TY - JOUR A1 - Horstmann, Marie-Therese A1 - Bialonski, Stephan A1 - Noenning, Nina A1 - Mai, Heinke A1 - Prusseit, Jens A1 - Wellmer, Jörg A1 - Hinrichs, Hermann A1 - Lehnertz, Klaus T1 - State dependent properties of epileptic brain networks: Comparative graph–theoretical analyses of simultaneously recorded EEG and MEG JF - Clinical Neurophysiology N2 - 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. Y1 - 2010 U6 - http://dx.doi.org/10.1016/j.clinph.2009.10.013 SN - 1388-2457 VL - 121 IS - 2 SP - 172 EP - 185 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Lehnertz, Klaus T1 - From brain to earth and climate systems: Small-world interaction networks or not? JF - Chaos: An Interdisciplinary Journal of Nonlinear Science N2 - We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization. Y1 - 2010 U6 - http://dx.doi.org/10.1063/1.3360561 SN - 1089-7682 VL - 20 IS - 1 PB - AIP Publishing CY - Melville, NY ER - TY - JOUR A1 - Bialonski, Stephan A1 - Wendler, Martin A1 - Lehnertz, Klaus T1 - Unraveling spurious properties of interaction networks with tailored random networks JF - Plos one N2 - We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis. Y1 - 2011 U6 - http://dx.doi.org/10.1371/journal.pone.0022826 VL - 6 IS - 8 PB - Plos CY - San Francisco 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 - 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 - JOUR A1 - Kuhnert, Marie-Therese A1 - Bialonski, Stephan A1 - Noenning, Nina A1 - Mai, Heinke A1 - Hinrichs, Hermann A1 - Helmstaedter, Christoph A1 - Lehnertz, Klaus T1 - Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks JF - Plos one N2 - Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions. Y1 - 2013 U6 - http://dx.doi.org/10.1371/journal.pone.0080273 VL - 8 IS - 11 PB - PLOS CY - San Francisco 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 - 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 - Ngamga, Eulalie Joelle A1 - Bialonski, Stephan A1 - Marwan, Norbert A1 - Kurths, Jürgen A1 - Geier, Christian A1 - Lehnertz, Klaus T1 - Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data JF - Physics Letters A N2 - We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database. Y1 - 2016 U6 - http://dx.doi.org/10.1016/j.physleta.2016.02.024 SN - 0375-9601 VL - 380 IS - 16 SP - 1419 EP - 1425 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bialonski, Stephan A1 - Caron, David A. A1 - Schloen, Julia A1 - Feudel, Ulrike A1 - Kantz, Holger A1 - Moorthi, Stefanie D. T1 - Phytoplankton dynamics in the Southern California Bight indicate a complex mixture of transport and biology JF - Journal of Plankton Research N2 - The stimulation and dominance of potentially harmful phytoplankton taxa at a given locale and time are determined by local environmental conditions as well as by transport to or from neighboring regions. The present study investigated the occurrence of common harmful algal bloom (HAB) taxa within the Southern California Bight, using cross-correlation functions to determine potential dependencies between HAB taxa and environmental factors, and potential links to algal transport via local hydrography and currents. A simulation study, in which Lagrangian particles were released, was used to assess travel times due to advection by prevailing ocean currents in the bight. Our results indicate that transport of some taxa may be an important mechanism for the expansion of their distributions into other regions, which was supported by mean travel times derived from our simulation study and other literature on ocean currents in the Southern California Bight. In other cases, however, phytoplankton dynamics were rather linked to local environmental conditions, including coastal upwelling events. Overall, our study shows that complex current patterns in the Southern California Bight may contribute significantly to the formation and expansion of HABs in addition to local environmental factors determining the spatiotemporal dynamics of phytoplankton blooms. Y1 - 2016 U6 - http://dx.doi.org/10.1093/plankt/fbv122 SN - 1464-3774 VL - 38 IS - 4 SP - 1077 EP - 1091 PB - Oxford University Press CY - Oxford 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 - Karnatak, Rajat A1 - Kantz, Holger A1 - Bialonski, Stephan T1 - Early warning signal for interior crises in excitable systems JF - Physical Review E Y1 - 2017 U6 - http://dx.doi.org/10.1103/PhysRevE.96.042211 SN - 2470-0053 VL - 96 IS - 4 SP - 042211 ER - TY - JOUR A1 - Schwabedal, Justus T. C. A1 - Sippel, Daniel A1 - Brandt, Moritz D. A1 - Bialonski, Stephan T1 - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning N2 - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle. Y1 - 2018 U6 - http://dx.doi.org/10.48550/arXiv.1809.08443 ER - TY - JOUR A1 - Grieger, Niklas A1 - Schwabedal, Justus T. C. A1 - Wendel, Stefanie A1 - Ritze, Yvonne A1 - Bialonski, Stephan T1 - Automated scoring of pre-REM sleep in mice with deep learning JF - Scientific Reports N2 - Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network. Y1 - 2021 U6 - http://dx.doi.org/10.1038/s41598-021-91286-0 SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 11 IS - Art. 12245 PB - Springer Nature CY - London ER - TY - CHAP A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning T2 - Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021 Y1 - 2021 U6 - http://dx.doi.org/10.48415/2021/fhw5-x128 N1 - SP - 105 EP - 111 PB - Heinrich Heine University CY - Düsseldorf ER - TY - INPR A1 - Ringers, Christa A1 - Bialonski, Stephan A1 - Solovev, Anton A1 - Hansen, Jan N. A1 - Ege, Mert A1 - Friedrich, Benjamin M. A1 - Jurisch-Yaksi, Nathalie T1 - Preprint: Local synchronization of cilia and tissue-scale cilia alignment are sufficient for global metachronal waves T2 - bioRxiv N2 - Motile cilia are hair-like cell extensions present in multiple organs of the body. How cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine here experiments, novel analysis tools, and theory to address this knowledge gap. We investigate collective dynamics of cilia in the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Despite the fact that synchronization is local only, we observed global patterns of traveling metachronal waves across the multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right nose, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment are sufficient to generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping. Y1 - 2021 U6 - http://dx.doi.org/10.1101/2021.11.23.469646 N1 - Veröffentlicht in eLife 12:e77701 (https://doi.org/10.7554/eLife.77701). ER -