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- W4320908413 abstract "Abstract TMS has been extensively used to investigate the physiology of the central nervous system in health and disease. However, there is a strong need for a personalized, multi-locus, real-time stimulation procedure, which can be adaptively fine-tuned based on case-specific feedback. Here, we modulate the effective connectivity of the 2-node brain network from supplementary motor area (SMA) to primary motor cortex (M1) by closed-loop stimulation, optimized by application of an online reinforcement learning algorithm. This algorithm learns to identify the individually optimal phase of the ongoing μ-rhythm to be targeted by paired SMA-M1 TMS for maximized long-term enhancement of facilitatory effective connectivity between SMA and M1. This is one of the first demonstrations of true closed-loop stimulation, a crucially important step towards individualized highly-effective brain stimulation for therapeutic modulation of dysfunctional brain networks, e.g., the deficient SMA-M1 connection in motor stroke. Research Category and Technology and Methods Translational Research: 7. Responsive (Closed-Loop) Stimulation Keywords: EEG-TMS, Machine Learning, Closed-loop stimulation, Motor cortex" @default.
- W4320908413 created "2023-02-16" @default.
- W4320908413 creator A5000920105 @default.
- W4320908413 date "2023-01-01" @default.
- W4320908413 modified "2023-09-23" @default.
- W4320908413 title "Enhancing the connectivity in a 2-node motor network using reinforcement learning-based closed-loop rTMS" @default.
- W4320908413 doi "https://doi.org/10.1016/j.brs.2023.01.024" @default.
- W4320908413 hasPublicationYear "2023" @default.
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