Listening to the Data: Computational Approaches to Addiction and Learning

Author:

Wilkinson Courtney S.ORCID,Luján Miguel Á.,Hales Claire,Costa Kauê M.ORCID,Fiore Vincenzo G.ORCID,Knackstedt Lori A.,Kober Hedy

Abstract

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.

Funder

HHS | NIH | National Institute on Drug Abuse

Publisher

Society for Neuroscience

Subject

General Neuroscience

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