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 - 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 - 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 - 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 - Keutmann, Sabine A1 - Staat, Manfred A1 - Laack, Walter van T1 - Untersuchung der thermischen Auswirkung von therapeutischem Ultraschall N2 - Zusammenfassung: In der Orthopädie zählt der therapeutische Ultraschall als Mittel zur Prävention und Therapiebegleitung. Er hat mechanische, thermische und physiko-chemische Auswirkungen auf den menschlichen Körper. Um mehr Erkenntnisse über die thermischen Auswirkungen zu erlangen, wurden Versuche an einem Hydrogel-Phantom und an Probanden durchgeführt. Dabei entstand eine signifikante Erwärmung des Gewebes, welche beim Probandenversuch an der Oberfläche und beim Hydrogelversuch in der Tiefe gemessen wurde. Summary: In orthopaedics, therapeutic ultrasound is a tool of prevention and therapy support. It has mechanical, thermal and physico-chemical effects on the human body. Tests with a hydrogel phantom and with human probands have been performed in order to obtain more knowledge about their thermal effects. Both tests measured temperature increases in cell tissue, on the surface with the human proband test and in depth with the hydrogel phantom test. T2 - Research about the thermal effects of therapeutic ultrasound Y1 - 2018 SN - 2193-5793 SN - 2193-5785 (Druckausgabe) VL - 7 IS - 10 SP - 518 EP - 522 PB - Deutscher Ärzte-Verl. CY - Köln 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 - 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 -