TY - JOUR A1 - Bialonski, Stephan A1 - Ansmann, Gerrit A1 - Kantz, Holger T1 - Data-driven prediction and prevention of extreme events in a spatially extended excitable system JF - Physical Review E Y1 - 2015 U6 - http://dx.doi.org/10.1103/PhysRevE.92.042910 SN - 2470-0053 VL - 92 IS - 4 SP - 042910 ER - TY - JOUR A1 - Mulhern, Colm A1 - Bialonski, Stephan A1 - Kantz, Holger T1 - Extreme events due to localization of energy JF - Physical Review E Y1 - 2015 U6 - http://dx.doi.org/10.1103/PhysRevE.91.012918 SN - 2470-0053 VL - 91 IS - 1 SP - 012918 ER - TY - JOUR A1 - Geier, Christian A1 - Lehnertz, Klaus A1 - Bialonski, Stephan T1 - Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing JF - Frontiers in Human Neuroscience Y1 - 2015 U6 - http://dx.doi.org/10.3389/fnhum.2015.00462 SN - 1662-5161 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Geier, Christian A1 - Bialonski, Stephan A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - How important is the seizure onset zone for seizure dynamics? JF - Seizure Y1 - 2015 U6 - http://dx.doi.org/10.1016/j.seizure.2014.10.013 SN - 1059-1311 VL - 25 SP - 160 EP - 166 ER - TY - JOUR A1 - Lehnertz, Klaus A1 - Ansmann, Gerrit A1 - Bialonski, Stephan A1 - Dickten, Henning A1 - Geier, Christian A1 - Porz, Stephan T1 - Evolving networks in the human epileptic brain JF - Physica D: Nonlinear Phenomena N2 - Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives. Y1 - 2014 U6 - http://dx.doi.org/10.1016/j.physd.2013.06.009 SN - 0167-2789 VL - 267 SP - 7 EP - 15 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bialonski, Stephan A1 - Lehnertz, Klaus T1 - Assortative mixing in functional brain networks during epileptic seizures JF - Chaos: An Interdisciplinary Journal of Nonlinear Science Y1 - 2013 U6 - http://dx.doi.org/10.1063/1.4821915 VL - 23 IS - 3 SP - 033139 ER - TY - CHAP A1 - Bialonski, Stephan A1 - Lehnertz, Klaus T1 - From time series to complex networks: an overview T2 - Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction N2 - The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach. Y1 - 2013 SN - 978-981-4525-36-7 U6 - http://dx.doi.org/10.1142/9789814525350_0010 SP - 132 EP - 147 ER - TY - BOOK A1 - Bialonski, Stephan T1 - Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions Y1 - 2012 N1 - Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Diss., 2012 PB - Universitäts- und Landesbibliothek Bonn CY - Bonn ER - TY - CHAP A1 - Bialonski, Stephan T1 - Are interaction clusters in epileptic networks predictive of seizures? T2 - Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics Y1 - 2016 SN - 978-143983886-0 SP - 349 EP - 355 PB - CRC Press ER - TY - JOUR A1 - Bialonski, Stephan A1 - Wellmer, Jörg A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - Interictal focus localization in neocortical lesional epilepsies with synchronization cluster analysis JF - Epilepsia Y1 - 2006 SN - 0013-9580 VL - 47 SP - 36 ER - TY - CHAP A1 - Lehnertz, Klaus A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Krug, Dieter A1 - Rothkegel, Alexander A1 - Staniek, Matthäus A1 - Wagner, Tobias T1 - Epilepsy T2 - Reviews of Nonlinear Dynamics and Complexity, Volume 2 Y1 - 2010 SN - 9783527628001 U6 - http://dx.doi.org/10.1002/9783527628001.ch5 SP - 159 EP - 200 PB - Wiley-VCH ER - TY - JOUR A1 - Lehnertz, Klaus A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Krug, Dieter A1 - Rothkegel, Alexander A1 - Staniek, Matthäus A1 - Wagner, Tobias T1 - Synchronization phenomena in human epileptic brain networks JF - Journal of neuroscience methods Y1 - 2009 U6 - http://dx.doi.org/10.1016/j.jneumeth.2009.05.015 SN - 0165-0270 VL - 183 IS - 1 SP - 42 EP - 48 ER - TY - CHAP A1 - Osterhage, Hannes A1 - Bialonski, Stephan A1 - Staniek, Matthäus A1 - Schindler, Kaspar A1 - Wagner, Tobias A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - Bivariate and multivariate time series analysis techniques and their potential impact for seizure prediction T2 - Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications Y1 - 2008 SN - 978-3-527-62519-2 U6 - http://dx.doi.org/10.1002/9783527625192.ch15 SP - 189 EP - 208 PB - Wiley-VCH CY - Weinheim ER - TY - JOUR A1 - Schindler, Kaspar A. A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - Evolving functional network properties and synchronizability during human epileptic seizures JF - Chaos: An Interdisciplinary Journal of Nonlinear Science Y1 - 2008 U6 - http://dx.doi.org/10.1063/1.2966112 SN - 1089-7682 VL - 18 IS - 3 SP - 033119 ER - TY - JOUR A1 - Bialonski, Stephan A1 - Allefeld, C. A1 - Wellmer, J. A1 - Elger, C. A1 - Lehnertz, K. T1 - An approach to identify synchronization clusters within the epileptic network JF - Klinische Neurophysiologie Y1 - 2008 U6 - http://dx.doi.org/10.1055/s-2008-1072881 VL - 39 IS - 1 SP - A79 ER - TY - JOUR A1 - Bialonski, Stephan A1 - Schindler, K. A1 - Elger, C. E. A1 - Lehnertz, Klaus T1 - Lateralized characteristics of the evolution of EEG correlation during focal onset seizures: a mechanism to prevent secondary generalization? JF - Epilepsia N2 - Rationale: Previous studies [Topolnik et al., Cereb Cortex 2003; 13: 883; Schindler et al., Brain 2007; 130: 65] indicate that the termination of focal onset seizures may be causally related to an increase of global neuronal correlation during the second half of the seizures. This increase was observed to occur earlier in complex partial seizures than in secondarily generalized seizures. We here address the question whether such an increase of neuronal correlation prior to seizure end is indeed a global phenomenon, involving both hemispheres or whether there are side-specific differences. Methods: We analyzed 20 focal onset seizures (10 complex partial, 10 secondarily generalized seizures) recorded in 13 patients who underwent presurgical evaluation of focal epilepsies of different origin. EEG was recorded intracranially from bilaterally implanted subdural strip and intrahippocampal depth electrodes. Utilizing a moving window approach, we investigated the evolution of the maximum cross correlation for all channel combinations during seizures. For each moving window the mean value of the maximum cross correlation (MCC) between all electrode contacts was computed separately for each hemisphere. After normalization of seizure durations, MCC values of the ipsi- and contralateral hemisphere for all seizures were determined. Results: We observed that the MCC of the contralateral hemisphere in complex partial seizures increased during the first half of the seizure, whereas, for the same time interval, the MCC of the ipsilateral hemisphere even declined below the level of the pre-seizure period. In contrast, no significant differences between both hemispheres could be observed for secondarily generalized seizures where both hemispheres showed a simultaneous increase of MCC during the second half of the seizures. The level of MCC for the contralateral hemisphere was higher for complex partial seizures than for secondarily generalized seizures during the first half of the seizure. Conclusions: Our findings indicate that there are indeed lateralized differences in the evolution of global neuronal correlation during complex partial and secondarily generalized seizures. The observed contralateral increase of neuronal correlation during complex partial seizures might indicate an emerging self-organizing mechanism for preventing the spread of seizure activity. Y1 - 2008 SN - 0013-9580 VL - 49 SP - 11 EP - 11 ER - TY - JOUR A1 - Allefeld, Carsten A1 - Bialonski, Stephan T1 - Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains JF - Physical Review E Y1 - 2007 U6 - http://dx.doi.org/10.1103/PhysRevE.76.066207 SN - 2470-0053 VL - 76 IS - 6 SP - 066207 ER - TY - JOUR A1 - Lehnertz, Klaus A1 - Mormann, Florian A1 - Osterhage, Hannes A1 - Andy, Müller A1 - Prusseit, Jens A1 - Chernihovskyi, Anton A1 - Staniek, Matthäus A1 - Krug, Dieter A1 - Bialonski, Stephan A1 - Elger, Christian E. T1 - State-of-the-art of seizure prediction JF - Journal of Clinical Neurophysiology Y1 - 2007 U6 - http://dx.doi.org/10.1097/WNP.0b013e3180336f16 SN - 1537-1603 VL - 24 IS - 2 SP - 147 EP - 153 ER - TY - JOUR A1 - Bialonski, Stephan A1 - Lehnertz, Klaus T1 - Identifying phase synchronization clusters in spatially extended dynamical systems JF - Physical Review E Y1 - 2006 U6 - http://dx.doi.org/10.1103/PhysRevE.74.051909 SN - 2470-0053 VL - 74 IS - 5 SP - 051909 ER - TY - JOUR A1 - Schneider, Jules A1 - Schwabedal, Justus T. C. A1 - Bialonski, Stephan T1 - Schlafspindeln – Funktion, Detektion und Nutzung als Biomarker für die psychiatrische Diagnostik JF - Der Nervenarzt N2 - Hintergrund: Die Schlafspindel ist ein Graphoelement des Elektroenzephalogramms (EEG), das im Leicht- und Tiefschlaf beobachtet werden kann. Veränderungen der Spindelaktivität wurden für verschiedene psychiatrische Erkrankungen beschrieben. Schlafspindeln zeigen aufgrund ihrer relativ konstanten Eigenschaften Potenzial als Biomarker in der psychiatrischen Diagnostik. Methode: Dieser Beitrag liefert einen Überblick über den Stand der Wissenschaft zu Eigenschaften und Funktionen der Schlafspindeln sowie über beschriebene Veränderungen der Spindelaktivität bei psychiatrischen Erkrankungen. Verschiedene methodische Ansätze und Ausblicke zur Spindeldetektion werden hinsichtlich deren Anwendungspotenzial in der psychiatrischen Diagnostik erläutert. Ergebnisse und Schlussfolgerung: Während Veränderungen der Spindelaktivität bei psychiatrischen Erkrankungen beschrieben wurden, ist deren exaktes Potenzial für die psychiatrische Diagnostik noch nicht ausreichend erforscht. Diesbezüglicher Erkenntnisgewinn wird in der Forschung gegenwärtig durch ressourcenintensive und fehleranfällige Methoden zur manuellen oder automatisierten Spindeldetektion ausgebremst. Neuere Detektionsansätze, die auf Deep-Learning-Verfahren basieren, könnten die Schwierigkeiten bisheriger Detektionsmethoden überwinden und damit neue Möglichkeiten für die praktisch KW - Schlafspindeldetektion KW - Psychiatrische Biomarker KW - · Psychiatrische Erkrankungen/Diagnostik KW - Elektroenzephalographie KW - Deep Learning Y1 - 2022 U6 - http://dx.doi.org/10.1007/s00115-022-01340-z SN - 1433-0407 SP - 1 EP - 8 PB - Springer CY - Berlin, Heidelberg ER -