Phase-amplitude coupling analysis shows that a state of postictal generalized EEG suppression has increased delta-gamma coupling. These coupling features, used with an unsupervised hidden Markov model, reliably differentiated four substates in seizure episodes. A sudden unexpected death in epilepsy case study showed coupling activity similar to a postictal state. Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5-4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5-1.5 Hz signal and amplitude of 30-50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier – a hidden Markov model – was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics, that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

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Publisher's Statement © 2017 IEEE. Personal use of this material is permitted.Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. How to cite TSpace itemsAlways cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the author manuscript from TSpace because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page.This article was made openly accessible by U of T Faculty. Please tell us how this access benefits you. Your story matters.0018-9294 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBME. Abstract-Objective: One of the features used in the study of hyperexcitablility are high frequency oscillations (HFOs, >80Hz). HFOs have been reported in the electrical rhythms of the brain's neuro-glial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low frequency rhythms was used to identify pathologic HFOs (pHFOs) (i) in the epileptogenic zones of epileptic patients, and (ii) as a biomarker for the severity of seizure-like events in genetically modified rodent models. We describe a model to replicate reported CFC features extracted from recorded local field potentials (LFPs) representing network properties. Methods: This study deals with a 4-unit neuro-glial cellular network model where each unit incorporates pyramidal cells, interneurons and astrocytes. Three different pathways of hyperexcitability generation -Na + -K + ATPase pump, glial potassium clearance, and potassium afterhyperpolarization channel -were used to generate LFPs. Changes in excitability, average spontaneous electrical discharge (SED) duration and CFC were then measured and analyzed. Results: Each parameter caused an increase in network excitability and the consequent lengthening of the SED duration. Short SEDs showed CFC between HFOs and theta oscillations (4-8 Hz), but in longer SEDs the low frequency changed to the delta range (1-4 Hz). Conclusion: Longer duration SEDs exhibit CFC features similar to those reported by our team. Significance: (i) Identifying the exponential relationship between network excitability and SED durations, (ii) highlighting the importance of glia in hyperexcitability (as they relate to extracellular potassium), and (iii) elucidation of the ...

properties. Particularly, short and long SEDs are associated with deterministic and random processes, respectively. Furthermore, there was evidence of theta-HFO phase-amplitude cross-frequency coupling (CFC) in the short SEDs, and delta-HFO CFC in the long SEDs, as was previously reported in a mouse model of Seizure-Like Events (SLEs) and in human patients with epilepsy.iii