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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.

Brain Stimulation
|December 24, 2024
PubMed
Summary

This study developed a machine learning system to personalize brain activity patterns for transcranial magnetic stimulation (TMS). This approach enhances corticospinal tract (CST) activation, paving the way for improved stroke motor recovery.

Keywords:
Brain stimulationCorticospinal tractElectroencephalographyMachine learningMotor cortexTranscranial magnetic stimulation

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Area of Science:

  • Neuroscience
  • Rehabilitation Medicine
  • Biomedical Engineering

Background:

  • Transcranial magnetic stimulation (TMS) shows promise for stroke-related motor impairments, but treatment effects are inconsistent.
  • Brain state-dependent TMS is a potential solution, yet individual differences in stroke lesions and brain activity complicate its application.
  • Personalized approaches are crucial to overcome these challenges and optimize TMS efficacy in post-stroke patients.

Purpose of the Study:

  • To develop and validate a novel machine learning-based system for real-time identification of personalized brain activity patterns.
  • To investigate the ability of this system to predict corticospinal tract (CST) activation states (strong and weak) in real-time.
  • To assess the impact of real-time, personalized brain state-dependent TMS on motor evoked potential (MEP) amplitudes and variability.

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 involving TMS-EEG-EMG data acquisition, personalized classifier training, and real-time TMS guided by the classifier.
  • Single-pulse TMS was delivered during classifier-predicted personalized CST states (strong, weak, or random) in healthy adults.

Main Results:

  • Motor evoked potential (MEP) amplitudes were significantly larger during classifier-predicted strong CST states compared to weak and random states.
  • MEP amplitude variability was significantly reduced during strong CST states relative to weak CST states.
  • Personalized strong and weak CST states were identified in real-time, lasting approximately 1-2 seconds, with unique spectro-spatial EEG patterns differentiating them between individuals.

Conclusions:

  • This study demonstrates for the first time that personalized whole-brain EEG patterns can predict CST activation in real-time in humans.
  • These findings represent a significant advancement towards personalized, brain state-dependent TMS interventions for enhancing post-stroke motor function.
  • The developed system offers a pathway to more effective and targeted neuromodulation strategies for stroke rehabilitation.