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Related Experiment Video

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Corticospinal Excitability Modulation During Action Observation
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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.

Biorxiv : the Preprint Server for Biology
|September 4, 2024
PubMed
Summary

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.

Keywords:
brain stimulationelectroencephalographymachine learningmotor cortextranscranial magnetic stimulation

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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.