Automated Hotspot Search (AHS): A Gaussian Process-Bayesian Optimization (GP-BO) Approach for Precise and Efficient Robotic TMS Motor Hotspot Localization.

OBJECTIVE: Precise localization of motor hotspots is critical for the therapeutic efficacy of transcranial magnetic stimulation (TMS). However, traditional manual methods are highly subjective and time-consuming, while existing automated approaches often struggle to balance efficiency and accuracy. This study aims to develop and validate an Automated Hotspot Search (AHS) algorithm to address these limitations. METHODS: The AHS algorithm integrates robotic TMS with a Gaussian Process-based Bayesian Optimization (GP-BO) framework. Unlike exhaustive search approaches, AHS employs novel heuristics and a dynamic acquisition function to efficiently model cortical excitability, ensuring rapid convergence with minimal pulses. In an intra-subject study (n=11), we compared AHS with manual hotspot search (MHS) and semi-automatic hotspot search (SAHS) in terms of localization error, operational time, and physiological outcome (resting motor threshold, RMT). RESULTS: AHS demonstrated significantly reduced localization error (6.59 ± 2.06 mm, p < 0.01) compared to MHS and SAHS. AHS search time (226.55 ± 47.52 s) was reduced by 37% and 61% relative to MHS and SAHS, respectively, exhibiting superior operational stability (p < 0.001). Furthermore, the RMT determined by AHS (57.00 ± 9.10% MSO) was significantly lower than that of MHS (61.36 ± 9.06% MSO, p = 0.009). A significant correlation was also found between localization error and RMT. CONCLUSION: The AHS algorithm provides a more precise, efficient, and physiologically valid solution for motor hotspot localization. SIGNIFICANCE: This work provides an objective, data-driven tool that overcomes the subjectivity and inefficiency of manual operations, thereby enhancing the rigor and reproducibility of TMS applications.