Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.

ABSTRACT:

Background

Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed.

Methods

As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants compl

SUBMITTER: Khatri UU 

PROVIDER: S-EPMC11370398 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature