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Debates on six controversial topics on the network theory of epilepsy were held during two debate sessions, as part of the International Conference for Technology and Analysis of Seizures, 2019 (ICTALS 2019) convened at the University of Exeter, UK, September 2-5 2019. The debate topics were (1) From pathologic to physiologic: is the epileptic network part of an existing large-scale brain network? (2) Are micro scale recordings pertinent for defining the epileptic network? (3) From seconds to years: do we need all temporal scales to define an epileptic network? (4) Is it necessary to fully define the epileptic network to control it? (5) Is controlling seizures sufficient to control the epileptic network? (6) Does the epileptic network want to be controlled? This article, written by the organizing committee for the debate sessions and the debaters, summarizes the arguments presented during the debates on these six topics. [ABSTRACT FROM AUTHOR]

Purpose Research into epileptic networks has recently allowed deeper insights into the epileptic process. Here we investigated the importance of individual network nodes for seizure dynamics. Methods We analysed intracranial electroencephalographic recordings of 86 focal seizures with different anatomical onset locations. With time-resolved correlation analyses, we derived a sequence of weighted epileptic networks spanning the pre-ictal, ictal, and post-ictal period, and each recording site represents a network node. We assessed node importance with commonly used centrality indices that take into account different network properties. Results A high variability of temporal evolution of node importance was observed, both intra- and interindividually. Nevertheless, nodes near and far off the seizure onset zone (SOZ) were rated as most important for seizure dynamics more often (65% of cases) than nodes from within the SOZ (35% of cases). Conclusion Our findings underline the high relevance of brain outside of the SOZ but within the large-scale epileptic network for seizure dynamics. Knowledge about these network constituents may elucidate targets for individualised therapeutic interventions that aim at preventing seizure generation and spread. [ABSTRACT FROM AUTHOR]

Abstract: Graph-theoretical analyses of complex brain networks is a rapidly evolving field with a strong impact for neuroscientific and related clinical research. Due to a number of confounding variables, however, a reliable and meaningful characterization of particularly functional brain networks is a major challenge. Addressing this problem, we present an analysis approach for weighted networks that makes use of surrogate networks with preserved edge weights or vertex strengths. We first investigate whether characteristics of weighted networks are influenced by trivial properties of the edge weights or vertex strengths (e.g., their standard deviations). If so, these influences are then effectively segregated with an appropriate surrogate normalization of the respective network characteristic. We demonstrate this approach by re-examining, in a time-resolved manner, weighted functional brain networks of epilepsy patients and control subjects derived from simultaneous EEG/MEG recordings during different behavioral states. We show that this surrogate-assisted analysis approach reveals complementary information about these networks, can aid with their interpretation, and thus can prevent deriving inappropriate conclusions. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Summary: The First International Collaborative Workshop on Seizure Prediction was held at the Department of Epileptology, University of Bonn, in Bonn, Germany on April 24–28, 2002. Organized by the Universities of Pennsylvania and Bonn, and funded by grants from the American Epilepsy Society, the German Section of the International League against Epilepsy, and the German Section of the International Federation of Clinical Neurophysiology, the workshop was attended by 51 researchers from 16 centers in seven countries. There were four major goals for the workshop: (1) to host a one-day didactic session on the science of seizure prediction, with lectures by leaders in the field; (2) to assess the current state of the field by applying current methods used to predict seizures to a shared set of continuous intracranial EEG data and discussing the strengths and weaknesses of each approach; (3) to establish a consensus on minimal data requirements, a common nomenclature, and objective methods for comparing system performance across platforms and laboratories for seizure prediction research; and most importantly (4) to establish a multi-laboratory, international working group dedicated to understanding seizure generation and making on-line, prospective seizure prediction a reality. Following the didactic course, each participating group presented their results, after applying their seizure prediction methods to five common data sets agreed upon in advance and distributed before the meeting. What follows is a description of the shared data set used for analysis, a summary of the major discussion points from the workshop, and points of consensus among the group. The brief discussion serves as a common introduction to the research papers that follow in this issue, and the description of the shared data is referenced in each of these papers. Participants in the workshop are listed at the end of the Conclusions section, in alphabetical order. [Copyright &y& Elsevier]

Abstract: Objective: To test whether increasing synchronization of neuronal activity might be causally related to seizure termination. Methods: Neuronal synchronization was assessed by the relative changes of the eigenvalue spectrum of the equal-time correlation matrix computed from a short window sliding along multi-channel EEGs, recorded with either intracranial or surface electrodes. Results: Synchronization dynamics of six status epilepticus EEG recordings from six patients were assessed. In all six recordings EEG synchronization fluctuated around relatively low levels during ongoing epileptiform activity. Synchronization only persistently increased before, or in one case, at the end of status epilepticus. Ongoing seizure activity stopped without pharmacological intervention in one patient. In four of the five other cases, the persistent increase of synchronization followed administration of anticonvulsant drugs. Conclusions: Our findings support the hypothesis that increasing synchronization of neuronal activity may be considered as an emergent self-regulatory mechanism for seizure termination. Significance: The traditional concept is challenged that increasing neuronal synchronization during epileptic seizures is always pathological and should be suppressed. On the contrary, our findings imply that therapeutic interventions to increase synchronization during seizures might be beneficial. [Copyright &y& Elsevier]

Epileptic seizures are generally unpredictable and arise spontaneously. Patients often report non-specific triggers such as stress or sleep deprivation, but only rarely do seizures occur as a reflex event, in which they are objectively and consistently modulated, precipitated, or inhibited by external sensory stimuli or specific cognitive processes. The seizures triggered by such stimuli and processes in susceptible individuals can have different latencies. Once seizure-suppressing mechanisms fail and a critical mass (the so-called tipping point) of cortical activation is reached, reflex seizures stereotypically manifest with common motor features independent of the physiological network involved. The complexity of stimuli increases from simple sensory to complex cognitive-emotional with increasing age of onset. The topography of physiological networks involved follows the posterior-to-anterior trajectory of brain development, reflecting age-related changes in brain excitability. Reflex seizures and traits probably represent the extremes of a continuum, and understanding of their underlying mechanisms might help to elucidate the transition of normal physiological function to paroxysmal epileptic activity. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To investigate whether functional brain networks of epilepsy patients treated with antiepileptic medication differ from networks of healthy controls even during the seizure-free interval. Methods: We applied different rules to construct binary and weighted networks from EEG and MEG data recorded under a resting-state eyes-open and eyes-closed condition from 21 epilepsy patients and 23 healthy controls. The average shortest path length and the clustering coefficient served as global statistical network characteristics. Results: Independent on the behavioral condition, epileptic brains exhibited a more regular functional network structure. Similarly, the eyes-closed condition was characterized by a more regular functional network structure in both groups. The amount of network reorganization due to behavioral state changes was similar in both groups. Consistent findings could be achieved for networks derived from EEG but hardly from MEG recordings, and network construction rules had a rather strong impact on our findings. Conclusions: Despite the locality of the investigated processes epileptic brain networks differ in their global characteristics from non-epileptic brain networks. Further methodological developments are necessary to improve the characterization of disturbed and normal functional networks. Significance: An increased regularity and a diminished modulation capability appear characteristic of epileptic brain networks. [Copyright &y& Elsevier]

Abstract: We present a cellular neuronal network (CNN) based approach to classify magnetic resonance images with and without hippocampal or Ammon’s horn sclerosis (AHS) in the medial temporal lobe. A CNN combines the architecture of cellular automata and artificial neural networks and is an array of locally coupled nonlinear electrical circuits or cells, which is capable of processing a large amount of information in parallel and in real time. Using an exemplary database that consists of a large number of volumes of interest extracted from T1-weighted magnetic resonance images from 144 subjects we here demonstrate that the network allows to classify brain tissue with respect to the presence or absence of mesial temporal sclerosis. Results indicate the general feasibility of CNN-based computer-aided systems for diagnosis and classification of images generated by medical imaging systems. [Copyright &y& Elsevier]

Abstract: We present a client–server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system''s performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost. [Copyright &y& Elsevier]

Abstract: 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 5–30min 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 240min 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. [Copyright &y& Elsevier]

Focal brain lesions such as cortical dysplasia and glioneural tumors generate epileptic activity and thus may be synaptically connected with normal cortex. To test this hypothesis, we compared event-related potentials recorded directly from the medial temporal lobe (MTL) and a dysplastic lesion in eight patients with intractable temporal lobe epilepsy. The P3 component, related to visual target detection, showed different peak latencies in four patients and a larger intralesional amplitude compared to established anterior-MTL-generators in two patients. Semantic processing was identified by the N400 component and showed a different latency in four patients and larger intralesional amplitudes in two patients. These results are compatible with the hypothesis that cortical lesions interact with synaptic pathways related to cognitive functions such as visual target detection, and verbal processing. [Copyright &y& Elsevier]