain areas will engage in risky behaviors. Lots of other things matter too, like family support, peer influence, and individual personality.

However, this study helps us understand that the brain plays an important role in risk-taking behavior, and that drug use during adolescence might make it even harder for teenagers to make healthy choices. This knowledge can help us develop better ways to prevent risky behaviors in teenagers. We can teach them about the effects of drugs on the brain and give them tools to make healthier choices.

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