Pre-stimulus brain states predict and control variability in stimulation responses

Brain stimulation outcomes vary across trials even under identical parameters and targets, limiting reproducibility and clinical translation. Previous work suggests that ongoing brain states, including oscillatory and network-level activity, can modulate stimulation effects, yet the most reliable predictive features and their generalizability across modalities remain unclear.

We analyzed simultaneous high-density EEG and stereotactic EEG recordings from 36 epilepsy patients across approximately 320 sessions, comprising more than 10,000 single-pulse electrical stimulations. Trial-by-trial relationships between pre-stimulus spontaneous activity and post-stimulus evoked responses were screened across 127 candidate metrics, after which primary analyses were restricted to the 5 most reliable metrics of interest. These metrics captured signal dynamics, synchronization, functional connectivity, and complexity, and were quantified at both local and whole-brain scales RESULTS:: Pre-stimulus brain states explained a substantial fraction of post-stimulation variance within session, with higher mean ρ² in SEEG than hd-EEG (17.6% vs 7.1%; maxima 85.0% and 71.8%, respectively), and also supported out-of-sample prediction, with positive generalization observed on average in 42.1% of SEEG sessions and 19.0% of hd-EEG sessions. Whole-brain pre-stimulus measures outperformed local measures in both modalities. Explanatory power was also network-dependent and, in SEEG, followed a hierarchy, with the highest explained variance in primary systems and lower values in higher-order networks. Retrospective and prospective closed-loop analyses further converged in showing that conditioning stimulation on favorable pre-stimulus states reduced post-stimulation response variability.

This work establishes that trial-to-trial variability in stimulation outcomes is not purely stochastic, but is systematically shaped by ongoing brain dynamics. These findings shift the focus from fixed stimulation settings toward adaptive, state-aware strategies. By identifying pre-stimulus neural signatures that explain and predict stimulation responses, and by showing convergent retrospective and prospective support for state-dependent control, this study provides candidate biomarkers for real-time monitoring and closed-loop neuromodulation. Together, these results support the development of more reproducible and translational stimulation protocols in both research and clinical settings.