A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy.

The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.

Resetting of brain dynamics: epileptic versus psychogenic nonepileptic seizures.

We investigated the possibility of differential diagnosis of patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES) through an advanced analysis of the dynamics of the patients' scalp EEGs. The underlying principle was the presence of resetting of brain's preictal spatiotemporal entrainment following onset of ES and the absence of resetting following PNES. Long-term (days) scalp EEGs recorded from five patients with ES and six patients with PNES were analyzed. It was found that: (1) Preictal entrainment of brain sites was reset at ES (P<0.05) in four of the five patients with ES, and not reset (P=0.28) in the fifth patient. (2) Resetting did not occur (p>0.1) in any of the six patients with PNES. These preliminary results in patients with ES are in agreement with our previous findings from intracranial EEG recordings on resetting of brain dynamics by ES and are expected to constitute the basis for the development of a reliable and supporting tool in the differential diagnosis between ES and PNES. Finally, we believe that these results shed light on the electrophysiology of PNES by showing that occurrence of PNES does not assist patients in overcoming a pathological entrainment of brain dynamics. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Interactive rehabilitation and dynamical analysis of scalp EEG.

Electroencephalography (EEG) has been used for decades to measure the brain's electrical activity. Planning and performing a complex movement (e.g., reaching and grasping) requires the coordination of muscles by electrical activity that can be recorded with scalp EEG from relevant regions of the cortex. Prior studies, utilizing motion capture and kinematic measures, have shown that an augmented reality feedback system for rehabilitation of stroke patients can help patients develop new motor plans and perform reaching tasks more accurately. Historically, traditional signal analysis techniques have been utilized to quantify changes in EEG when subjects perform common, simple movements. These techniques have included measures of event-related potentials in the time and frequency domains (e.g., energy and coherence measures). In this study, a more advanced, nonlinear, analysis technique, mutual information (MI), is applied to the EEG to capture the dynamics of functional connections between brain sites. In particular, the cortical activity that results from the planning and execution of novel reach trajectories by normal subjects in an augmented reality system was quantified by using statistically significant MI interactions between brain sites over time. The results show that, during the preparation for as well as the execution of a reach, the functional connectivity of the brain changes in a consistent manner over time, in terms of both the number and strength of cortical connections. A similar analysis of EEG from stroke patients may provide new insights into the functional deficiencies developed in the brain after stroke, and contribute to evaluation, and possibly the design, of novel therapeutic schemes within the framework of rehabilitation and BMI (brain machine interface).