Epilepsy is a neurological disease characterized by epileptic seizures, which commonly manifest with pronounced frequency and amplitude changes in the EEG signal. In the case of focal seizures, initially localized pathological activity spreads from a so-called "onset zone" to a wider network of brain areas. Chimeras, defined as states of simultaneously occurring coherent and incoherent dynamics in symmetrically coupled networks are increasingly invoked for characterization of seizures. In particular, chimera-like states have been observed during the transition from a normal (asynchronous) to a seizure (synchronous) network state. However, chimeras in epilepsy have only been investigated with respect to the varying phases of oscillators. We propose a novel method to capture the characteristic pronounced changes in the recorded EEG amplitude during seizures by estimating chimera-like states directly from the signals in a frequency- and time-resolved manner. We test the method on a publicly available intracranial EEG dataset of 16 patients with focal epilepsy. We show that the proposed measure, titled Amplitude Entropy, is sensitive to the altered brain dynamics during seizure, demonstrating its significant increases during seizure as compared to before and after seizure. This finding is robust across patients, their seizures, and different frequency bands. In the future, Amplitude Entropy could serve not only as a feature for seizure detection, but also help in characterizing amplitude chimeras in other networked systems with characteristic amplitude dynamics.

Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague 182 00 Czech Republic

Department of Physiology 2nd Faculty of Medicine Charles University Prague 150 06 Czech Republic

Institute of Neuroinformatics University of Zurich and ETH Zurich Zurich Switzerland

National Institute of Mental Health Klecany 250 67 Czech Republic

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Pecora, L. M. & Carroll, T. L. Synchronization in chaotic systems. PubMed DOI

Pikovsky, A., Rosenblum, M. & Kurths, J.

Engel, A. K. & Singer, W. Temporal binding and the neural correlates of sensory awareness. PubMed DOI

Ward, L. M. Synchronous neural oscillations and cognitive processes. PubMed DOI

Uhlhaas, P. J. & Singer, W. Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology. PubMed DOI

Shorvon, S. et al. (eds)

Fisher, R. S. et al. ILAE Official Report: A practical clinical definition of epilepsy. PubMed DOI

Penfield, W. & Jasper, H. Epilepsy and the functional anatomy of the human brain. DOI

Mormann, F., Lehnertz, K., David, P. & Elger, C. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. DOI

Mormann, F. et al. Epileptic seizures are preceded by a decrease in synchronization. PubMed DOI

Espinoso, A. & Andrzejak, R. G. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients. PubMed DOI

Kuhlmann, L. et al. Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. PubMed DOI

Jiruska, P. et al. Synchronization and desynchronization in epilepsy: controversies and hypotheses: Synchronization in epilepsy. PubMed DOI PMC

Jouny, C. C. & Bergey, G. K. Characterization of early partial seizure onset: Frequency, complexity and entropy. PubMed DOI PMC

Mormann, F., Andrzejak, R. G., Elger, C. E. & Lehnertz, K. Seizure prediction: The long and winding road. PubMed DOI

Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B. & Zaveri, H. P. Seizure prediction - ready for a new era. PubMed DOI

Stam, C. J. Modern network science of neurological disorders. PubMed DOI

Moraes, M. F. D., de Castro Medeiros, D., Mourao, F. A. G., Cancado, S. A. V. & Cota, V. R. Epilepsy as a dynamical system, a most needed paradigm shift in epileptology. PubMed DOI

Kalitzin, S. et al. Epilepsy as a manifestation of a multistate network of oscillatory systems. PubMed DOI

Engel, J., Stern, J. M., Bragin, A. & Mody, I. PubMed PMC

Da Silva, F. H. L., Gorter, J. A. & Wadman, W. J. Epilepsy as a dynamic disease of neuronal networks. PubMed

Dallmer-Zerbe, I., Jiruska, P. & Hlinka, J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. PubMed DOI

Zakharova, A.

Kuramoto, Y. & Battogtokh, D. Coexistence of coherence and incoherence in nonlocally coupled phase oscillators.

Abrams, D. M. & Strogatz, S. H. Chimera states for coupled oscillators. PubMed DOI

Parastesh, F. et al. Chimeras. DOI

Ferré, M. Critical visit to the chimera world. DOI

Andrzejak, R. G., Rummel, C., Mormann, F. & Schindler, K. All together now: Analogies between chimera state collapses and epileptic seizures. PubMed DOI PMC

Lainscsek, C., Rungratsameetaweemana, N., Cash, S. S. & Sejnowski, T. J. Cortical chimera states predict epileptic seizures. PubMed DOI PMC

Gerster, M. et al. FitzHugh-Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena. PubMed DOI

Chouzouris, T. et al. Chimera states in brain networks: Empirical neural vs. modular fractal connectivity. PubMed DOI

Škoch, A. et al. Human brain structural connectivity matrices-ready for modelling. PubMed DOI PMC

Onojima, T. & Kitajo, K. A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a kalman filter. PubMed DOI

Zakharova, A., Kapeller, M. & Schöll, E. Chimera death: Symmetry breaking in dynamical networks. PubMed DOI

Zakharova, A., Kapeller, M. & Schöll, E. Amplitude chimeras and chimera death in dynamical networks. DOI

Fisher, R. S., Scharfman, H. E. & DeCurtis, M. How can we identify ictal and interictal abnormal activity? PubMed PMC

Cámpora, N. E., Mininni, C. J., Kochen, S. & Lew, S. E. Seizure localization using pre ictal phase-amplitude coupling in intracranial electroencephalography. PubMed DOI PMC

Rungratsameetaweemana, N. et al. Brain network dynamics codify heterogeneity in seizure evolution. PubMed DOI PMC

SWEC, I. B. & ISL, E. Z. SWEC-ETHZ iEEG Database.

Burrello, A., Schindler, K., Benini, L. & Rahimi, A. One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing. In

Burrello, A., Schindler, K., Benini, L. & Rahimi, A. Hyperdimensional computing with local binary patterns: one-shot learning of seizure onset and identification of ictogenic brain regions using short-time iEEG recordings. PubMed DOI

Banerjee, T., Biswas, D., Ghosh, D., Schöll, E. & Zakharova, A. Networks of coupled oscillators: From phase to amplitude chimeras. PubMed DOI

Huang, N. E.

Freeman, W. J. Origin, structure, and role of background eeg activity part 1 analytic amplitude. PubMed DOI

Shannon, C. E. A mathematical theory of communication. DOI

Schindler, K., Leung, H., Elger, C. E. & Lehnertz, K. Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. PubMed DOI

Inc., T. M. Matlab version: 9.13.0 (r2020b) (2020).

West, B. T., Welch, K. B. & Galecki, A. T.

Perucca, P., Dubeau, F. & Gotman, J. Intracranial electroencephalographic seizure-onset patterns: Effect of underlying pathology. PubMed DOI

Espinoso, A., Leguia, M. G., Rummel, C., Schindler, K. & Andrzejak, R. G. The part and the whole: How single nodes contribute to large-scale phase-locking in functional EEG networks. PubMed DOI

Dallmer-Zerbe, I. et al. Computational modeling allows unsupervised classification of epileptic brain states across species. PubMed DOI PMC

Chang, W.-C. et al. Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations. PubMed DOI PMC

Maturana, M. I. et al. Critical slowing down as a biomarker for seizure susceptibility. PubMed DOI PMC

Rummel, C. et al. Resected brain tissue, seizure onset zone and quantitative EEG measures: Towards prediction of post-surgical seizure control. PubMed DOI PMC

Goodfellow, M. et al. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery. PubMed DOI PMC

Kudlacek, J. et al. Long-term seizure dynamics are determined by the nature of seizures and the mutual interactions between them. PubMed DOI

Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents-eeg, ecog, lfp and spikes. PubMed DOI PMC

Young, J. J. et al. Quantitative signal characteristics of electrocorticography and stereoelectroencephalography: The effect of contact depth. PubMed DOI PMC

Mathews, C. G., Lesku, J. A., Lima, S. L. & Amlaner, C. J. Asynchronous eye closure as an anti-predator behavior in the western fence lizard (sceloporus occidentalis). DOI

Rattenborg, N. C., Amlaner, C. J. & Lima, S. L. Behavioral, neurophysiological and evolutionary perspectives on unihemispheric sleep. PubMed DOI

Haugland, S. W. The changing notion of chimera states, a critical review.

Haugland, S. W., Schmidt, L. & Krischer, K. Self-organized alternating chimera states in oscillatory media. PubMed DOI PMC

Ramlow, L. et al. Partial synchronization in empirical brain networks as a model for unihemispheric sleep. DOI

Majhi, S., Bera, B. K., Ghosh, D. & Perc, M. Chimera states in neuronal networks: A review. PubMed DOI

Kang, L., Tian, C., Huo, S. & Liu, Z. A two-layered brain network model and its chimera state. PubMed DOI PMC

Hizanidis, J., Kouvaris, N. E., Zamora-López, G., Díaz-Guilera, A. & Antonopoulos, C. G. Chimera-like states in modular neural networks. PubMed DOI PMC

Omel’chenko, O. E. The mathematics behind chimera states. DOI

Saggio, M. L. et al. A taxonomy of seizure dynamotypes. PubMed DOI PMC

Fisher, R. S. et al. Operational classification of seizure types by the international league against epilepsy: Position paper of the ilae commission for classification and terminology. PubMed DOI

Burelo, K., Sharifshazileh, M., Indiveri, G. & Sarnthein, J. Automatic detection of high-frequency oscillations with neuromorphic spiking neural networks. PubMed DOI PMC