Brain Networks and Adolescent Alcohol Use

Functional brain connectivity predictors of prospective substance use initiation and their environmental correlates

Background

Early substance use initiation (SUI) places youth at substantially higher risk for later substance use disorders. Furthermore, adolescence is a critical period for the maturation of brain networks, the pace and magnitude of which are susceptible to environmental influences and may shape risk for SUI.

Methods

We examined whether patterns of functional brain connectivity during rest (rsFC), measured longitudinally in pre-and-early adolescence, can predict future SUI. In an independent sub-sample, we also tested whether these patterns are associated with key environmental factors, specifically neighborhood pollution and socioeconomic dimensions. We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study®. SUI was defined as first-time use of at least one full dose of alcohol, nicotine, cannabis, or other drugs. We created a control group (N = 228) of participants without SUI who were matched with the SUI group (N = 233) on age, sex, race/ethnicity, and parental income and education.

Results

Multivariate analysis showed that whole-brain rsFC prior to SUI during 9-10 and 11-12 years of age successfully differentiated the prospective SUI and control groups. This rsFC signature was expressed more at older ages in both groups, suggesting a pattern of accelerated maturation in the SUI group in the years prior to SUI. In an independent sub-sample (N = 2,854) and adjusted for family socioeconomic factors, expression of this rsFC pattern was associated with higher pollution, but not neighborhood disadvantage.

Conclusion

Brain functional connectivity patterns in early adolescence that are linked to accelerated maturation and environmental exposures can predict future SUI in youth.

Neural responses to reward, threat, and emotion regulation and transition to hazardous alcohol use

AIMS

Reward processing and regulation of emotions are thought to impact the development of addictive behaviors. In this study, we aimed to determine whether neural responses during reward anticipation, threat appraisal, emotion reactivity, and cognitive reappraisal predicted the transition from low-level to hazardous alcohol use over a 12-month period.

METHODS

Seventy-eight individuals aged 18-22 with low-level alcohol use [i.e. Alcohol Use Disorder Identification Test (AUDIT) score <7] at baseline were enrolled. They completed reward-based and emotion regulation tasks during magnetic resonance imaging to examine reward anticipation, emotional reactivity, cognitive reappraisal, and threat anticipation (in the nucleus accumbens, amygdala, superior frontal gyrus, and insula, respectively). Participants completed self-report measures at 3-, 6-, 9-, and 12-month follow-up time points to determine if they transitioned to hazardous use (as defined by AUDIT scores ≥8).

RESULTS

Of the 57 participants who completed follow-up, 14 (24.6%) transitioned to hazardous alcohol use. Higher baseline AUDIT scores were associated with greater odds of transitioning to hazardous use (odds ratio = 1.73, 95% confidence interval 1.13-2.66, P = .005). Brain activation to reward, threat, and emotion regulation was not associated with alcohol use. Of the neural variables, the amygdala response to negative imagery was numerically larger in young adults who transitioned to hazardous use (g = 0.31), but this effect was not significant.

CONCLUSIONS

Baseline drinking levels were significantly associated with the transition to hazardous alcohol use. Studies with larger samples and longer follow-up should test whether the amygdala response to negative emotional imagery can be used to indicate a future transition to hazardous alcohol use.

Neural response to threat and reward among young adults at risk for alcohol use disorder

Alcohol use disorder (AUD) is heritable. Thus, young adults with positive family histories represent an at-risk group relative to those without a family history, and if studied at a time when both groups have similar levels of alcohol use, it provides an opportunity to identify neural processing patterns associated with risk for AUD. Previous studies have shown that diminished response to potential reward is associated with genetic risk for AUD, but it is unclear how threat may modulate this response. We used a modified Monetary Incentive Delay task during fMRI to examine neural correlates of the interaction between threat and reward anticipation in a sample of young adults with (n = 31) and without (n = 44) family histories of harmful alcohol use. We found an interaction (p = 0.048) between cue and group in the right nucleus accumbens where the family history positive group showed less differentiation to the anticipation of gaining $5 and losing $5 relative to gaining $0. The family history-positive group also reported less excitement for trials to gain $5 relative to gaining $0 (p < 0.001). Family history-positive individuals showed less activation in the left insula during both safe and threat blocks compared to family history-negative individuals (p = 0.005), but the groups did not differ as a function of threat (p > 0.70). Young adults with, relative to without, enriched risk for AUD may have diminished reward processing via both neural and behavioural markers to potential rewarding and negative consequences. Neural response to threat may not be a contributing factor to risk at this stage.

Brain-phenotype predictions can survive across diverse real-world data

Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.