Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.

ABSTRACT: Transcranial magnetic stimulation (TMS) has emerged as a promising neuromodulation technique with both therapeutic and diagnostic applications. As accurate coil placement is known to be essential for focal stimulation, computational models have been established to help find the optimal coil positioning by maximizing electric fields at the cortical target. While these numerical simulations provide realistic and subject-specific field distributions, they are computationally demanding, precluding their use in real-time applications. In this paper, we developed a novel multi-task deep neural network which simultaneously predicts the optimal coil placement for a given cortical target as well as the associated TMS-induced electric field. Trained on large amounts of preceding numerical optimizati

SUBMITTER: Moser P 

PROVIDER: S-EPMC11339299 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature