Dustin Scheinost - US grants

Project Summary (Abstract) A primary challenge facing functional neuroimaging is the translation of research findings to the clinical setting. In To predictive part, fMRI has struggled as a clinical tool due to the lack of functional phenotypes that characterize patients. address this, we have developed connectome-based predictive modeling (CPM) to identify and validate models of behavior/symptoms based on functional connectivity data. The promise of this approach is that by developing predictive models based on the functional organization of an individual's brain, we may be able to extract a rich connectivity phenotypes to aid in the clinical characterization of patients. This approach has the potential to improve our ability to categorize patients in otherwise heterogeneous groups and monitor the effectiveness of treatment interventions. To do this, modeling generalize methods across multiple behaviors, symptoms and diagnostic groups. are needed that are designed to In this proposal, we will push forward several major developments in CPM focused on generating transdiagnostic models for three specific behaviors (attention, working memory, and fluid intelligence) and factors from clinical tests, that will lead to functional phenotypes. We will collect a battery of continuous performance tasks in a spectrum of (N=300) individuals. We propose three specific aims: (1) To characterize node-boundary x dimensional construct effects; (2) To preform unidimensional and multi-dimensional CPM to predict RDoC constructs; (3) To evaluate the extent to which subjects with similar functional phenotypes cluster into symptom based or DSM-5 categorical clusters. This aim will also allow us to investigate the functional networks that vary with symptom and to investigate categorical subtleties in these symptom based phenotypes. The significance of transdiagnostic predictive models of behavior from functional connectivity data lay in their ability to delineate clinically relevant information from any individual (i.e. patient or control). The current lack of transdiagnostic predictive limits implications collection the clinical utility of f MRI in translating fMRI into a viable clinical tool. T of a novel trans-diagnostic data set to be made , providing a framework for, and generating, these models could models have important he innovation of this proposal is fourfold: 1) the publicly available ; 2) the development of an approach to generate personalized functional atlases to account for individual differences in anatomy; 3) the development of methods to delineate meaningful functional phenotypes to assess symptoms, and 4) to provide a means for comparing alignment of subjects on symptom dimensions versus DSM-5 categories using these functional phenotypes. These developments will be validated using a combination of novel data to be collected here as well as 3 publicly available data sets. The final deliverables will yield tools for measuring functional phenotypes reflecting symptom scores suitable for an individualized approach to medicine.

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.