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

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.

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).

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.

The AHS algorithm provides a more precise, efficient, and physiologically valid solution for motor hotspot localization.

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.