A systems-level approach to human epileptic seizures.

Epileptic seizures are due to the pathological collective activity of large cellular assemblies. A better understanding of this collective activity is integral to the development of novel diagnostic and therapeutic procedures. In contrast to reductionist analyses, which focus solely on small-scale characteristics of ictogenesis, here we follow a systems-level approach, which combines both small-scale and larger-scale analyses. Peri-ictal dynamics of epileptic networks are assessed by studying correlation within and between different spatial scales of intracranial electroencephalographic recordings (iEEG) of a heterogeneous group of patients suffering from pharmaco-resistant epilepsy. Epileptiform activity as recorded by a single iEEG electrode is determined objectively by the signal derivative and then subjected to a multivariate analysis of correlation between all iEEG channels. We find that during seizure, synchrony increases on the smallest and largest spatial scales probed by iEEG. In addition, a dynamic reorganization of spatial correlation is observed on intermediate scales, which persists after seizure termination. It is proposed that this reorganization may indicate a balancing mechanism that decreases high local correlation. Our findings are consistent with the hypothesis that during epileptic seizures hypercorrelated and therefore functionally segregated brain areas are re-integrated into more collective brain dynamics. In addition, except for a special sub-group, a highly significant association is found between the location of ictal iEEG activity and the location of areas of relative decrease of localised EEG correlation. The latter could serve as a clinically important quantitative marker of the seizure onset zone (SOZ).

Assessing periodicity of periodic leg movements during sleep.

BACKGROUND: Periodic leg movements (PLM) during sleep consist of involuntary periodic movements of the lower extremities. The debated functional relevance of PLM during sleep is based on correlation of clinical parameters with the PLM index (PLMI). However, periodicity in movements may not be reflected best by the PLMI. Here, an approach novel to the field of sleep research is used to reveal intrinsic periodicity in inter movement intervals (IMI) in patients with PLM. METHODS: Three patient groups of 10 patients showing PLM with OSA (group 1), PLM without OSA or RLS (group 2) and PLM with RLS (group 3) are considered. Applying the "unfolding" procedure, a method developed in statistical physics, enhances or even reveals intrinsic periodicity of PLM. The degree of periodicity of PLM is assessed by fitting one-parameter distributions to the unfolded IMI distributions. Finally, it is investigated whether the shape of the IMI distributions allows to separate patients into different groups. RESULTS: Despite applying the unfolding procedure, periodicity is neither homogeneous within nor considerably different between the three clinically defined groups. Data-driven clustering reveals more homogeneous and better separated clusters. However, they consist of patients with heterogeneous demographic data and comorbidities, including RLS and OSA. CONCLUSIONS: The unfolding procedure may be necessary to enhance or reveal periodicity. Thus this method is proposed as a pre-processing step before analyzing PLM statistically. Data-driven clustering yields much more reasonable results when applied to the unfolded IMI distributions than to the original data. Despite this effort no correlation between the degree of periodicity and demographic data or comorbidities is found. However, there are indications that the nature of the periodicity might be determined by long-range interactions between LM of patients with PLM and OSA.

Analyzing spatio-temporal patterns of genuine cross-correlations.

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

Peri-ictal correlation dynamics of high-frequency (80-200 Hz) intracranial EEG.

PURPOSE: To assess (1) how large-scale correlation of intracranial EEG signals in the high-frequency range (80-200Hz) evolves from the pre-ictal, through the ictal into the postictal state and (2) whether the contribution of local neuronal activity to large-scale EEG correlation differentiates epileptogenic from non-epileptogenic brain tissue. METHODS: Large-scale correlation of intracranial EEG was assessed by the total correlation strength (TCS), a measure derived from the eigenvalue spectra of zero-lag correlation matrices computed in a time-resolved manner by using a moving window approach. The relative change of total correlation strength (Delta(j)) resulting from leaving out EEG channel j ("leave-one-out approach") was used to quantify the contribution of local neuronal activity to large-scale EEG correlation. RESULTS: 19 seizures of 3 patients were analyzed. On average, TCS increased significantly from the pre-ictal to the ictal, and from the ictal to the postictal state. In the pre-ictal state, Delta(j) was significantly more negative when EEG channels that recorded the electrical activity of brain tissue considered to be epileptogenic were left out; the identification of the epileptogenic area, that was subsequently surgically removed in two patients, was based on visual analysis. The spatio-temporal pattern of Delta(j) dramatically changed at seizure onsets and endings, revealing qualitative similarities between the seizures of different patients. DISCUSSION: The evolution of large-scale EEG correlation in the high-frequency range is qualitatively similar to the one previously described for the low-frequency range. Because the two patients who underwent surgery became seizure free, our findings are consistent with the hypothesis that epileptogenic brain tissue may be characterized by its relatively increased contribution to pre-ictal large-scale correlation.

Cortical local and long-range synchronization interplay in human absence seizure initiation.

Brain activity relies on transient, fluctuating interactions between segregated neuronal populations. Synchronization within a single and between distributed neuronal clusters reflects the dynamics of these cooperative patterns. Thus absence epilepsy can be used as a model for integrated, large-scale investigation of the emergence of pathological collective dynamics in the brain. Indeed, spike-wave discharges (SWD) of an absence seizure are thought to reflect abnormal cortical hypersynchronization. In this paper, we address two questions: how and where do SWD arise in the human brain? Therefore, we explored the spatio-temporal dynamics of interactions within and between widely distributed cortical sites using magneto-encephalographic recordings of spontaneous absence seizures. We then extracted, from their time-frequency analysis, local synchronization of cortical sources and long-range synchronization linking distant sites. Our analyses revealed a reproducible sequence of 1) long-range desynchronization, 2) increased local synchronization and 3) increased long-range synchronization. Although both local and long-range synchronization displayed different spatio-temporal profiles, their cortical projection within an initiation time window overlap and reveal a multifocal fronto-central network. These observations contradict the classical view of sudden generalized synchronous activities in absence epilepsy. Furthermore, they suggest that brain states transition may rely on multi-scale processes involving both local and distant interactions.

Exploring the dynamics of collective synchronizations in large ensembles of brain signals.

Developing methods characterizing the dynamics of synchronization in large ensemble of electromagnetic brain signals has become an important issue. In this article, we review a recently introduced method for analyzing multivariate phase synchronization in brain signals. The approach is based on the equivalence between phase locking and frequency locking in narrow band signals, which allows tracking multivariate phase synchronization in the time-frequency domain as periods of common frequency among multiple channels. The method is illustrated with simulations of multivariate phase dynamics in coupled oscillators and real multichannel electro- and magnetoencephalographic data recorded prior and during epileptic seizures. The reviewed results support the relevance of this method in the context of brain synchronization, in particular to track transient collective dynamics fluctuating in time, frequency and space.