Abstracts

Rationale: Rigorously analyzing intracranial EEG (iEEG) recordings in epilepsy is critical to controlling seizures with implantable brain devices. Current brain stimulation paradigms are largely empirical, providing an opportunity to improve their effectiveness in closed-loop by leveraging feedback control strategies using model-based design. Methods: We present a framework for modeling seizures that can be used in control strategies using a linear dynamical systems model. We analyzed the peri-ictal intracranial EEG in 94 seizures recorded from 21 patients with medication-resistant, partial-onset epilepsy. Results: Quantitative assessment of the temporal fluctuations in the spatial profile of the estimated systems’ oscillatory modes revealed that at seizure onset, the epileptic network transitions gradually or abruptly into a time-invariant regime of prolonged focal oscillations across all seizure samples. Our model provides a quantitative framework in which closed-loop actuation may be used to effectively dampen focal rhythmic activity characteristic of seizures, and prevent seizure spread. We present examples of the model applied to patients who are candidates for responsive brain stimulation devices and explain how the algorithm can be used prospectively to increase their effectiveness. Conclusions: Hence, this approach paves the ground for modulation and control algorithms for brain stimulation using implantable closed-loop anti-epileptic devices. Funding: D.S.B., A.A., and A.N.K. would like to acknowledge support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Health (2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, R01MH109520, 1R01NS099348, R21-M MH-106799, the Office of Naval Research, and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, BCS-1631550, and CNS-1626008). K.D. acknowledges support from K23NS073801-01, and UH2-NS095495-01. S.P. and G.J.P. are supported in part by the TerraSwarm Research Center, one of six centers supported by the STARnet phase of the Focus Center Research Program (FCRP) a Semiconductor Research Corporation program sponsored by MARCO and DARPA. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. B.L. acknowledges support from NINDS R01 NS099348-01, NIH UH2 NS095495-01, 5 R01 NS092882, the Mirowsky Family Foundation and Neil and Barbara Smit. B.L. is a founder of Blackfynn, a company focused in data integration, analysis and neurodevices.