ArticleCommunications biology2024
Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Ion dynamics underlying the seizure delay effect of low-frequency electrical stimulation.PLoS computational biology · 2025Article
- Bioelectric; Neuromodulation; Epilepsy; DBS; High-frequency stimulation; Low frequency stimulation.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Analyzing the brain's dynamic response to targeted stimulation using generative modeling.Network neuroscience (Cambridge, Mass.) · 2025Review
- Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy.Communications biology · 2024Article
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Authors and funding
3 authors.
Funding
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 exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.
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Registered trials
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