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

项目总结 阿片类药物滥用、阿片成瘾和过量是严重的国家公共卫生危机--阿片类药物 流行病--尽管增加了科学、临床和政府的关注，但仍在继续增长。美沙酮是 阿片类药物使用障碍的一般有效治疗，但复发率仍然很高，并有过量服药的风险 在复发期间是最好的。有必要改进对以下因素的机械性理解 促进阿片类药物复发，提高我们对阿片类药物使用障碍及其治疗的认识。vbl.使用 基于连接组的方法(即功能连接性)在功能磁共振成像(FMRI)中，我们 最近发现了一个大规模的大脑网络，可以预测阿片类药物在休息和任务状态下的复发。 基于连接组的方法能够对与行为相关的整个大脑网络进行数据驱动的表征 这可能更适合描述复杂的临床现象(例如，阿片类药物复发)。在以前的基础上构建 表明实时功能磁共振神经反馈用于测试与特定脑区相关的脑激活模式的研究 功能和个体能力来调节这些功能，建议的项目将使用基于连接体的 神经反馈对我们新近发现的“阿片类药物戒断”中的功能连接的靶模式 网络“。这一信息对于增进对阿片类药物复发机制的了解至关重要。个人上的 美沙酮将被随机接受基于连接组的活性(n=12)或假(n=12) 每周3次扫描时的神经反馈，包括反馈和传输运行。其他基线和 后续扫描将包括休息状态、奖励和认知任务运行。渴求、负面情绪和 阿片类药物的使用将每周测量一次，并进行1个月的随访。基于我们的试点数据，基于Connectome 反馈将针对阿片类药物戒断网络，我们假设在 这一网络将与改善临床结果相关。目标1将检验这一假设 反馈与减少阿片类药物的使用有关，从基线到后续扫描(目标1a)和在1个月的后续扫描- 向上(目标1b)。目标2将验证主动反馈与阿片类药物戒断增加相关的假设 休息状态(目标2a)和任务(奖励、认知)状态(目标2b)与假状态下的网络连接 反馈，就像我们的试点工作一样。目标3将检验这一假设，即主动反馈与更大的 与假反馈相比，阿片类药物使用障碍(渴望、消极情绪)临床特征的改善(目标3a) 阿片类药物戒断网络连接的增加将与这些改善相关(目标3b)。 总体而言，该项目测试了一个潜在的变革性假说，该假说将大规模脑网络动力学与 阿片类药物使用障碍的结果，并测试了一种高度创新的方法，用于从 阿片类药物戒断网络改善阿片类药物使用障碍的临床特征。该项目将提供 对阿片类药物复发的功能神经生物学的前所未有的洞察力，更普遍地具有潜在的 改变现有的成瘾实时功能磁共振成像范式。

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.