Connectome-Based Prediction of Addiction Severity

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7. Abstract The United States is currently facing an epidemic of fatalities associated with the use of multiple types of substances. Given this public health crisis, there is an urgent need to identify transdiagnostic, dimensional biomarkers of substance use disorders (SUDs). In the field of clinical neuroscience, traditional, group-level functional magnetic resonance imaging (fMRI) approaches have struggled to identify predictive biomarkers of addiction that replicate across samples, possibly due to methods that (a) overfit the data and (b) examine different SUDs in isolation. To address this, the first objective of this application is to leverage recent advances in connectome-based predictive modeling (CPM), a data-driven machine-learning approach, to identify patterns of functional connectivity (“neural fingerprints”) that are predictive of a transdiagnostic, dimensional measure of SUD severity at the individual level. Further, as opposed to single-modality research, NIDA has outlined the need to examine the complex interactions of factors across biological, psychological, and environmental domains (Priority Focus #1) in order to understand the real-word complexity of SUD vulnerability (Priority Focus #3). In line with these priorities, the second objective of this application is to integrate SUD-related whole-brain metrics into multilevel models of addiction vulnerability that include well-established psychological and environmental risk factors. The proposed research will make use of archival data collected as part of two independent studies (N = 242). Clinical assessments, self-report data, and resting-state fMRI data were collected from a diverse sample of community adults, elevated on illicit drug use, with 43% meeting criteria for a lifetime SUD and 81% reporting a history of illicit drug use. We hypothesize that a CPM-derived neural network will emerge in relation to SUD severity in the training sample that can be used to accurately predict SUD severity in another independent sample (Aim #1). This is expected to produce a working neural model that can be further tested and applied to make highly-individualized predictions of SUD severity in other, unseen samples. Next, we hypothesize that these networks will contribute unique variance to a multi-faceted model of addiction vulnerability, above and beyond well-established psychological and environmental risk factors (Aim #2). The integration of knowledge across multiple domains makes this application well-situated to address the real-world complexity of SUDs, and thus presents an opportunity for significant advances towards the development of precision medicine methods. This F31 application will provide opportunities for the applicant’s training in two critical areas: (a) the technical skills necessary to implement machine learning approaches to isolate brain networks with predictive power to identify individual differences in SUD severity and (b) the conceptualization and design of multi-faceted models that incorporate factors from multiple domains of analysis to characterize addiction vulnerability.