The ambiguous relationship between spikes and seizures

Antiepileptic drugs induce subcritical dynamics in human cortical networks

Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.

Seizure prediction and intervention

Epilepsy treatment is challenging due to a lack of essential diagnostic tools, including methods for reliable seizure detection in the ambulatory setting, to assess seizure risk over time and to monitor treatment efficacy. This lack of objective diagnostics constitutes a significant barrier to better treatments, raises methodological concerns about the antiseizure medication evaluation process and, to patients, is a main issue contributing to the disease burden. Recent years have seen rapid progress towards better diagnostics that meet these needs of epilepsy patients and clinicians. Availability of comprehensive data and the rise of more powerful computational analysis methods have driven progress in this area. Here, we provide an overview on data- and theory-driven approaches aimed at identifying methods to reliably detect and forecast seizures as well as to monitor brain excitability and treatment efficacy in epilepsy. We provide a particular account on neural criticality, the hypothesis that cortical networks may be poised in a critical state at the boundary between different types of dynamics, and discuss its role in informing diagnostics to track cortex excitability and seizure risk in recent experiments. With the further expansion of digitalization in medicine, tele-medicine and long-term, ambulatory monitoring, these computationally based methods may gain more relevance in epilepsy in the future. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Seizure prediction - ready for a new era

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

EEG changes in patients on antipsychotic therapy: A systematic review

OBJECTIVES

The objective of the study was to characterize the electroencephalogram (EEG) changes associated with different antipsychotic medications based on the evidence from the literature.

METHODS

A systematic search of the databases Medline, PsycINFO, and PubMed was conducted. The Preferred Items Reporting for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in the construction of this systematic review. Primary research articles that reported descriptive EEG results, included comparisons of subjects with and without antipsychotic therapy, and excluded patients with epilepsy were included in the analysis. The outcome was the presence of epileptiform discharges or slowing on EEG. We analyzed pooled data, where possible, from studies with a similar intervention and methodology.

RESULTS

Fourteen articles reporting on a total of 665 patients were reviewed. Among the publications, clozapine was the drug most consistently accompanied by EEG slowing and epileptiform discharges, with an odds ratio of 16.9 (95% confidence intervals (CI): 5.4 to 53.2) and 6.2 (95% CI: 3.4 to 11.3), respectively in the analysis of pooled data. Only one study reported a significant increase in epileptiform discharges with phenothiazine antipsychotic therapy as a group, but the impact of individual drugs was not analyzed separately.

CONCLUSIONS

This systematic review suggests that, among antipsychotics, clozapine most frequently induces EEG slowing and epileptiform discharges. There remains limited data with respect to other individual antipsychotic agents and covariates including drug dose, plasma levels, dose adjustments, and treatment duration that influence EEG changes.