Neuropsychosocial markers of binge drinking in young adults

Abstract

Binge drinking is associated with disease and death, and developing tools to identify risky drinkers could mitigate its damage. Brain processes underlie risky drinking, so we examined whether neural and psychosocial markers could identify binge drinkers. Reward is the most widely studied neural process in addiction, but processes such as emotion, social cognition, and self-regulation are also involved. Here we examined whether neural processes apart from reward contribute to predicting risky drinking behaviors. From the Human Connectome Project, we identified 177 young adults who binged weekly and 309 nonbingers. We divided the sample into a training and a testing set and used machine-learning algorithms to classify participants based on psychosocial, neural, or both (neuropsychosocial) data. We also developed separate models for each of the seven fMRI tasks used in the study. An ensemble model developed in the training dataset was then applied to the testing dataset. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and differences between models were assessed using DeLong's test. The three models performed better than chance in the test sample with the neuropsychosocial (AUC = 0.86) and psychosocial (AUC = 0.84) performing better than the neural model (AUC = 0.64). Two fMRI-based models predicted binge drinking status better than chance, corresponding to the social and language tasks. Models developed with psychosocial and neural variables could contribute as diagnostic tools to help classify risky drinkers. Since social and language fMRI tasks performed best among the neural discriminators (including those from gambling and emotion tasks), it suggests the involvement of a broader range of brain processes than those traditionally associated with binge drinking in young adults.

Figure 1.. Schematic of our analytic approach.

Data were comprised of fMRI data from seven tasks, neuroanatomical data, and a range of data assessing personality, cognition, history, and more. The sample was divided into a training and test sample. Models were generated in the training sample using random forest, radial support vector machine, and elastic net algorithms. Tenfold cross-validation was repeated 20 times and was optimized on model parameters to maximize area under the receiver operating characteristic curve. The best performing model from each algorithm was used to generate an ensemble model using general linear regression, and this was also repeated with tenfold cross-validation. The ensemble was applied to the test sample. The performance in the test sample was assessed by the area under the receiver operating characteristic curve.

Figure 2.. Group differences.

Effect sizes (Cohen’s d) for the two sample t-tests comparing binge drinkers (n=177) with non-binge drinkers (n=309). Panel A presents psychosocial variables and panel B presents neural variables. Panel B is color-coded to highlight the brain lobes where differences exist. Positive values for d indicate significantly higher values in the binge drinking group relative to controls. The dashed line represent p-value < 0.05 using false discovery rate correction for multiple comparisons.

Figure 3.. Performance of Neuropsychosocial, Psychosocial, and Neural Models.

Panel A depicts the receiver operating characteristic plot for the ensemble models generated from each data source when applied to the test sample. Panel B depicts the area under the curve for the plots and the error bars represent the 95% confidence interval. Panel B also includes the psychosocial datasets that excluded substance use history to determine how much influence it had on model performance. The dashed line represents chance performance, so if the error bars do not contain the dashed line, they can be considered to perform better than chance with a probability of p < 0.05.

Figure 4.. Performance of Language, Motor, and Social fMRI models.

Panel A depicts the receiver operating characteristic plot for the ensemble models generated from each data source when applied to the test sample. Panel B depicts the area under the curve for the plots and the error bars represent the 95% confidence interval. The dashed line represents chance performance, so if the error bars do not contain the dashed line, they can be considered to perform better than chance with a probability of p < 0.05.

Figure 5.. Neural Contributions to Classification.

Variable importance scores were generated for the models of the social and language fMRI tasks. Each region received a score. Higher scores indicate greater importance in the model’s classification scheme. Brighter colors depict greater variable importance, with yellow being the most important. Panel A depicts scores from the social task and Panel B depicts scores from the language task. Approximately 10 regions per task contributed to classification, suggesting that neural models of risky drinking may need to incorporate complex models to characterize substance use problems accurately.