Machine learning-based closed-loop rTMS of brain networks

In this project, 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 enhancement of the 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.

Project contributors: Dania Humaidan and Jiahua Xu

Funding: European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ConnectToBrain, ERC synergy grant agreement No. 810377)