Exploratory decoding of TMS-EEG: Predicting TEP response to intermittent and continuous theta burst stimulation

Abstract

Theta burst stimulation (TBS) is a promising form of repetitive transcranial magnetic stimulation (rTMS) capable of modulating cortical excitability and intracortical processes, offering therapeutic potential for neurological and psychiatric disorders. However, clinical translation remains limited by high inter-subject and inter-session variability in stimulation effects. To more directly capture cortical responses, there is increased interest in combining TMS with electroencephalography (EEG) to assess TMS-evoked potentials (TEPs) before and after stimulation. As an individual’s neurophysiological state influences stimulation outcomes, this study explores whether pre-stimulation resting-state EEG can predict changes in TEP component amplitudes following intermittent (iTBS) and continuous (cTBS) protocols applied to the left primary motor cortex, using data from fifteen healthy male participants in a randomized, single-blind crossover design. Linear (Lasso regression) and nonlinear (CatBoost regression) models were designed to predict changes in six TEP components (N15, P30, N45, P60, N100, P180). Both models consistently achieved lower mean absolute errors than the random guessing baseline, demonstrating their ability to capture meaningful predictive patterns in cortical responses. The best performing model varied by TEP component and TBS protocol. Incorporating feature deltas (post- vs. pre-stimulation feature difference) did not significantly enhance predictive performance. Feature importance analysis revealed the predictive value of spectral power and connectivity measures. For instance, connectivity between the stimulation site and frontal/parietal regions, together with oscillatory power in frontal and motor areas, often emerged as the top predictors. Given the limited sample size, these findings should be interpreted as exploratory and hypothesis-generating, requiring validation in larger and independent cohorts. The study establishes a framework for predicting individual TEP responses to TBS using machine learning, paving the way for personalized neuromodulation.