Model-based design for seizure control by stimulation

Arian Ashourvan, Sérgio Pequito, Ankit N. Khambhati, Fadi Mikhail, Steven N. Baldassano, Kathryn A. Davis, Timothy H. Lucas, Jean M. Vettel, Brian Litt, George J. Pappas, Danielle S. Bassett

Research output: Contribution to journalArticlepeer-review

Abstract

Objective. Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. Approach. Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. Main results. Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. Significance. Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.

Original languageEnglish (US)
Article number026009
JournalJournal of neural engineering
Volume17
Issue number2
DOIs
StatePublished - Apr 2020

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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