Real-time posterior probabilistic TMS motor mapping with robust individualized stopping criteria

Background. Electric-field-informed regression provides high-resolution transcranial magnetic stimulation (TMS) motor maps, but conventional nonlinear fitting is too slow to update the map during acquisition, so sessions rely on predefined stimulation budgets, despite substantial inter-individual variability in the data required for stable localization. Objective. To develop a real-time analysis updating a posterior probability map between pulses and compare its hotspots with finger-tapping functional MRI (fMRI). Methods. We analyzed two independent cohorts (12 and 13 participants, one newly collected), analyzing each hand muscle independently. We replaced the conventional per-element sigmoid fit with Spearman's rank correlation coefficient and transformed the resulting score map into a posterior probability map using marginal-likelihood evidence. Acquisition stopped once posterior mass within 5 mm of the hotspot passed a preset confidence level. Results. The Spearman and linear scores updated the region of interest within milliseconds, versus tens of seconds for sigmoid fitting. Spearman more closely matched the full-data sigmoid hotspot in recorded sessions and converged faster and more precisely to the true source in virtual subjects. Across both fast scores, at a confidence level of 0.85, the stopping criterion was met after a median of 74-159 pulses, with 26-454 across individual problems. FDI hotspots lay 8.3 {+/-} 4.3 mm from the fMRI center of gravity, in the precentral hand region. Conclusions. Rank correlation reduces computation time without loss of accuracy relative to sigmoid fitting. The resulting posterior probability map provides a session-specific stopping criterion in place of a fixed pulse budget.