Increasing synchronization may promote seizure termination: Evidence from status epilepticus

Data-driven estimates of the number of clusters in multivariate time series

An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.

Evolving functional network properties and synchronizability during human epileptic seizures

We assess electrical brain dynamics before, during, and after 100 human epileptic seizures with different anatomical onset locations by statistical and spectral properties of functionally defined networks. We observe a concave-like temporal evolution of characteristic path length and cluster coefficient indicative of a movement from a more random toward a more regular and then back toward a more random functional topology. Surprisingly, synchronizability was significantly decreased during the seizure state but increased already prior to seizure end. Our findings underline the high relevance of studying complex systems from the viewpoint of complex networks, which may help to gain deeper insights into the complicated dynamics underlying epileptic seizures.

Epilepsy and nonlinear dynamics

This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.

How do seizures stop?

Although often overshadowed by factors influencing seizure initiation, seizure termination is a critical step in the return to the interictal state. Understanding the mechanisms contributing to seizure termination could potentially identify novel targets for anticonvulsant drug development and may also highlight the pathophysiological processes contributing to seizure initiation. In this article, we review known physiological mechanisms contributing to seizure termination and discuss additional mechanisms that are likely to be relevant even though specific data are not yet available. This review is organized according to successively increasing "size scales"-from membranes to synapses to networks to circuits. We first discuss mechanisms of seizure termination acting at the shortest distances and affecting the excitable membranes of neurons in the seizure onset zone. Next we consider the contributions of ensembles of neurons and glia interacting at intermediate distances within the region of the seizure onset zone. Lastly, we consider the contribution of brain nuclei, such as the substantia nigra pars reticulata (SNR), that are capable of modulating seizures and exert their influence over the seizure onset zone (and neighboring areas) from a relatively great-in neuroanatomical terms-distance. It is our hope that the attention to the mechanisms contributing to seizure termination will stimulate novel avenues of epilepsy research and will contribute to improved patient care.

Changes of EEG synchronization during low-frequency electric stimulation of the seizure onset zone

PURPOSE

To assess whether EEG synchronization changes during short-term low-frequency electrical stimulation of the seizure onset zone.

METHODS

In 10 patients (34+/-11 years) with pharmaco-resistant epilepsy the seizure onset zone (9 temporal lobe, 1 frontal lobe) was electrically stimulated at 1Hz for 5min via intracranial electrodes. Bipolar stimuli were applied and four pulse widths (0.05, 0.1, 0.5, and 1.0ms) were tested. Stimulation amplitudes were held fixed at 1mA for strip electrodes and at 2mA for depth electrodes. Changes of EEG synchronization were assessed by the eigenvalue dynamics of the cross-correlation matrix computed from a 2.5s sliding window.

RESULTS

37 stimulations were performed. We observed EEG desynchronization in 49% (18/37), an increase of EEG synchronization in 27% (10/37) and an EEG pattern with no significant change of synchronization in 24% (9/37). EEG synchronization most frequently occurred when stimulating with a pulse width of 0.5ms. In a patient with bilateral independent seizure onsets stimulation effects on EEG synchronization were different for each side. In the patient with the shortest duration of temporal lobe epilepsy, stimulation triggered periodic epileptic spikes phase-locked to stimulation. One patient experienced an aura during stimulation, which did not evolve into a seizure, and in one patient a sub-clincial seizure occurred.

DISCUSSION

Low-frequency stimulation of the seizure onset zone is associated with different changes of EEG synchronization and its effects depend on the widths of the stimulation pulses. It may be an appropriate stimulation technique for long-term studies assessing whether synchronized or desynchronized brain dynamics prevent seizure occurrence.