Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy.

ABSTRACT: Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects

SUBMITTER: Acharya G 

PROVIDER: S-EPMC11438964 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature