Integrating Brain, Neurocognitive, and Computational Tools in OUD

The 5-year K01 Mentored Research Scientist proposal will employ brain, neurocognitive, and computational tools (e.g., machine learning) to understand the impact of opioid-use disorder (OUD) and common co-occurring issues on executive function and clinical outcomes. There have been record numbers of fatal and non-fatal overdoses (ODs) associated with opioids (and other drugs) in the past 12-months. Improving classification and predictive capabilities to enhance treatment and prevent relapse is of the upmost importance. Deficits in neurocognition often are associated with poor treatment outcomes (e.g., more drug use, medication non-adherence), yet co-occurring issues associated with OUD (e.g., depression, anxiety, physical/sexual abuse, neglect) make it difficult to parse which contributing factors lead to worse executive function (EF) and poorer treatment outcomes. Novel brain, neurocognitive, and computational tools are needed to help determine these differences, in order to lay the foundation for better treatments. This need has shaped both the training plan and the associated research project in a 5-year K01 Mentored Research Scientist proposal, building on Dr. Regier's prior preclinical and clinical addiction neuroscience experience (focused mostly on cocaine-use disorders, cue-reactivity, subcortical networks, prior adversity, and univariate imaging techniques).

This description comes directly from the study's public registry record.

Paul Regier, PhD  ·  215-746-3706  ·  paul.regier@pennmedicine.upenn.edu

Megan Ivey, MS  ·  215-746-7712  ·  megan.ivey@pennmedicine.upenn.edu

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