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Related Experiment Video

Updated: Jan 16, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Complexity of Resting Cortical Activity Predicts Neurophysiological Responses to Theta- Burst Stimulation but Fails

Matthew Ning1, Haoqi Sun1, Brice Passera1

  • 1Beth Israel Deaconess Medical Center.

Research Square
|October 3, 2025
PubMed
Summary

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Age-related differences in default-mode network connectivity in response to intermittent theta-burst stimulation and its relationships with maintained cognition and brain integrity in healthy aging.

NeuroImage·2018

Predicting brain responses to intermittent theta-burst stimulation (iTBS) is challenging due to individual variability. Resting-state EEG complexity shows some predictive power, but single-session iTBS effects are unstable, limiting model generalizability.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Neuromodulation

Background:

  • Individual responses to intermittent theta-burst stimulation (iTBS) vary significantly, hindering clinical application.
  • Neurophysiological predictors for this variability are poorly understood.
  • Previous machine-learning studies often used limited features and lacked independent validation.

Purpose of the Study:

  • To identify neurophysiological predictors of iTBS response using resting-state EEG and TMS-evoked measures.
  • To assess the generalizability of machine-learning models in predicting neurophysiological outcomes.
  • To investigate the impact of test-retest variability on predictive model performance.

Main Methods:

  • Statistical and reliability analyses of resting-state EEG (rsEEG) and iTBS response.
Keywords:
MEPMachine LearningTEPTMS-EEGTest-Retest iTBSiTBS

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Last Updated: Jan 16, 2026

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  • Supervised machine learning models integrating rsEEG features, motor-evoked potentials (MEPs), and TMS-evoked potentials (TEPs).
  • Prediction of neurophysiological responses to iTBS over the primary motor cortex in two independent studies.

    • Main Results:

    • Internal cross-validation achieved 81% accuracy, identifying rsEEG multiscale entropy as a predictor of TEP changes (100-131ms).
    • External validation accuracy dropped to 69%, indicating unstable predictor-outcome relationships.
    • High intra- and inter-individual variability in iTBS effects was observed.

    Conclusions:

    • EEG complexity measures offer partial insight into baseline brain states for neuromodulation.
    • The instability of single-session iTBS effects limits model generalizability.
    • Test-retest paradigms are crucial for accurate performance estimation; future research needs multi-session protocols.