Wavelet phase coherence of ictal scalp EEG-extracted muscle activity (SMA) as a biomarker for sudden unexpected death in epilepsy (SUDEP).

Objective: Approximately 50 million people worldwide have epilepsy and 8-17% of the deaths in patients with epilepsy are attributed to sudden unexpected death in epilepsy (SUDEP). The goal of the present work was to establish a biomarker for SUDEP so that preventive treatment can be instituted.

Approach: Seizure activity in patients with SUDEP and non-SUDEP was analyzed, specifically, the scalp EEG extracted muscle activity (SMA) and the average wavelet phase coherence (WPC) during seizures was computed for two frequency ranges (1-12 Hz, 13-30 Hz) to identify differences between the two groups.

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.

Objective: In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models.

Methods: Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome.

EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms.

Objective: Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls.

Ictal ECG-based assessment of sudden unexpected death in epilepsy.

Introduction: Previous case-control studies of sudden unexpected death in epilepsy (SUDEP) patients failed to identify ECG features (peri-ictal heart rate, heart rate variability, corrected QT interval, postictal heart rate recovery, and cardiac rhythm) predictive of SUDEP risk. This implied a need to derive novel metrics to assess SUDEP risk from ECG.

Methods: We applied Single Spectrum Analysis and Independent Component Analysis (SSA-ICA) to remove artifact from ECG recordings.

A Peri-Ictal EEG-Based Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP) Derived From Brain Network Analysis.

Sudden unexpected death in epilepsy (SUDEP) is the leading seizure-related cause of death in epilepsy patients. There are no validated biomarkers of SUDEP risk. Here, we explored peri-ictal differences in topological brain network properties from scalp EEG recordings of SUDEP victims.

Interregional phase-amplitude coupling between theta rhythm in the nucleus tractus solitarius and high-frequency oscillations in the hippocampus during REM sleep in rats.

Cross-frequency coupling (CFC) between theta and high-frequency oscillations (HFOs) is predominant during active wakefulness, REM sleep and behavioral and learning tasks in rodent hippocampus. Evidence suggests that these state-dependent CFCs are linked to spatial navigation and memory consolidation processes. CFC studies currently include only the cortical and subcortical structures.

Altered neocortical oscillations and cellular excitability in an in vitro Wwox knockout mouse model of epileptic encephalopathy.

Loss of function mutations of the WW domain-containing oxidoreductase (WWOX) gene are associated with severe and fatal drug-resistant pediatric epileptic encephalopathy. Epileptic seizures are typically characterized by neuronal hyperexcitability; however, the specific contribution of WWOX to that hyperexcitability has yet to be investigated. Using a mouse model of neuronal Wwox-deletion that exhibit spontaneous seizures, in vitro whole-cell and field potential electrophysiological characterization identified spontaneous bursting activity in the neocortex, a marker of the underlying network hyperexcitability.

Electrographic Features of Spontaneous Recurrent Seizures in a Mouse Model of Extended Hippocampal Kindling.

Epilepsy is a chronic neurological disorder characterized by spontaneous recurrent seizures (SRS) and comorbidities. Kindling through repetitive brief stimulation of a limbic structure is a commonly used model of temporal lobe epilepsy. Particularly, extended kindling over a period up to a few months can induce SRS, which may simulate slowly evolving epileptogenesis of temporal lobe epilepsy.

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression.

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.

Pannexin-1 Deficiency Decreases Epileptic Activity in Mice.

Objective: Pannexin-1 (Panx1) is suspected of having a critical role in modulating neuronal excitability and acute neurological insults. Herein, we assess the changes in behavioral and electrophysiological markers of excitability associated with Panx1 via three distinct models of epilepsy. Methods Control and Panx1 knockout C57Bl/6 mice of both sexes were monitored for their behavioral and electrographic responses to seizure-generating stimuli in three epilepsy models-(1) systemic injection of pentylenetetrazol, (2) acute electrical kindling of the hippocampus and (3) neocortical slice exposure to 4-aminopyridine.

Transitions between neocortical seizure and non-seizure-like states and their association with presynaptic glutamate release.

The transition between seizure and non-seizure states in neocortical epileptic networks is governed by distinct underlying dynamical processes. Based on the gamma distribution of seizure and inter-seizure durations, over time, seizures are highly likely to self-terminate; whereas, inter-seizure durations have a low chance of transitioning back into a seizure state. Yet, the chance of a state transition could be formed by multiple overlapping, unknown synaptic mechanisms.

Glial Modulation of Electrical Rhythms in a Neuroglial Network Model of Epilepsy.

Objective: An important EEG-based biomarker for epilepsy is the phase-amplitude cross-frequency coupling (PAC) of electrical rhythms; however, the underlying pathways of these pathologic markers are not always clear. Since glial cells have been shown to play an active role in neuroglial networks, it is likely that some of these PAC markers are modulated via glial effects.

Methods: We developed a 4-unit hybrid model of a neuroglial network, consisting of 16 sub-units, that combines a mechanistic representation of neurons with an oscillator-based Cognitive Rhythm Generator (CRG) representation of glial cells-astrocytes and microglia.

Cross-Frequency Coupling Features of Postictal Generalized EEG Suppression State.

In patients with epilepsy, convulsive seizures are often followed by a postictal generalized EEG suppression (PGES) state characterized by reduced background activity. Recent studies found a correlation between seizure termination state and PGES duration, and suggested that PGES is the result of the cessation of neuronal activity. To test that assertion, we investigated ten seizure records obtained from intracranial EEG (iEEG) from six patients, four of which had Engel Class 1 surgical outcome.

Excitation and Inhibition Balance Underlying Epileptiform Activity.

Objective: The phenomenon of postictal generalized EEG suppression state (PGES) - a period with suppressed activity following seizure termination and has been found to be associated with sudden unexpected death in epilepsy - remains poorly understood. This article aims to examine the how the balance of excitation and inhibition (E/I balance) affect the dynamics of seizure and PGES.

Methods: A network of 1000 Izhikevich model neurons was developed and only the strengths of synaptic connections were adjusted to recreate the dynamics observed in recordings of seizure and PGES from human patients.

Classification of Scalp EEG States Prior to Clinical Seizure Onset.

Objective: To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group.

Results: Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.

Epilepsy as a manifestation of a multistate network of oscillatory systems.

The human brain, largely accepted as the most complex biological system known, is still far from being understood in its parts or as a whole. More specifically, biological mechanisms of epileptic states and state transitions are not well understood. Here, we explore the concept of the epilepsy as a manifestation of a multistate network composed of coupled oscillatory units.

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions.

In the epileptic brain, phase amplitude cross-frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in Layer II/III using simultaneous local field potential (LFP) and whole-cell voltage clamp electrodes.

Neuro-Glial Network Model Of Postictal Generalized EEG Suppression (PGES).

Over the past couple of decades, glial cells have been highlighted as active agents in hyperexcitability of neuronal networks, specifically playing key roles in seizure onset and termination. In particular, microglia have been suggested to have both neuroprotective and neurotoxic effects on the brain. Investigation into seizure termination is of particular interest, as it is sometimes followed by a postictal generalized EEG suppression (PGES) - a low activity state that is potentially associated with sudden unexpected death in epilepsy.

Coupled Oscillators Model of Hyperexcitable Neuroglial Networks.

Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team.

Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features.

Objective: This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset.

Methods: EEG recordings from 12 patients with drug resistant epilepsy were marked by an expert neurologist for clinical seizure onset. Scalp EEG recordings consisted of 56 seizures and 9.

Electrographic and pharmacological characterization of a progressive epilepsy phenotype in female MeCP2-deficient mice.

Rett Syndrome is a neurodevelopmental disorder caused primarily by mutations in the gene encoding Methyl-CpG-binding protein 2 (MECP2). Spontaneous epileptiform activity is a common co-morbidity present in Rett syndrome, and hyper-excitable neural networks are present in MeCP2-deficient mouse models of Rett syndrome. In this study we conducted a longitudinal assessment of spontaneous cortical electrographic discharges in female MeCP2-deficient mice and defined the pharmacological responsiveness of these discharges to anti-convulsant drugs.

Low-to-High Cross-Frequency Coupling in the Electrical Rhythms as Biomarker for Hyperexcitable Neuroglial Networks of the Brain.

Objective: One of the features used in the study of hyperexcitablility is high-frequency oscillations (HFOs, >80 Hz). HFOs have been reported in the electrical rhythms of the brain's neuroglial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low-frequency rhythms was used to identify pathologic HFOs in the epileptogenic zones of epileptic patients and as a biomarker for the severity of seizure-like events in genetically modified rodent models.

Prediction of antiepileptic drug treatment outcomes using machine learning.

Objective: Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs.

Approach: Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome.

Epileptogenic Source Imaging Using Cross-Frequency Coupled Signals From Scalp EEG.

Objective: The epileptogenic zone (EZ) is a brain region containing the sources of seizure genesis. Removal of the EZ is associated with cessation of seizures after resective surgical procedures, as measured by Engel Class I score. This study describes a novel EEG (electroencephalography) source imaging (ESI) method which uses cross-frequency coupled potential signals (S) derived from scalp EEG.

Identification of brain regions of interest for epilepsy surgery planning using support vector machines.

In patients with intractable epilepsy, surgical resection is a promising treatment; however, post surgical seizure freedom is contingent upon accurate identification of the seizure onset zone (SOZ). Identification of the SOZ in extratemporal epilepsy requires invasive intracranial EEG (iEEG) recordings as well as resource intensive and subjective analysis by epileptologists. Expert inspection yields inconsistent localization of the SOZ which leads to comparatively poor post surgical outcomes for patients.