Real-time fMRI neurofeedback of large-scale network dynamics in opioid use disorder

阿片类药物使用障碍大规模网络动态的实时功能磁共振成像神经反馈

• 批准号: 10025590
• 负责人: Kathleen A. GARRISON
• 金额: $ 20.94万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10025590
• 关键词: Abstinence; Alcohol or Other Drugs use; Attention; Behavior; Behavioral; Brain; Brain region; Characteristics; Clinical; Cognitive; Complex; Data; Data Analytics; Dose; Feedback; Functional Magnetic Resonance Imaging; Government; Individual; Intervention; Learning; Link; Machine Learning; Measures; Methadone; Methods; Modeling; Neurobiology; Opiate Addiction; Opioid; Outcome; Pathway interactions; Pattern; Population; Public Health; Randomized; Refractory; Relapse; Reporting; Research; Rest; Rewards; Running; Scanning; Signal Transduction; Symptoms; Testing; Therapeutic; Time; Training; Work; addiction; base; brain behavior; cognitive process; cognitive task; connectome; craving; design; effective therapy; follow-up; high risk; imaging study; improved; improved outcome; innovation; insight; methadone treatment; negative affect; neural network; neural patterning; neurofeedback; novel; opioid epidemic; opioid misuse; opioid overdose; opioid use; opioid use disorder; overdose risk; prescription opioid; relating to nervous system; response; therapy development; tool

PROJECT SUMMARY The misuse of opioids, opioid addiction and overdose are a serious national public health crisis—the opioid epidemic—that despite increased scientific, clinical and government attention, continues to grow. Methadone is a generally effective treatment for opioid use disorder, however relapse rates remain high, and risk of overdose is greatest during relapse. There is a need for improved mechanistic understanding of the factors that contribute to opioid relapse to improve our understanding of opioid use disorder and its treatment. Using connectome-based methods (i.e., functional connectivity) in functional magnetic resonance imaging (fMRI), we recently identified a large-scale brain network that predicted opioid relapse from both resting and task states. Connectome-based methods enable data-driven characterization of whole brain networks related to behavior that might be better suited to describe complex clinical phenomena (e.g., opioid relapse). Building on prior work indicating the utility of real-time fMRI neurofeedback to test brain activation patterns related to specific functions and individual abilities to regulate these functions, the proposed project will use connectome-based neurofeedback to target patterns of functional connectivity within our recently identified “opioid abstinence network”. This information is critical to improve understanding of mechanisms of opioid relapse. Individuals on methadone will be randomized to receive either active (n=12) or sham (n=12) connectome-based neurofeedback at 3 weekly scanning sessions including feedback and transfer runs. Additional baseline and follow-up scans will include resting state and reward and cognitive task runs. Craving, negative affect and opioid use will be measured weekly and at 1-mo follow-up. Based on our pilot data, connectome-based feedback will be targeted at the opioid abstinence network and we hypothesize that increased connectivity in this network will be associated with improved clinical outcomes. Aim 1 will test the hypothesis that active feedback is associated with reduced opioid use from baseline to follow-up scans (Aim 1a) and at 1-mo follow- up (Aim 1b). Aim 2 will test the hypothesis that active feedback is associated with increased opioid abstinence network connectivity in resting state (Aim 2a) and task (reward, cognitive) state (Aim 2b) versus sham feedback, as in our pilot work. Aim 3 will test the hypothesis that active feedback is associated with greater improvements in clinical features of opioid use disorder (craving, negative affect) than sham feedback (Aim 3a) and that increased opioid abstinence network connectivity will correlate with these improvements (Aim 3b). Overall, this project tests a potentially transformative hypothesis relating large-scale brain network dynamics to outcomes in opioid use disorder, and tests a highly innovative method for real-time fMRI neurofeedback from the opioid abstinence network to improve clinical features of opioid use disorder. This project will provide unprecedented insight into the functional neurobiology of opioid relapse and more generally has the potential to transform existing real-time fMRI paradigms in addictions.

项目摘要 阿片类药物的滥用、阿片类药物成瘾和过量是一个严重的国家公共卫生危机--阿片类药物 尽管科学，临床和政府的关注不断增加，但仍在继续增长。美沙酮是 阿片类药物使用障碍的普遍有效治疗，但复发率仍然很高， 在复发的时候是最大的有必要从机制上更好地理解 有助于阿片类药物复吸，以提高我们对阿片类药物使用障碍及其治疗的理解。使用 基于连接体的方法（即，功能性连接）在功能性磁共振成像（fMRI），我们 最近确定了一个大规模的大脑网络，可以从休息和任务状态预测阿片类药物的复发。 基于连接组的方法使与行为相关的全脑网络的数据驱动表征成为可能 可能更适合于描述复杂的临床现象（例如，阿片类药物复发）。基于先前的 这项工作表明了实时功能磁共振成像神经反馈的实用性，以测试与特定功能相关的大脑激活模式。 功能和个人的能力来调节这些功能，拟议的项目将使用基于连接体 我们最近发现的“阿片类药物戒断”中， 网络”。这些信息对于提高对阿片类药物复吸机制的理解至关重要。上的个人 美沙酮将随机接受活性（n=12）或假（n=12）基于连接体的 每周3次扫描会议的神经反馈，包括反馈和转移运行。额外基线和 后续扫描将包括静息状态和奖励以及认知任务运行。渴望，负面影响， 阿片类药物的使用将每周和1个月随访时进行测量。根据我们的试点数据，基于连接组的 反馈将针对阿片类药物戒断网络，我们假设， 该网络将与改善的临床结果相关联。目标1将检验以下假设： 反馈与从基线到随访扫描（Aim 1a）和1个月随访时阿片类药物使用减少相关。 向上（目标1b）。目标2将检验主动反馈与阿片类药物戒断增加相关的假设 静息状态（Aim 2a）和任务（奖励，认知）状态（Aim 2b）与假手术的网络连接 反馈，就像我们的试点工作一样。目标3将检验主动反馈与更大的 与假反馈相比，阿片类药物使用障碍的临床特征（渴求、负面影响）得到改善（目标3a） 阿片类戒断网络连接的增加将与这些改善相关（目标3b）。 总的来说，该项目测试了一个潜在的变革性假设，该假设将大规模大脑网络动力学与 阿片类药物使用障碍的结果，并测试了一种高度创新的实时fMRI神经反馈方法， 阿片类药物戒断网络，以改善阿片类药物使用障碍的临床特征。本项目将提供 对阿片类药物复发的功能性神经生物学的前所未有的洞察，更普遍地说， 来改变现有的成瘾症的实时功能磁共振成像模式。