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Updated: Jun 14, 2025
Corticospinal Excitability Modulation During Action Observation
Published on: December 31, 2013
Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time
Uttara U Khatri1, Kristen Pulliam1, Muskan Manesiya1
1Movement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
This study developed a machine learning system to personalize brain state-dependent transcranial magnetic stimulation (TMS) for stroke recovery. The system accurately predicts corticospinal tract (CST) states in real-time, paving the way for more effective motor function treatments.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Science
Background:
- Transcranial magnetic stimulation (TMS) shows potential for treating stroke-related motor impairments, but its effectiveness is highly variable.
- Brain state-dependent TMS offers a promising approach, yet challenges remain due to individual differences in lesion location and brain activity.
- Personalized strategies are needed to optimize brain state-dependent TMS for post-stroke motor recovery.
Purpose of the Study:
- To develop and test a novel machine learning-based system for real-time identification of personalized brain activity patterns related to corticospinal tract (CST) output.
- To investigate the feasibility of using these personalized brain states to guide single-pulse TMS.
Main Methods:
- A machine learning-based EEG-TMS system was developed to identify personalized strong and weak CST states in real-time.
- Participants underwent a single-session study involving TMS-EEG-EMG data acquisition, personalized classifier training, and real-time EEG-informed TMS.
- Single-pulse TMS was delivered during classifier-predicted personalized CST states.
Main Results:
- Motor evoked potential (MEP) amplitudes were significantly larger and less variable during real-time personalized strong CST states compared to weak or random states.
- Personalized CST states were identified in real-time, lasting approximately 1-2 seconds each.
- Unique spectro-spatial EEG patterns differentiated personalized strong and weak CST states across individuals.
Conclusions:
- Personalized whole-brain EEG activity patterns can predict CST activation in real-time in healthy individuals.
- This study represents a significant advancement towards personalized, brain state-dependent TMS interventions for enhancing post-stroke CST function.
- The findings support the development of tailored TMS therapies to improve motor recovery after stroke.
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