@article{KaulenSchwabedalSchneideretal.2022, author = {Kaulen, Lars and Schwabedal, Justus T. C. and Schneider, Jules and Ritter, Philipp and Bialonski, Stephan}, title = {Advanced sleep spindle identification with neural networks}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, number = {Article number: 7686}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-022-11210-y}, pages = {1 -- 10}, year = {2022}, abstract = {Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.}, language = {en} } @article{BialonskiAllefeldWellmeretal.2008, author = {Bialonski, Stephan and Allefeld, C. and Wellmer, J. and Elger, C. and Lehnertz, K.}, title = {An approach to identify synchronization clusters within the epileptic network}, series = {Klinische Neurophysiologie}, volume = {39}, journal = {Klinische Neurophysiologie}, number = {1}, doi = {10.1055/s-2008-1072881}, pages = {A79}, year = {2008}, 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} } @article{SchwabedalSippelBrandtetal.2018, author = {Schwabedal, Justus T. C. and Sippel, Daniel and Brandt, Moritz D. and Bialonski, Stephan}, title = {Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning}, doi = {10.48550/arXiv.1809.08443}, year = {2018}, abstract = {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.}, language = {en} } @article{GriegerSchwabedalWendeletal.2021, author = {Grieger, Niklas and Schwabedal, Justus T. C. and Wendel, Stefanie and Ritze, Yvonne and Bialonski, Stephan}, title = {Automated scoring of pre-REM sleep in mice with deep learning}, series = {Scientific Reports}, volume = {11}, journal = {Scientific Reports}, number = {Art. 12245}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-021-91286-0}, year = {2021}, abstract = {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.}, language = {en} } @article{BialonskiAnsmannKantz2015, author = {Bialonski, Stephan and Ansmann, Gerrit and Kantz, Holger}, title = {Data-driven prediction and prevention of extreme events in a spatially extended excitable system}, series = {Physical Review E}, volume = {92}, journal = {Physical Review E}, number = {4}, issn = {2470-0053}, doi = {10.1103/PhysRevE.92.042910}, pages = {042910}, year = {2015}, language = {en} } @article{BialonskiGrieger2023, author = {Bialonski, Stephan and Grieger, Niklas}, title = {Der KI-Chatbot ChatGPT: Eine Herausforderung f{\"u}r die Hochschulen}, series = {Die neue Hochschule}, volume = {2023}, journal = {Die neue Hochschule}, number = {1}, publisher = {HLB}, address = {Bonn}, issn = {0340-448X}, doi = {10.5281/zenodo.7533758}, pages = {24 -- 27}, year = {2023}, abstract = {Essays, Gedichte, Programmcode: ChatGPT generiert automatisch Texte auf bisher unerreicht hohem Niveau. Dieses und nachfolgende Systeme werden nicht nur die akademische Welt nachhaltig ver{\"a}ndern.}, language = {de} } @article{AllefeldBialonski2007, author = {Allefeld, Carsten and Bialonski, Stephan}, title = {Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains}, series = {Physical Review E}, volume = {76}, journal = {Physical Review E}, number = {6}, issn = {2470-0053}, doi = {10.1103/PhysRevE.76.066207}, pages = {066207}, year = {2007}, language = {en} } @article{KarnatakKantzBialonski2017, author = {Karnatak, Rajat and Kantz, Holger and Bialonski, Stephan}, title = {Early warning signal for interior crises in excitable systems}, series = {Physical Review E}, volume = {96}, journal = {Physical Review E}, number = {4}, issn = {2470-0053}, doi = {10.1103/PhysRevE.96.042211}, pages = {042211}, year = {2017}, language = {en} } @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{SchindlerBialonskiHorstmannetal.2008, author = {Schindler, Kaspar A. and Bialonski, Stephan and Horstmann, Marie-Therese and Elger, Christian E. and Lehnertz, Klaus}, title = {Evolving functional network properties and synchronizability during human epileptic seizures}, series = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, volume = {18}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, number = {3}, issn = {1089-7682}, doi = {10.1063/1.2966112}, pages = {033119}, year = {2008}, 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{MulhernBialonskiKantz2015, author = {Mulhern, Colm and Bialonski, Stephan and Kantz, Holger}, title = {Extreme events due to localization of energy}, series = {Physical Review E}, volume = {91}, journal = {Physical Review E}, number = {1}, issn = {2470-0053}, doi = {10.1103/PhysRevE.91.012918}, pages = {012918}, year = {2015}, language = {en} } @article{BialonskiHorstmannLehnertz2010, author = {Bialonski, Stephan and Horstmann, Marie-Therese and Lehnertz, Klaus}, title = {From brain to earth and climate systems: Small-world interaction networks or not?}, series = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, volume = {20}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, number = {1}, publisher = {AIP Publishing}, address = {Melville, NY}, issn = {1089-7682}, doi = {10.1063/1.3360561}, pages = {013134}, year = {2010}, abstract = {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.}, 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{BialonskiLehnertz2006, author = {Bialonski, Stephan and Lehnertz, Klaus}, title = {Identifying phase synchronization clusters in spatially extended dynamical systems}, series = {Physical Review E}, volume = {74}, journal = {Physical Review E}, number = {5}, issn = {2470-0053}, doi = {10.1103/PhysRevE.74.051909}, pages = {051909}, year = {2006}, 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{BialonskiWellmerElgeretal.2006, author = {Bialonski, Stephan and Wellmer, J{\"o}rg and Elger, Christian E. and Lehnertz, Klaus}, title = {Interictal focus localization in neocortical lesional epilepsies with synchronization cluster analysis}, series = {Epilepsia}, volume = {47}, journal = {Epilepsia}, issn = {0013-9580}, pages = {36}, year = {2006}, language = {en} } @article{BialonskiSchindlerElgeretal.2008, author = {Bialonski, Stephan and Schindler, K. and Elger, C. E. and Lehnertz, Klaus}, title = {Lateralized characteristics of the evolution of EEG correlation during focal onset seizures: a mechanism to prevent secondary generalization?}, series = {Epilepsia}, volume = {49}, journal = {Epilepsia}, issn = {0013-9580}, pages = {11 -- 11}, year = {2008}, abstract = {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.}, language = {en} } @article{RingersBialonskiEgeetal.2023, author = {Ringers, Christa and Bialonski, Stephan and Ege, Mert and Solovev, Anton and Hansen, Jan Niklas and Jeong, Inyoung and Friedrich, Benjamin M. and Jurisch-Yaksi, Nathalie}, title = {Novel analytical tools reveal that local synchronization of cilia coincides with tissue-scale metachronal waves in zebrafish multiciliated epithelia}, series = {eLife}, volume = {12}, journal = {eLife}, publisher = {eLife Sciences Publications}, issn = {2050-084X}, doi = {10.7554/eLife.77701}, pages = {27 Seiten}, year = {2023}, abstract = {Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on 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. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, 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, i.e., cilia colliding with each other, 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 coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.}, language = {en} }