@article{NgamgaBialonskiMarwanetal.2016, author = {Ngamga, Eulalie Joelle and Bialonski, Stephan and Marwan, Norbert and Kurths, J{\"u}rgen and Geier, Christian and Lehnertz, Klaus}, title = {Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data}, series = {Physics Letters A}, volume = {380}, journal = {Physics Letters A}, number = {16}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0375-9601}, doi = {10.1016/j.physleta.2016.02.024}, pages = {1419 -- 1425}, year = {2016}, abstract = {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.}, language = {en} } @article{GeierLehnertzBialonski2015, author = {Geier, Christian and Lehnertz, Klaus and Bialonski, Stephan}, title = {Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing}, series = {Frontiers in Human Neuroscience}, journal = {Frontiers in Human Neuroscience}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1662-5161}, doi = {10.3389/fnhum.2015.00462}, year = {2015}, language = {en} } @article{GeierBialonskiElgeretal.2015, author = {Geier, Christian and Bialonski, Stephan and Elger, Christian E. and Lehnertz, Klaus}, title = {How important is the seizure onset zone for seizure dynamics?}, series = {Seizure}, volume = {25}, journal = {Seizure}, issn = {1059-1311}, doi = {10.1016/j.seizure.2014.10.013}, pages = {160 -- 166}, year = {2015}, language = {en} } @article{LehnertzAnsmannBialonskietal.2014, author = {Lehnertz, Klaus and Ansmann, Gerrit and Bialonski, Stephan and Dickten, Henning and Geier, Christian and Porz, Stephan}, title = {Evolving networks in the human epileptic brain}, series = {Physica D: Nonlinear Phenomena}, volume = {267}, journal = {Physica D: Nonlinear Phenomena}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-2789}, doi = {10.1016/j.physd.2013.06.009}, pages = {7 -- 15}, year = {2014}, abstract = {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.}, language = {en} } @article{BialonskiLehnertz2013, author = {Bialonski, Stephan and Lehnertz, Klaus}, title = {Assortative mixing in functional brain networks during epileptic seizures}, series = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, volume = {23}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, number = {3}, doi = {10.1063/1.4821915}, pages = {033139}, year = {2013}, language = {en} } @incollection{BialonskiLehnertz2013, author = {Bialonski, Stephan and Lehnertz, Klaus}, title = {From time series to complex networks: an overview}, series = {Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction}, booktitle = {Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction}, isbn = {978-981-4525-36-7}, doi = {10.1142/9789814525350_0010}, pages = {132 -- 147}, year = {2013}, abstract = {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.}, language = {en} } @article{KuhnertBialonskiNoenningetal.2013, author = {Kuhnert, Marie-Therese and Bialonski, Stephan and Noenning, Nina and Mai, Heinke and Hinrichs, Hermann and Helmstaedter, Christoph and Lehnertz, Klaus}, title = {Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks}, series = {Plos one}, volume = {8}, journal = {Plos one}, number = {11}, publisher = {PLOS}, address = {San Francisco}, doi = {10.1371/journal.pone.0080273}, pages = {e80273}, year = {2013}, abstract = {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.}, language = {en} } @article{BialonskiWendlerLehnertz2011, author = {Bialonski, Stephan and Wendler, Martin and Lehnertz, Klaus}, title = {Unraveling spurious properties of interaction networks with tailored random networks}, series = {Plos one}, volume = {6}, journal = {Plos one}, number = {8}, publisher = {Plos}, address = {San Francisco}, doi = {10.1371/journal.pone.0022826}, pages = {e22826}, year = {2011}, abstract = {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 Erd{\"o}s-R{\´e}nyi 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.}, language = {en} } @incollection{LehnertzBialonskiHorstmannetal.2010, author = {Lehnertz, Klaus and Bialonski, Stephan and Horstmann, Marie-Therese and Krug, Dieter and Rothkegel, Alexander and Staniek, Matth{\"a}us and Wagner, Tobias}, title = {Epilepsy}, series = {Reviews of Nonlinear Dynamics and Complexity, Volume 2}, booktitle = {Reviews of Nonlinear Dynamics and Complexity, Volume 2}, publisher = {Wiley-VCH}, isbn = {9783527628001}, doi = {10.1002/9783527628001.ch5}, pages = {159 -- 200}, year = {2010}, language = {en} } @article{HorstmannBialonskiNoenningetal.2010, author = {Horstmann, Marie-Therese and Bialonski, Stephan and Noenning, Nina and Mai, Heinke and Prusseit, Jens and Wellmer, J{\"o}rg and Hinrichs, Hermann and Lehnertz, Klaus}, title = {State dependent properties of epileptic brain networks: Comparative graph-theoretical analyses of simultaneously recorded EEG and MEG}, series = {Clinical Neurophysiology}, volume = {121}, journal = {Clinical Neurophysiology}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1388-2457}, doi = {10.1016/j.clinph.2009.10.013}, pages = {172 -- 185}, year = {2010}, 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.}, language = {en} }