The presynaptic active zone protein RIM1α controls epileptogenesis following status epilepticus.

To ensure operation of synaptic transmission within an appropriate dynamic range, neurons have evolved mechanisms of activity-dependent plasticity, including changes in presynaptic efficacy. The multidomain protein RIM1α is an integral component of the cytomatrix at the presynaptic active zone and has emerged as key mediator of presynaptically expressed forms of synaptic plasticity. We have therefore addressed the role of RIM1α in aberrant cellular plasticity and structural reorganization after an episode of synchronous neuronal activity pharmacologically induced in vivo [status epilepticus (SE)]. Post-SE, all animals developed spontaneous seizure events, but their frequency was dramatically increased in RIM1α-deficient mice (RIM1α(-/-)). We found that in wild-type mice (RIM1α(+/+)) SE caused an increase in paired-pulse facilitation in the CA1 region of the hippocampus to the level observed in RIM1α(-/-) mice before SE. In contrast, this form of short-term plasticity was not further enhanced in RIM1α-deficient mice after SE. Intriguingly, RIM1α(-/-) mice showed a unique pattern of selective hilar cell loss (i.e., endfolium sclerosis), which so far has not been observed in a genetic epilepsy animal model, as well as less severe astrogliosis and attenuated mossy fiber sprouting. These findings indicate that the decrease in release probability and altered short- and long-term plasticity as present in RIM1α(-/-) mice result in the formation of a hyperexcitable network but act in part neuroprotectively with regard to neuropathological alterations associated with epileptogenesis. In summary, our results suggest that presynaptic plasticity and proper function of RIM1α play an important part in a neuron's adaptive response to aberrant electrical activity.

Identification of preseizure States in epilepsy: a data-driven approach for multichannel EEG recordings.

The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure - so-called measure profiles - for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.

Symbolic transfer entropy: inferring directionality in biosignals.

Inferring directional interactions from biosignals is of crucial importance to improve understanding of dynamical interdependences underlying various physiological and pathophysiological conditions. We here present symbolic transfer entropy as a robust measure to infer the direction of interactions between multidimensional dynamical systems. We demonstrate its performance in quantifying driver-responder relationships in a network of coupled nonlinear oscillators and in the human epileptic brain.

Synchronization phenomena in human epileptic brain networks.

Epilepsy is a malfunction of the brain that affects over 50 million people worldwide. Epileptic seizures are usually characterized by an abnormal synchronized firing of neurons involved in the epileptic process. In human epilepsy the exact mechanisms underlying seizure generation are still uncertain as are mechanisms underlying seizure spreading and termination. There is now growing evidence that an improved understanding of the epileptic process can be achieved through the analysis of properties of epileptic brain networks and through the analysis of interactions in such networks. In this overview, we summarize recent methodological developments to assess synchronization phenomena in human epileptic brain networks and present findings obtained from analyses of brain electromagnetic signals recorded in epilepsy patients.

Transcriptional upregulation of Cav3.2 mediates epileptogenesis in the pilocarpine model of epilepsy.

In both humans and animals, an insult to the brain can lead, after a variable latent period, to the appearance of spontaneous epileptic seizures that persist for life. The underlying processes, collectively referred to as epileptogenesis, include multiple structural and functional neuronal alterations. We have identified the T-type Ca(2+) channel Ca(v)3.2 as a central player in epileptogenesis. We show that a transient and selective upregulation of Ca(v)3.2 subunits on the mRNA and protein levels after status epilepticus causes an increase in cellular T-type Ca(2+) currents and a transitional increase in intrinsic burst firing. These functional changes are absent in mice lacking Ca(v)3.2 subunits. Intriguingly, the development of neuropathological hallmarks of chronic epilepsy, such as subfield-specific neuron loss in the hippocampal formation and mossy fiber sprouting, was virtually completely absent in Ca(v)3.2(-/-) mice. In addition, the appearance of spontaneous seizures was dramatically reduced in these mice. Together, these data establish transcriptional induction of Ca(v)3.2 as a critical step in epileptogenesis and neuronal vulnerability.

Symbolic transfer entropy.

We propose to estimate transfer entropy using a technique of symbolization. We demonstrate numerically that symbolic transfer entropy is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and nonidentical coupled systems. Analyzing multiday, multichannel electroencephalographic recordings from 15 epilepsy patients our approach allowed us to reliably identify the hemisphere containing the epileptic focus without observing actual seizure activity.

State-of-the-art of seizure prediction.

SUMMARY: 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.