Integrating brain, neurocognitive, and computational tools in Opioid Use Disorder (OUD) to characterize executive function and to predict clinical outcomes

整合阿片类药物使用障碍 (OUD) 中的大脑、神经认知和计算工具来表征执行功能并预测临床结果

• 批准号: 10506495
• 负责人: Paul Regier
• 金额: $ 17.82万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10506495
• 关键词: Accounting; Adherence; Anxiety; Area; Attention; Behavior; Behavioral; Brain; Brain Injuries; Brain imaging; Classification; Clinical; Cocaine use disorder; Cognitive; Complex; Computational Technique; Data; Decision Making; Development; Devices; Diagnostic; Drug usage; Executive Dysfunction; Exhibits; Foundations; Future; Goals; Gur; Harm Reduction; Imaging Device; Imaging Techniques; Imaging technology; Individual; Knowledge; Lead; Life; Linear Regressions; Measures; Medical; Mental Depression; Mental Health; Mentors; Modeling; Neurocognition; Neurocognitive; Neurosciences; Opioid; Outcome; Outpatients; Overdose; Overdose reversal; Participant; Patients; Pattern; Performance; Persons; Pharmaceutical Preparations; Population; Prior Therapy; Public Health; Recovery; Recovery Support; Regulation; Relapse; Reporting; Research; Research Project Grants; Risk; Scientist; Sexual abuse; Shapes; Short-Term Memory; Site; Source; Subgroup; Suboxone; Surveys; Testing; Training; Treatment outcome; addiction; biomarker selection; career; career development; comorbidity; computerized; computerized tools; cost effective; cue reactivity; deep learning; deep learning model; deep neural network; depressive symptoms; design; effective therapy; executive function; experience; flexibility; functional near infrared spectroscopy; improved; improved outcome; innovation; medication compliance; medication nonadherence; medication-assisted treatment; multitask; neglect; neural; neural correlate; neuroimaging; novel; opioid epidemic; opioid exposure; opioid mortality; opioid overdose; opioid use disorder; outcome prediction; overdose death; parent grant; portability; pre-clinical; predict clinical outcome; recruit; relapse prevention; relapse risk; spatiotemporal; statistics; tool; treatment adherence

Project Summary. The 5-year K01 Mentored Research Scientist proposal will employ brain, neurocognitive, and computational tools (e.g., deep 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 (fMRI) techniques). Mentor Dr. Childress will guide career development, and will coordinate training and individualized mentoring from a group of top-tier experts centered around 4 areas: Training Aim 1) opioid use disorder (OUD), its treatments, and comorbidities (Dr. Kampman, mentor), Training Aim 2) neurocognition (Dr. Gur, mentor), the impact of mental health, and its relationship to clinical outcomes, Training Aim 3) functional near-infrared spectroscopy (fNIRS), a mobile, non- invasive cortical brain imaging technology (Dr. Ayaz, Mentor), and Training Aim 4) advanced computational techniques (deep learning; Drs. Ayaz and Curtin) in outcome prediction. The training aims will be enabled by the Research Project Aims. Research Aim 1 (Conventional Approach): Examine differences between OUD vs HC on EF scores and PFC activity during EF tasks (Aim 1a); Using step-wise regression, examine relationship of brain (PFC) data and/or co-occurring variables with EF (Aim 1b) and clinical outcomes (Aim 1c). Research Aim 2 (Deep Learning): Examine whether multi-task, spatiotemporal brain data can distinguish OUD from HCs (Aim 2a). Within the OUD population, examine whether multi-task, spatiotemporal brain data can classify better or worse EF (Aim 2b) and/or drug-use outcome groups (Aim 2c). Exploratory: Add co-occurring variables into the deep learning pipeline to determine whether they improve classification of either EF and/or drug-use outcomes. The proposed K01 will facilitate Dr. Regier's transition to an independent research career focused on brain- behavioral vulnerabilities in relapse and recovery. It will also provide much-needed knowledge about neurocognition and its neural correlates and co-occurring contributors to relapse risk in those struggling toward recovery.

项目总结。为期5年的K01指导研究科学家提案将采用大脑，神经认知，