Precision Medicine for Chronic Pain: Using Brain Stimulation to Identify and Test Network Biomarkers for Pain Relief

PROJECT SUMMARY / ABSTRACT Chronic pain is the leading cause of disability worldwide, yet effective treatments remain elusive for most chronic pain syndromes. Chronic neuropathic pain, resulting from a lesion or disease affecting the nervous system, produces central sensitization and maladaptive plastic changes across distributed pain processing circuits. Consequently, there is an urgent need to understand how targeted brain stimulation can influence these widespread pain networks. My overarching career goal is to become a clinician-investigator with an independent computational neuroscience laboratory dedicated to studying relationships between brain network organization and chronic pain. I aim to leverage this knowledge to develop safer and more effective personalized treatments for chronic pain. The K23 award will provide critical support, mentorship, and training in 4 areas crucial to the success of my future independent research program, including: (1) advanced functional magnetic resonance imaging (fMRI), network neuroscience, and machine learning methods; (2) non-invasive brain stimulation with repetitive transcranial magnetic stimulation (rTMS); (3) clinical trial design, management, and statistics; and (4) experimental pain research. The proposed research applies this training to fMRI and multimodal pain outcomes data collected from ongoing trials of rTMS at the primary motor cortex (M1), provided as a therapy for chronic neuropathic pain. Although M1 is an established rTMS target for pain, clinical outcomes are variable and unpredictable. I hypothesize that chronic pain results from hyperconnected brain networks and that targeting highly connected network hubs within M1 can improve network function and reduce pain. To test this hypothesis, I will first perform a retrospective analysis of data from a pilot clinical trial comparing high frequency excitatory and low frequency inhibitory rTMS at M1 to examine the relationship between pain relief and the "hub-ness" of the stimulated target (Aim 1). I will then test whether pain-relieving high-frequency M1 rTMS reduces pathologically hyperconnected global brain networks (Aim 2). Finally, to prospectively validate network hub stimulation as a pain-relieving intervention, I propose a new, randomized, controlled cross-over trial comparing high frequency rTMS at personalized hubs versus non-hubs within M1 (Aim 3). Successful completion of this study will identify and prospectively validate regional and global network characteristics associated with pain relief from M1 rTMS. Planned analyses will establish tools and methods capable of predicting new, personalized rTMS targets in future patients with chronic pain. Results will be used in NIH R01 and HEAL grant applications for funding to support the ongoing development and implementation of personalized pain-relieving interventions, to provide much needed alternatives to opioid-based analgesia. Collectively, this training and research will position me as a global leader in the development of personalized, non-invasive, brain-based therapies for pain.