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This study characterizes the spatial-temporal distribution of epileptic activity in mesial and neocortical temporal lobe epilepsy (TLE) assessed by subdural and depth electrodes during sleep. We determined in 13 patients the frequency, lateralization, and localization of interictal epileptic discharges (IEDs). As compared to the waking state, IEDs increased in light sleep (196%, p0.05) and in deep sleep (601%, p0.05) but did not change significantly in REM sleep (-8.33%, p = 0.94). From 11 patients with unilateral TLE, in all cases, IEDs lateralized to the seizure onset side during REM sleep and the waking state. In mesial TLE, IEDs tended to shift in an anterior-posterior axis and remained always localized in the amygdalo-hippocampal complex. By contrast, in neocortical TLE, the maximal activity moved in a mesial-lateral axis between neocortical and mesial structures. Twenty-six seizures were registered in 7 patients, 22 of which occurred in light sleep and 4 in wakefulness, but none occurred in deep or REM sleep.

Time scale parametric spike train distances like the Victor and the van Rossum distances/nare often applied to study the neural code based on neural stimuli discrimination./nDifferent neural coding hypotheses, such as rate or coincidence coding,/ncan be assessed by combining a time scale parametric spike train distance with a/nclassifier in order to obtain the optimal discrimination performance. The time scale/nfor which the responses to different stimuli are distinguished best is assumed to be/nthe discriminative precision of the neural code. The relevance of temporal coding/nis evaluated by comparing the optimal discrimination performance with the one/nachieved when assuming a rate code./nWe here characterize the measures quantifying the discrimination performance,/nthe discriminative precision, and the relevance of temporal coding. Furthermore,/nwe evaluate the information these quantities provide about the neural code. We/nshow that the discriminative precision is too unspecific to be interpreted in terms/nof the time scales relevant for encoding. Accordingly, the time scale parametric/nnature of the distances is mainly an advantage because it allows maximizing the/ndiscrimination performance across a whole set of measures with different sensitivities/ndetermined by the time scale parameter, but not due to the possibility to/nexamine the temporal properties of the neural code. DC is supported by the grant 2010FI-B2 00079 of the ”Comissionat per a Universitats/ni Recerca del Departament d’Innovació, Universitats i Empresa de la Generalitat/nde Catalunya i del Fons Social Europeu” and grant 2008BE1 00166 of the ”Comissionat per a Universitats i Recerca del Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya”. RGA acknowledges grant BFU2007-61710 of the Spanish Ministry of Education and Science.

The investigation of synchronization phenomena on measured experimental data such as biological time series has recently become an increasing focus of interest. Different approaches for measuring synchronization have been proposed that rely on certain characteristic features of the dynamical system under investigation. For experimental data the underlying dynamics are usually not completely known, therefore it is difficult to decide a priori which synchronization measure is most suitable for an analysis. In this study we use three different coupled model systems to create a ‘controlled’ setting for a comparison of six different measures of synchronization. All measures are compared to each other with respect to their ability to distinguish between different levels of coupling and their robustness against noise. Results show that the measure to be applied to a certain task can not be chosen according to a fixed criterion but rather pragmatically as the measure which most reliably yields plausible information in test applications, although certain dynamical features of a system under investigation (e.g., power spectra, dimension) may render certain measures more suitable than others. c 2006 Elsevier B.V. All rights reserved.

Objective: An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches. Methods: We compared 30 measures in terms of their ability to distinguish between the interictal period and the pre-seizure period. After completely analyzing continuous inctracranial multi-channel recordings from five patients lasting over days, we used ROC curves to distinguish between the amplitude distributions of interictal and preictal time profiles calculated for the respective measures. We compared different evaluation schemes including channelwise and seizurewise analysis plus constant and adaptive reference levels. Particular emphasis was placed on statistical validity and significance. Results: Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5‐30 min before seizures. Bivariate measures exhibited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 min before seizures. Linear measures were found to perform similar or better than non-linear measures. Conclusions: Results provide statistically significant evidence for the existence of a preictal state. Based on our findings, the most promising approach for prospective seizure anticipation could be a combination of bivariate and univariate measures. Significance: Many measures reported capable of seizure prediction in earlier studies are found to be insignificant in performance, which underlines the need for statistical validation in this field. q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The exact mechanisms leading to the occurrence of epileptic seizures in humans are still poorly understood. It is widely accepted, however, that the process of seizure generation is closely associated with an abnormal synchronization of neurons. In order to investigate this process, we here measure phase synchronization between different regions of the brain using intracranial EEG recordings. Based on our preliminary finding of a preictal drop in synchronization, we investigate whether this phenomenon can be used as a sensitive and specific criterion to characterize a preseizure state and to distinguish this state from the interictal interval. Applying an automated technique for detecting decreased synchronization to EEG recordings from a group of 18 patients with focal epilepsy comprising a total of 117 h, we observe a characteristic decrease in synchronization prior to 26 out of 32 analyzed seizures at a very high specificity as tested on interictal recordings. The duration of this preictal state is found to range from several minutes up to a few hours. Investigation of the spatial distribution of preictal desynchronization indicates that the process of seizure generation in focal epilepsy is not necessarily confined to the focus itself but may instead involve more distant, even contralateral areas of the brain. Finally, we demonstrate an intrahemispheric asymmetry in the spatial dynamics of preictal desynchronization that is found in the majority of seizures and appears to be an immanent part of the mechanisms underlying the initiation of seizures in humans.

The theory of deterministic chaos addresses simple deterministic dynamics in which nonlinearity gives rise to complex temporal behavior. Although biological neuronal networks such as the brain are highly complicated, a number of studies provide growing evidence that nonlinear time series analysis of brain electrical activity in patients with epilepsy is capable of providing potentially useful diagnostic information. In the present study, this analysis framework was extended by introducing a new measure ξ , designed to discriminate between nonlinear deterministic and linear stochastic dynamics. For the evaluation of its discriminative power, ξ was extracted from intracranial multi-channel EEGs recorded during the interictal state in 25 patients with unilateral mesial temporal lobe epilepsy. Strong indications of nonlinear determinism were found in recordings from within the epileptogenic zone, while EEG signals from other sites mainly resembled linear stochastic dynamics. In all investigated cases, this differentiation allowed to retrospectively determine the side of the epileptogenic zone in full agreement with results of the presurgical workup.