経頭蓋磁気刺激の運動野標的の効率的な前向き電場情報局在化

Transcranial magnetic stimulation (TMS) is a versatile non-invasive tool for brain mapping and neuromodulation in both healthy individuals and patients. Effective TMS-based causal brain mapping relies on precise localization of cortical targets. Current state-of-the-art approaches use statistical methods to quantify the relationship between TMS-induced electric fields (E-fields) and motor evoked potential (MEP) amplitudes. However, this method typically relies on the random selection of coil configurations, which limits its efficacy. In this study, we present a novel optimization strategy for TMS-based motor mapping by prospectively selecting coil configurations based on their E-field characteristics using an iterative sampling algorithm called farthest point sampling (FPS). Through a combination of theoretical analysis, simulation and experimental validation, including 10 healthy individuals, we systematically evaluated the performance of FPS against the random sampling approach. Our results demonstrate that FPS (median: 49.5 trials) is twice as sample-efficient as random sampling (median: 91.0 trials) in reducing the number of trials required to estimate the motor map. Moreover, FPS shows greater consistency across participants, as reflected in the narrower confidence intervals. These findings highlight the potential of FPS to significantly enhance the sample-efficiency of motor mapping, paving the way for the development of more effective TMS mapping algorithms.