Optimizing Accelerated TMS for Chronic Pain With Thompson Sampling

The purpose of this study is to evaluate the safety and efficacy of accelerated intermittent theta burst stimulation (aiTBS) targeting personalized nodes within the somato-cognitive action network (SCAN) and action motor network (AMN) for the treatment of chronic pain. The study will employ Thompson Sampling, a Bayesian reinforcement learning algorithm, to optimize stimulation site selection based on individual response patterns. This approach has the potential to revolutionize pain management by improving treatment accessibility through shortened timelines, addressing individual variations in pain networks through precision targeting, and potentially achieving more robust pain relief through accelerated neuroplasticity. The specific aims of the study are: 1. To establish the relationship between SCAN and AMN connectivity and cognitive- affective pain symptoms. 2. To evaluate the efficacy of Thompson Sampling-optimized aiTBS across SCAN and AMN on affective and sensorimotor pain dimensions. 3. To identify fMRI connectivity biomarkers predictive of treatment response and remission.