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.
This project leverages advances in real-time fMRI neurofeedback and machine learning to test whether methadone-treated individuals with opioid use disorder can increase functional connectivity within an empirically-derived ?opioid abstinence network? that has previously been identified to predict opioid relapse. This project will provide critical data and information to improve our understanding of opioid relapse and may transform existing real-time fMRI paradigms in addictions more generally. Findings should contribute to the scientific and clinical response to the growing opioid epidemic and be used to inform treatment development.
