Article
Mathematics, Applied
Peihua Feng, Jiayi Yang, Ying Wu, Zhilong Liu
Summary: Chimera, the coexistence of synchronization and non-synchronization in complex networks, has great explanatory power for unihemispheric sleep in birds and mammals. In this study, a coupled nonlinear oscillator system with a modular complex network topology was used to simulate the left and right hemispheres of the brain. The results showed the emergence of stable chimera, alternating chimera, and breathing chimera when changing the coupling strength and connection probability. Furthermore, the study found that the alternating chimera was robust to Gaussian white noise. This research provides deeper insights into the mechanism of brain functions like unihemispheric sleep.
Article
Multidisciplinary Sciences
Sindre W. Haugland, Anton Tosolini, Katharina Krischer
Summary: The text explores the behaviors of coupled oscillators, including synchronization and incoherence, as well as the discovery of "chimera states" and their relationship with synchronization and asynchronization. It demonstrates that globally coupled identical oscillators can express a wider range of coexistence patterns, including chimeras.
NATURE COMMUNICATIONS
(2021)
Article
Physics, Fluids & Plasmas
Kazuha Itabashi, Quoc Hoan Tran, Yoshihiko Hasegawa
Summary: By proposing a topological approach to characterize the phase dynamics in coupled oscillators, this study gains insights into the collective dynamics of complex systems. The method extracts quantitative features describing the shape of the phase data and extends these features to time-variant characteristics. Combining these features with the kernel method allows for characterization of multiclustered synchronized dynamics and qualitative explanation of chimera states.
Article
Engineering, Biomedical
Judie Tabbal, Aya Kabbara, Maxime Yochum, Mohamad Khalil, Mahmoud Hassan, Pascal Benquet
Summary: This study provides a quantitative assessment of the advantages and limitations of EEG/MEG source-space network analysis and introduces a complete framework to optimize the entire pipeline. Using a computational model, the performance of key steps involved in the analysis process was evaluated, and significant variability among tested algorithms was observed. The findings highlight the importance of using brain models to evaluate different steps and parameters in EEG/MEG analysis.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Mathematics, Applied
Guillermo H. Goldsztein, Lars Q. English, Emma Behta, Hillel Finder, Alice N. Nadeau, Steven H. Strogatz
Summary: Using theory, experiment, and simulation, this study examines the dynamics of two coupled metronomes on a moving platform. The experiments show that the platform motion is damped by a dry friction force of Coulomb type, contrary to previous assumptions of viscous linear friction force. A new mathematical model is developed based on previous models but with a different treatment of friction. The model analysis reveals various long-term behaviors, including synchronization, phase locking, and suppression, shedding light on the dynamics of coupled metronomes.
Article
Physics, Fluids & Plasmas
Biswabibek Bandyopadhyay, Tanmoy Banerjee
Summary: This study investigates the impact of Kerr anharmonicity on the symmetry-breaking phenomena of coupled quantum oscillators, revealing that Kerr nonlinearity hinders the process of symmetry breaking in both cases. The findings provide a means to control and engineer symmetry-breaking states for quantum technology.
Article
Mathematics, Applied
Juan Piccinini, Ignacio Perez Ipinna, Helmut Laufs, Morten Kringelbach, Gustavo Deco, Yonatan Sanz Perl, Enzo Tagliazucchi
Summary: The translation discusses an open problem in neuroscience of understanding complex spatiotemporal dynamics in neural systems. Computational models were used to compare two mechanisms, with the chaotic model showing better performance in capturing multiple observables. The results support the view of the brain as a non-equilibrium system.
Article
Computer Science, Artificial Intelligence
Ahmed Nebli, Mohammed Amine Gharsallaoui, Zeynep Gurler, Islem Rekik, Alzheimers Dis Neuroimaging Initiative
Summary: Graph neural networks (GNNs) have been widely used in various fields such as computer vision and computer-aided diagnosis, but the reproducibility of the discriminative features identified by GNNs is still a concern, especially in clinical applications. This study proposes a reproducibility-based GNN selection framework to quantify the reproducibility by evaluating the shared discriminative features among different models.
Article
Physiology
Qiang Li, Kelly C. Larosz, Dingding Han, Peng Ji, Juergen Kurths
Summary: This paper quantitatively demonstrates that the chimera states in two coupled networks have very small basins of attraction, in contrast to the simplified model. In addition, a bimodal distribution of basin stability after perturbations and the emergence of chimera states in brain networks are investigated.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Multidisciplinary Sciences
Madison Lewis, Tales Santini, Nicholas Theis, Brendan Muldoon, Katherine Dash, Jonathan Rubin, Matcheri Keshavan, Konasale Prasad
Summary: This study investigated the structural covariance network (SCN) of first-episode antipsychotic-naive psychosis (FEAP) using graph theoretical methods. The results showed that FEAP patients had higher betweenness centrality (BC) and lower degree in all three morphometric features, suggesting lower network resilience. Moreover, the disintegration of the network with fewer attacks was associated with greater negative symptom severity.
SCIENTIFIC REPORTS
(2023)
Article
Engineering, Electrical & Electronic
Fatemeh Parastesh, Mohadeseh Shafiei Kafraj, Yaser Merrikhi, Karthikeyan Rajagopal, Sajad Jafari
Summary: This paper investigates the chimera state in different networks, including empirical networks reconstructed from brain data and a constructed random network. By computing structural properties and applying order parameters and stability functions, the study reveals the transition sequence from asynchronization to chimeras and eventually to synchronization in all networks under increasing coupling strength.
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
(2023)
Review
Biochemistry & Molecular Biology
Alexis Porter, Sihan Fei, Katherine S. F. Damme, Robin Nusslock, Caterina Gratton, Vijay A. A. Mittal
Summary: Neuroimaging techniques can differentiate individuals with psychotic disorders from healthy controls, with resting state functional connectivity (rs-FC) showing slight advantages in predictive ability. However, there is substantial heterogeneity in the results and potential bias, necessitating more studies with external prediction and large samples to further validate the findings.
MOLECULAR PSYCHIATRY
(2023)
Article
Physics, Fluids & Plasmas
Bojun Li, Nariya Uchida
Summary: The study shows that the multichimera state disappears when the phase delay parameter alpha exceeds a critical value, but reappears when further increased. A transition from multichimera to multitwisted states is observed, involving five collective phases.
Article
Chemistry, Physical
Marco Benedetti, Enrico Ventura, Enzo Marinari, Giancarlo Ruocco, Francesco Zamponi
Summary: This study compares the Hebbian unlearning algorithm with a symmetric perceptron algorithm and finds that they have similar properties in terms of basin size and convergence region. The study also proposes a geometric interpretation of Hebbian unlearning and explores its potential applications in memory storage in materials.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Marco Benedetti, Enrico Ventura, Enzo Marinari, Giancarlo Ruocco, Francesco Zamponi
Summary: This study compared the Hebbian unlearning algorithm with a supervised algorithm in neural networks, finding similarities in stability of stored memories and learning paths. The proposed geometric interpretation explained the optimal performance of the Hebbian unlearning algorithm.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Multidisciplinary Sciences
Lucia Jajcay, David Tomecek, Jiri Horacek, Filip Spaniel, Jaroslav Hlinka
Summary: This study used graph-theoretical methods to analyze resting-state functional magnetic resonance imaging data of 90 healthy subjects and found that the left hemisphere is more modular compared to the right hemisphere. This result was consistent across different binarization thresholds, whether the two hemispheres were thresholded together or separately.
Article
Mathematics, Applied
Arthur Matsuo Yamashita Rios de Sousa, Jaroslav Hlinka
Summary: We extended Elsinger's work on chi-squared tests for independence using ordinal patterns and investigated a general class of m-dependent ordinal patterns processes. We proposed a test method to quantify the range of serial dependence in a process, and applied it to epilepsy electroencephalography time series data.
Article
Neuroimaging
Barbora Rehak Buckova, Jan Mares, Antonin Skoch, Jakub Kopal, Jaroslav Tintera, Robert Dineen, Kamila Rasova, Jaroslav Hlinka
Summary: This study used statistical and machine learning techniques to analyze multimodal neuroimaging data in order to discriminate between multiple sclerosis patients and healthy controls and predict motor disability scores in the patients. The study found a relationship between white and grey matter changes and motor impairment in multiple sclerosis.
BRAIN IMAGING AND BEHAVIOR
(2023)
Article
Engineering, Multidisciplinary
Anna Pidnebesna, Iveta Fajnerova, Jiri Horacek, Jaroslav Hlinka
Summary: Sparse linear regression methods, such as LASSO and the Dantzig selector, are widely used in engineering, including medical imaging. A novel approach is proposed that utilizes the entire family of sparse regression solutions and the theory of mixtures to estimate the probability of neuronal activation at each timepoint. Extensive numerical simulations show that this new method outperforms standard approaches in various realistic scenarios.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Mathematics, Applied
Zahra Dayani, Fatemeh Parastesh, Fahimeh Nazarimehr, Karthikeyan Rajagopal, Sajad Jafari, Eckehard Schoell, Juergen Kurths
Summary: In this paper, a time-varying coupling function is proposed to enhance synchronization in complex networks of oscillators. The stability of synchronization is analyzed using the master stability approach, considering the largest Lyapunov exponent of the linearized variational equations as the master stability function dependent on the network eigenvalues. Diffusive single-variable coupling is assumed for the oscillators, and the coupling with the smallest local Lyapunov exponent is selected for each time interval. The obtained coupling function decreases the critical coupling parameter, leading to enhanced synchronization. Moreover, it achieves faster synchronization and increased robustness. Illustratively, the optimal coupling function is found for three networks of chaotic Rossler, Chen, and Chua systems, showing enhanced synchronization.
Article
Mathematics, Applied
Max Thiele, Rico Berner, Peter A. A. Tass, Eckehard Schoell, Serhiy Yanchuk
Summary: This study presents a framework for describing the emergence of recurrent synchronization in complex networks with adaptive interactions. The phenomenon is manifested by temporal episodes of coherent and incoherent dynamics that alternate recurrently. Asymmetric adaptation rules and temporal separation between adaptation and individual node dynamics are identified as key features for the emergence of recurrent synchronization.
Article
Mathematics, Applied
S. G. Ngueuteu Mbouna, Tanmoy Banerjee, Eckehard Schoell, Rene Yamapi
Summary: We investigate networks of coupled oscillators governed by fractional-order versions of van der Pol and Rayleigh oscillators and report the presence of diverse amplitude chimeras and oscillation death patterns. For the first time, amplitude chimeras are observed in a network of van der Pol oscillators. We also identify and characterize a form of amplitude chimera, called damped amplitude chimera, where the incoherent region(s) continuously increase in size over time and the oscillations of drifting units are continuously damped until they reach steady state. Decreasing the order of fractional derivatives increases the lifetime of classical amplitude chimeras and leads to a transition to damped amplitude chimeras. This study demonstrates that lower fractional derivatives reduce synchronization propensity and promote oscillation death phenomena that were not observed in networks of integer-order oscillators, such as solitary oscillation death and chimera death patterns.
Review
Clinical Neurology
Isa Dallmer-Zerbe, Premysl Jiruska, Jaroslav Hlinka
Summary: Epilepsy, a common neurological disorder, often does not respond to current treatments. Therefore, a paradigm shift in the diagnosis and treatment of epilepsy is necessary. Computational modeling and network dynamics theory have proven to be effective in personalized epilepsy network modeling and neurostimulation therapy. This article reviews recent progress in these areas.
Article
Physics, Multidisciplinary
Jan Fialkowski, Serhiy Yanchuk, Igor M. Sokolov, Eckehard Schoell, Georg A. Gottwald, Rico Berner
Summary: Phase transitions in equilibrium and nonequilibrium systems are important in the natural sciences. In dynamical networks, phase transitions organize changes in the collective behavior of coupled dynamical units. We demonstrate two distinct nonequilibrium phase transitions in a finite-size adaptive network, where the network's connectivity structure changes over time and coevolves with the nodes' dynamical state. Depending on the defects in the internal frequency distribution, we observe either an abrupt single-step transition or a more gradual multistep transition. This observation resembles heterogeneous nucleation.
PHYSICAL REVIEW LETTERS
(2023)
Article
Mathematics, Applied
Elena Rybalova, Vasilii Nechaev, Eckehard Schoell, Galina Strelkova
Summary: This study numerically investigates the impact of additive Gaussian noise on the spatiotemporal dynamics of ring networks of nonlocally coupled chaotic maps. The results show that the coupling strength range is the widest at the optimal noise level, and chimera states can be observed with a high probability.
Article
Mathematics, Applied
Arthur Matsuo Yamashita Rios de Sousa, Jaroslav Hlinka
Summary: Inferring the dependence structure of complex networks from the observation of the non-linear dynamics of its components is a common but unresolved challenge. Existing methods using the ordinal patterns framework have limitations in their scope of application. We introduce sign patterns as an extension of ordinal patterns, enabling the encoding of longer sequences with fewer symbols. By considering necessary constraints, we derive improved statistical estimates and design an asymptotic chi-squared test to evaluate dependence between time series.
Article
Neurosciences
Jakub Kopal, Jaroslav Hlinka, Elodie Despouy, Luc Valton, Marie Denuelle, Jean-Christophe Sol, Jonathan Curot, Emmanuel J. Barbeau
Summary: This study investigated the dynamics of large-scale brain networks underlying recognition memory. Through analyzing intracranial electroencephalography data, the researchers identified two networks associated with successful recognition. The first network involved the right visual ventral stream and bilateral frontal regions, showing predominantly bottom-up information flow peaking at 115 ms. The second network, mainly in the left anterior hemisphere, exhibited predominantly top-down connectivity peaking at 220 ms. The transition between these networks was accompanied by changes in network topology, from a more segregated to a more integrated state.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Nikola Jajcay, Jaroslav Hlinka
Summary: One interesting aspect of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates. This study aims to characterize and analyze different microstate algorithms, and test the relationship between dynamic microstate properties and the linear characteristics of the underlying EEG signal.
Article
Multidisciplinary Sciences
Isa Dallmer-Zerbe, Nikola Jajcay, Jan Chvojka, Radek Janca, Petr Jezdik, Pavel Krsek, Petr Marusic, Premysl Jiruska, Jaroslav Hlinka
Summary: Current advances in epilepsy treatment aim to personalize and adjust treatment parameters to overcome patient heterogeneity. Computational modeling has been introduced as a diagnostic tool for predicting individual treatment outcomes. In this article, the Wendling model is used to automatically classify epileptic brain states and the results are validated against real data.
SCIENTIFIC REPORTS
(2023)
Article
Physics, Fluids & Plasmas
S. G. Ngueuteu Mbouna, Tanmoy Banerjee, Eckehard Schoell
Summary: In this paper, the study focuses on the investigation of symmetry-breaking phenomena in neuronal networks using simplified versions of the FitzHughNagumo model. The network of FitzHugh-Nagumo oscillators, in its original form, exhibits diverse partial synchronization patterns that are not observed in networks with simplified models. The study reports the discovery of a new type of chimera pattern and a peculiar hybrid state, as well as the emergence of oscillation death in the network. By deriving a reduced model, the transition from spatial chaos to oscillation death via the chimera state with a solitary state is explained. This study deepens the understanding of chimera patterns in neuronal networks.
