We investigate the relative merit of different linear and nonlinear synchronization measures for a characterization of the spatio-temporal dynamics of the epileptic process. Analyzing long-lasting multichannel electroencephalographic recordings from more than 20 epilepsy patients we show that all measures are able to identify brain regions of pathological synchronization associated with epilepsy, even during the seizure-free interval, and are able to detect a long-lasting transitional preseizure state. These findings render synchronization measures attractive for future prospective studies on seizure prediction.

Theta oscillations in the medial temporal lobe (MTL) of mammals are involved in various functions such as spatial navigation, sensorimotor integration, and cognitive processing. While the theta rhythm was originally assumed to originate in the medial septum, more recent studies suggest autonomous theta generation in the MTL. Although coherence between entorhinal and hippocampal theta activity has been found to infl uence memory formation, it remains unclear whether these two structures can generate theta independently. In this study we analyzed intracranial electroencephalographic (EEG) recordings from 22 patients with unilateral hippocampal sclerosis undergoing presurgical evaluation prior to resection of the epileptic focus. Using a wavelet-based, frequency-band-specifi c measure of phase synchronization, we quantifi ed synchrony between 10 different recording sites along the longitudinal axis of the hippocampal formation in the non-epileptic brain hemisphere. We compared EEG synchrony between adjacent recording sites (i) within the entorhinal cortex, (ii) within the hippocampus, and (iii) between the hippocampus and entorhinal cortex. We observed a signifi cant interregional gap in synchrony for the delta and theta band, indicating the existence of independent delta/theta rhythms in different subregions of the human MTL. The interaction of these rhythms could represent the temporal basis for the information processing required for mnemonic encoding and retrieval.

Measuring the directionality of coupling between dynamical systems is one of the challenging problems in nonlinear time series analysis. We investigate the relative merit of two approaches to assess directionality, one based on phase dynamics modeling and one based on state space topography. We analyze unidirectionally coupled model systems to investigate the ability of the two approaches to detect driver-responder relationships and discuss certain problems and pitfalls. In addition we apply both approaches to the intracranial electroencephalogram (EEG) recorded from one epilepsy patient during the seizure-free interval to demonstrate the general suitability of directionality measures to reflect the pathological interaction of the epileptic focus with other brain areas.

Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available as to how, when, or why a seizure occurs in humans. The fact that seizures occur without warning in the majority of cases is one of the most disabling aspects of epilepsy. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities and quality of life could improve dramatically. The last three decades have witnessed a rapid increase in the development of new EEG analysis techniques that appear to be capable of defining seizure precursors. Since the 1970s, studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena and proof of principle studies via controlled studies to studies on continuous multiday recordings. At present, it is unclear whether prospective algorithms can predict seizures. If prediction algorithms are to be used in invasive seizure intervention techniques in humans, they must be proven to perform considerably better than a random predictor. The authors present an overview of the field of seizure prediction, its history, accomplishments, recent controversies, and potential for future development.

We study directional relationships-in the driver-responder sense-in networks of coupled nonlinear oscillators using a phase modeling approach. Specifically, we focus on the identification of drivers in clusters with varying levels of synchrony, mimicking dynamical interactions between the seizure generating region ͑epileptic focus͒ and other brain structures. We demonstrate numerically that such an identification is not always possible in a reliable manner. Using the same analysis techniques as in model systems, we study multichannel electroencephalographic recordings from two patients suffering from focal epilepsy. Our findings demonstrate thatdepending on the degree of intracluster synchrony-certain subsystems can spuriously appear to be driving others, which should be taken into account when analyzing field data with unknown underlying dynamics.