Neuropsychosocial markers of binge drinking in young adults

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.

Automated summary

This study investigated whether neural processes apart from reward contribute to predicting risky drinking behaviors, using machine-learning algorithms to classify participants based on psychosocial, neural, or both data. The neuropsychosocial and psychosocial models outperformed the neural model in the test sample, with fMRI tasks related to social and language showing better prediction of binge drinking status.