BaMS3: Bayesian motor mapping with structured inference for anatomical precision.

Abstract

Transcranial magnetic stimulation (TMS)-based motor mapping is a key method for non-
invasively localizing cortical muscle representations from motor evoked potentials (MEPs). However, con-
ventional vertex-wise R2-mapping approaches treat cortical locations independently and do not account for
spatial interactions, non-linear recruitment dynamics, or variable trial-to-trial noise, limiting spatial precision
particularly in anatomically complex and folded cortex.
Objective. We present BaMS3 (Bayesian Motor-Mapping with 3-Stage Structured Fitting), a probabilis-
tic framework that jointly models spatial sensitivity, nonlinear recruitment dynamics, and variable noise in
TMS-based motor mapping.
Methods. BaMS3 incorporates anatomical and physiological determinants of MEP generation by model-
ing spatial dependencies across cortical locations, nonlinear input-output relationships, and location-specific
variability. The framework was evaluated using subject-specific synthetic simulations and empirical datasets
from eight healthy participants and compared with conventional R2-based mapping in terms of hotspot
localization accuracy and motor map focality.
Results. BaMS3 consistently outperforms conventional R2-based mapping, yielding more anatomically
precise and spatially focal motor maps while preserving canonical hotspot localization in empirical datasets.
The largest improvements were observed in anatomically complex cortical regions, including sulcal walls and
cortical folds.
Conclusions. By modeling TMS motor mapping as a probabilistic inference problem, BaMS3 provides a
principled approach for individualized functional brain mapping and may improve spatial target definition
for causal brain mapping, cognitive neuroscience, and preoperative functional localization.