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.

项目摘要。为期5年的K 01指导研究科学家提案将采用大脑，神经认知， 和计算工具（例如，深度学习）以了解阿片类药物使用障碍（OUD）的影响， 关于执行功能和临床结局的常见并发问题。有创纪录数量的 过去12个月内与阿片类药物（和其他药物）相关的致命和非致命过量（OD）。改善 加强治疗和预防复发的分类和预测能力是最重要的。 神经认知缺陷通常与不良治疗结果相关（例如，更多的药物使用， 不遵守），但与OUD相关联的共同发生的问题（例如，抑郁、焦虑、身体/性虐待， 忽视）使得难以分析哪些促成因素导致执行功能（EF）更差， 治疗结果。需要新的大脑、神经认知和计算工具来帮助确定这些 差异，以便为更好的治疗奠定基础。这一需求塑造了培训计划， 在5年K 01指导研究科学家提案中的相关研究项目，建立在Regier博士的 先前的临床前和临床成瘾神经科学经验（主要集中在可卡因使用障碍，提示， 反应性、皮层下网络、先前逆境和单变量成像（fMRI）技术）。导师奇尔德里斯博士 将指导职业发展，并将协调培训和个性化的指导，从一组顶级 专家围绕4个领域：培训目标1）阿片类药物使用障碍（OUD），其治疗和合并症 (Dr. Kampman，导师），培训目标2）神经认知（古尔博士，导师），心理健康的影响，及其 与临床结果的关系，培训目标3）功能性近红外光谱（fNIRS），一种移动的， 侵入性大脑皮层成像技术（Ayaz博士，导师），和培训目标4）先进的计算 技术（深度学习; Ayaz和科廷博士）在结果预测。培训目标将通过 研究项目目标。研究目标1（传统方法）：检查OUD与HC之间的差异 在EF任务期间（目标1a），EF分数和PFC活动;使用逐步回归，检查EF分数和PFC活动之间的关系。 脑（PFC）数据和/或与EF（目标1b）和临床结局（目标1c）共现的变量。研究目标 2（深度学习）：检查多任务，时空大脑数据是否可以区分OUD和HC（Aim 2a）。在OUD人群中，检查多任务，时空大脑数据是否可以更好地分类， EF更差（目标2b）和/或药物使用结局组（目标2c）。探索性：将共现变量添加到 深度学习管道，以确定它们是否改善了EF和/或药物使用结果的分类。 拟议中的K 01将促进Regier博士向专注于大脑的独立研究事业的过渡， 在复发和恢复中的行为脆弱性。它还将提供急需的知识， 神经认知及其神经相关性和共同发生的贡献者复发的风险， 复苏