Computational model prospective on the observation of proictal states in epileptic neuronal systems.

Epilepsy is a pathological condition of the human central nervous system in which normal brain functions are impaired by unexpected transitions to states called seizures. We developed a lumped neuronal model that has the property of switching between two states as a result of intrinsic or extrinsic perturbations, such as noisy fluctuations. In one version of the model, seizure risk is controlled by a single connectivity parameter representing excitatory couplings between two model lumps. We show that this risk can be reconstructed from calculation of the cross-covariance between the activities of the two neural populations during the nonictal phase. In a second simulation sequence, we use a system of 10 interconnected lumps with randomly generated connectivity matrices. We show again that the tendency to develop seizures can be inferred from the cross-covariances calculated during the nonictal states. Our conclusion is that the risk of epileptic transitions in biological systems can be objectively quantified. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Beta rebound after different types of motor imagery in man.

We investigated the behavior of short-lasting beta bursts (beta rebound, beta ERS) induced after imagination of hand, foot or tongue movement. Nine able-bodied subjects were asked to imagine one type of movement following the presentation of a visual cue stimulus. EEG was recorded from 60 closely spaced electrodes placed over frontal, central and parietal areas. Spatiotemporal ERD/ERS patterns were evaluated in form of time-frequency maps. Seven out of nine subjects displayed a significant short-lasting beta power increase (beta rebound) after termination of imagery of movement of both feet in the frequency range of 23-29Hz with a clear maximum at the vertex. In contrast, only two subjects displayed a clear beta rebound at the vertex after imagery of one of the hands, although five exhibited a significant beta rebound at the contralateral side of the head. None showed a beta rebound after tongue motor imagery. The midcentrally located beta rebound is interpreted as an electrophysiological correlate of a simultaneous "resetting" of overlapping neural networks in the foot representation areas and the SMA.

Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy.

OBJECTIVE: To explore and validate a novel stimulation and analysis paradigm proposed to monitor spatial distribution and temporal changes of the excitability state in patients with temporal lobe epilepsy (TLE). METHODS: We use intermittent pulse stimulation in the frequency range 10-20Hz. A quantitative measure of spectral phase de-modulation, the relative phase clustering index (rPCI) was applied to the evoked EEG signals, measured from electrodes implanted in the hippocampal formation. RESULTS: We found that in the interictal periods, high values of rPCI recorded from specific sites were correlated with the most probable seizure onset sites (SOS). Furthermore we found that high values of rPCI from certain locations correlated with shorter time intervals to the next seizure. CONCLUSIONS: Our clinical findings indicate that although the precise moment of ictal transitions is in general unpredictable, it may be possible to estimate the probability of occurrence of some epileptic seizures. SIGNIFICANCE: The use of the rPCI for probabilistic forecasting of upcoming epileptic seizures is warranted. rPCI measurements may be used to guide interventions with the aim of modifying local tissue excitability that ultimately might prevent ictal transitions.