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. Then cross-frequency phase-phase coupling (PPC) was applied to a 20-s mid-seizure window and a contour of −3 dB coupling strength was determined. The contour centroid polar coordinates, amplitude (alpha) and angle (theta), were calculated. Association of alpha and theta with SUDEP was assessed and a logistic classifier for alpha was constructed.

Results

Alpha was higher in SUDEP patients, compared to non-SUDEP patients (p < 0.001). Theta showed no significant difference between patient populations. The receiver operating characteristic (ROC) of a logistic classifier for alpha resulted in an area under the ROC curve (AUC) of 94% and correctly classified two test SUDEP patients.

Discussion

This study develops a novel metric alpha, which highlights non-linear interactions between two rhythms in the ECG, and is predictive of SUDEP risk.

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. Functional connectivity networks were constructed and examined as directed graphs derived from undirected delta and high frequency oscillation (HFO) EEG coherence networks in eight SUDEP and 14 non-SUDEP epileptic patients. These networks were proxies for information flow at different spatiotemporal scales, where low frequency oscillations coordinate large-scale activity driving local HFOs. The clustering coefficient and global efficiency of the network were higher in the SUDEP group pre-ictally, ictally and post-ictally (p < 0.0001 to p < 0.001), with features characteristic of small-world networks. These results suggest that cross-frequency functional connectivity network topology may be a non-invasive biomarker of SUDEP risk.

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. Currently, electroencephalographic (EEG) features of SRS in rodent models of extended kindling remain to be detailed. We explored this using a mouse model of extended hippocampal kindling. Intracranial EEG recordings were made from the kindled hippocampus and unstimulated hippocampal, neocortical, piriform, entorhinal, or thalamic area in individual mice. Spontaneous EEG discharges with concurrent low-voltage fast onsets were observed from the two corresponding areas in nearly all SRS detected, irrespective of associated motor seizures. Examined in brain slices, epileptiform discharges were induced by alkaline artificial cerebrospinal fluid in the hippocampal CA3, piriform and entorhinal cortical areas of extended kindled mice but not control mice. Together, these in vivo and in vitro observations suggest that the epileptic activity involving a macroscopic network may generate concurrent discharges in forebrain areas and initiate SRS in hippocampally kindled mice.

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. 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.

Effects of Antiepileptic Drugs on Spontaneous Recurrent Seizures in a Novel Model of Extended Hippocampal Kindling in Mice

Epilepsy is a common neurological disorder characterized by naturally-occurring spontaneous recurrent seizures and comorbidities. Kindling has long been used to model epileptogenic mechanisms and to assess antiepileptic drugs. In particular, extended kindling can induce spontaneous recurrent seizures without gross brain lesions, as seen clinically. To date, the development of spontaneous recurrent seizures following extended kindling, and the effect of the antiepileptic drugs on these seizures are not well understood. In the present study we aim to develop a mouse model of extended hippocampal kindling for the first time. Once established, we plan to evaluate the effect of three different antiepileptic drugs on the development of the extended-hippocampal-kindled-induced spontaneous recurrent seizures. Male C57 black mice were used for chronic hippocampal stimulations or handling manipulations (twice daily for up to 70 days). Subsequently, animals underwent continuous video/EEG monitoring for seizure detection. Spontaneous recurrent seizures were consistently observed in extended kindled mice but no seizures were detected in the control animals. The aforementioned seizures were generalized events characterized by hippocampal ictal discharges and concurrent motor seizures. Incidence and severity of the seizures was relatively stable while monitored over a few months after termination of the hippocampal stimulation. Three antiepileptic drugs with distinct action mechanisms were tested: phenytoin, lorazepam and levetiracetam. They were applied via intra-peritoneal injections at anticonvulsive doses and their effects on the spontaneous recurrent seizures were analyzed 10-12 h post-injection. Phenytoin (25 mg/kg) and levetiracetam (400 mg/kg) abolished the spontaneous recurrent seizures. Lorazepam (1.5 mg/kg) decreased motor seizure severity but did not reduce the incidence and duration of corresponding hippocampal discharges, implicating its inhibitory effects on seizure spread. No gross brain lesions were observed in a set of extended hippocampal kindled mice submitted to histological evaluation. All these data suggests that our model could be considered as a novel mouse model of extended hippocampal kindling. Some limitations remain to be considered.

Highly efficient adenoviral transduction of pancreatic islets using a microfluidic device

Tissues are challenging to genetically manipulate due to limited penetration of viral particles resulting in low transduction efficiency. We are particularly interested in expressing genetically-encoded sensors in ex vivo pancreatic islets to measure glucose-stimulated metabolism, however poor viral penetration biases these measurements to only a subset of cells at the periphery. To increase mass transfer of viral particles, we designed a microfluidic device that holds islets in parallel hydrodynamic traps connected by an expanding by-pass channel. We modeled viral particle flow into the tissue using fluorescently-labelled gold nanoparticles of varying sizes and showed a penetration threshold of only ∼5 nm. To increase this threshold, we used EDTA to transiently reduce cell-cell adhesion and expand intercellular space. Ultimately, a combination of media flow and ETDA treatment significantly increased adenoviral transduction to the core of the islet. As proof-of-principle, we used this protocol to transduce an ER-targeted redox sensitive sensor (eroGFP), and revealed significantly greater ER redox capacity at core islet cells. Overall, these data demonstrate a robust method to enhance transduction efficiency of islets, and potentially other tissues, by using a combination of microfluidic flow and transient tissue expansion.