Substance use and substance use disorder are leading causes of death and disability worldwide. Importantly, most adults with substance use disorder begin using substances as adolescents, making adolescence an important period for the development of substance use disorder. Thus, it is critical to identify factors related to substance use among adolescents, consistent with NIDA objective 1.1. One factor linked to adolescent substance use is negative emotion processing. As adolescents undergo significant biological and psychosocial changes across development they may experience altered negative emotion processing that can lead to substance use if not appropriately regulated. Unfortunately, there is limited understanding in how neural level differences in negative emotion processing is related to substance use among adolescents. Moreover, extant neuroimaging research on negative emotion processing and substance use has employed univariate methods instead of multivariate methods. In contrast to univariate methods, multivariate methods are more sensitive in detecting patterns of neural activation across voxels and yield more generalizable and replicable findings overall. In addition, this research has employed standardized negative emotion paradigms, which have high experimental control, but may be less likely to reflect negative emotion processing as it occurs in the real world. To address these gaps in the literature, the proposed study will use multivariate machine learning approaches (i.e., multivoxel pattern analysis) to classify patterns of neural activation in a standardized negative emotion processing task and in a novel naturalistic negative emotion processing task that differentiate substance using adolescents from non-using adolescents, as well as predict substance use disorder risk factors. Additionally, the proposed study will use machine learning approaches to examine sex differences in emotion-related neural activation to these tasks in relation to substance use. This research will be conducted on a sample of 326 12-13 year old adolescents from Sponsor?s (Chaplin) competing renewal grant (RO1 DA033431-06A1). Knowledge from the proposed study will be used to identify emotion-related neurobiological markers of adolescent substance use. Ultimately, these markers can be used to target at-risk adolescents in need of substance use prevention and intervention efforts. The goals of the proposed study will be accomplished within a research training plan aimed at developing multidisciplinary expertise in affective neuroscience, particularly in multivariate machine learning methods, and developmental models of substance use. The training plan includes completion of relevant coursework, attendance at targeted workshops, individual mentorship by experts in the field of development and neuroscience, and scientific writing and presentation experience.
This project uses machine learning fMRI approaches to investigate patterns of neural activation during negative emotion processing to classify and predict adolescent substance use. The goal of this research is to identify emotion-related neurobiological markers of substance use. This knowledge will be used to target at-risk adolescents in need of substance use prevention and intervention efforts.
