Article
Computer Science, Artificial Intelligence
Jing Chen, Min Xiao, Youhong Wan, Chengdai Huang, Fengyu Xu
Summary: This article investigates a large-scale neural network model with a ring-hub structure using fractional-order delayed differential equations. The stability and Hopf bifurcation of the model are analyzed by obtaining the characteristic equation of the linearized model and providing numerical examples to support the theoretical results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Chemistry, Applied
Ruiyu Zhong, Yujie Liang, Fei Huang, Shinuo Liang, Shengwei Liu
Summary: In this study, a 1D/2D Schottky junction photocatalyst was fabricated by growing 1D crystalline g-C3N4 nanorods along the c-axis and assembling them onto a 2D Ti3C2Tx substrate. The optimized hybrid photocatalyst exhibited significantly enhanced photocatalytic CO2 reduction activity and selectivity towards CH4, compared to the conventional 2D/2D catalysts.
CHINESE JOURNAL OF CATALYSIS
(2023)
Article
Construction & Building Technology
Dong Hyuk Yi, Cheol Soo Park
Summary: The study proposes using model evidence as an objective index for selecting the optimal hypothesis of unidentifiable parameters in Bayesian inference of building energy models. It shows that higher model evidence leads to posterior values closer to true values; however, there is no significant relationship between model prediction error and the accuracy of posterior inference.
ENERGY AND BUILDINGS
(2021)
Review
Chemistry, Multidisciplinary
Lan Yuan, Ming-Yu Qi, Zi-Rong Tang, Yi-Jun Xu
Summary: The photocatalytic reduction of CO2 to solar fuels and fine chemicals is a promising approach to increase energy supply and reduce greenhouse gas emissions. Recent developments integrating CO2 valorization with selective organic synthesis into one reaction system show efficient utilization of photogenerated electrons and holes for sustainable economic and social development goals.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2021)
Article
Mathematics, Interdisciplinary Applications
Karthikeyan Rajagopal, Sajad Jafari, Chunbiao Li, Anitha Karthikeyan, Prakash Duraisamy
Summary: The introduction of magnetic flux coupling in neuron models has shown to suppress spiral waves in networks, while considering delayed asymmetric electrical synapse coupling is more effective at suppressing spiral waves compared to magnetic flux coupling.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Zeric Tabekoueng Njitacke, Nestor Tsafack, Balamurali Ramakrishnan, Kartikeyan Rajagopal, Jacques Kengne, Jan Awrejcewicz
Summary: This paper investigates the influence of electromagnetic flux on the dynamics of a model of heterogeneous coupled neurons, revealing hidden firing activities and hysteretic dynamics through numerical simulations. By utilizing Hamilton energy and the Helmholtz theorem, the characteristics of the coupled neurons are demonstrated, and the model is validated and encrypted using digital implementation and compressive sensing techniques.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Engineering, Environmental
Xiyan Mu, Jia Liu, Hui Wang, Lilai Yuan, Chengju Wang, Yingren Li, Jing Qiu
Summary: The study found that exposure to BPF inhibited the cognitive ability of zebrafish, while increasing the number of microglia and decreasing the number of neurons in the brain. In addition, BPF exposure triggered significant inflammatory response and enhanced phagocytic activity, in which microglia played a dominant role. Furthermore, BPF also affected specific neurological diseases such as movement disorders and neuromuscular diseases, but with different involved genes.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoyu Zhang, Chuandong Li, Hongfei Li, Jing Xu
Summary: This article addresses the synchronization issue for coupled neural networks with mixed couplings using delayed impulsive control. Novel delayed impulsive differential inequalities involving distributed-delay-dependent impulses are proposed, and sufficient criteria and distributed-delay-dependent impulsive controller are derived for CNNs with different topologies. With the use of matrix decomposition techniques, low-dimensional criteria suitable for large scale CNN applications are set out, and the theoretical results are validated through numerical examples involving various cases.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Ze Tang, Deli Xuan, Ju H. Park, Yan Wang, Jianwen Feng
Summary: This paper investigates the exponential synchronization problem on a class of coupled heterogeneous neural networks with hybrid time-varying delays by introducing a distributed pinning control strategy. Sufficient conditions for achieving exponential quasi-synchronization are obtained by applying the concept of average impulsive intervals, the extended comparison principle of impulsive systems, and vector norm techniques. Two different situations of exponential synchronization are considered based on the different roles of impulsive effects in network synchronizing.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Physics, Multidisciplinary
Karthikeyan Rajagopal, Shirin Panahi, Zahra Shourgashti, Anitha Karthikeyan, Iqtadar Hussain
Summary: This paper investigates the effect of time delays in suppressing wave turbulence in a neuronal network. By analyzing a multi-layer lattice with time delays, it is found that considering time delays in both layers can effectively suppress waves, and using multiple time delays in both layers can eliminate spiral waves with lower time delays.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2022)
Article
Chemistry, Multidisciplinary
D. Faust Akl, D. Poier, S. C. D'Angelo, T. P. Araujo, V Tulus, O. Safonova, S. Mitchell, R. Marti, G. Guillen-Gosalbez, J. Perez-Ramirez
Summary: The Pd-Cu catalysed Sonogashira coupling is a significant strategy for C-C bond formation. Traditional homogeneous systems are not reusable, but using heterogeneous catalysts based on isolated metal atoms allows for efficient Pd utilization. Recovering and reusing Pd atoms anchored on nitrogen-doped carbon is possible. The hybrid homogeneous-heterogeneous catalytic process has a smaller footprint compared to the purely homogeneous process.
Article
Mathematics
Yilin Li, Chengbo Yi, Jianwen Feng, Jingyi Wang
Summary: This paper explores the quasi-synchronization of a general class of heterogeneous neural networks using an event-based impulsive control strategy. Instead of the traditional average impulsive interval (AII) method, an event-triggered mechanism (ETM) is employed to determine the impulsive instants, eliminating the subjectivity in selecting the controlling sequence. Furthermore, considering the inevitable communication delay between instruction allocation and execution, an ETM centered on communication delays and aperiodic sampling is proposed to avoid Zeno behavior.
Article
Engineering, Mechanical
Kuan Lu, Yulin Jin, Panfeng Huang, Fan Zhang, Haopeng Zhang, Chao Fu, Yushu Chen
Summary: In this paper, the POD method is applied for dimension reduction in a dual rotor-bearing experiment rig, with established dynamical model and verified efficiency. The results provide engineering guidance for actual dual rotor-bearing systems.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Mathematics, Interdisciplinary Applications
Dongli Duan, Xue Bai, Yisheng Rong, Gege Hou, Jiale Hang
Summary: A new dimension reduction method is proposed to predict the state of individual nodes, explore the behavior pattern of different dynamic models in the network, and quantify the responses of the network states in terms of its own structure and external disturbances.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Mathematics
Shaomin Li, Haoyu Wei, Xiaoyu Lei
Summary: This paper investigates the variable selection and dispersion estimation for heterogeneous NBR models, which models the dispersion parameter as a function. The proposed double regression and double Li-penalty are used, and the oracle inequalities for the lasso estimators are proven. The consistency and convergence rate of the estimators are theoretical guarantees for further statistical inference.
Article
Neurosciences
Mikhael Azilinon, Julia Makhalova, Wafaa Zaaraoui, Samuel Medina Villalon, Patrick Viout, Tangi Roussel, Mohamed M. El Mendili, Ben Ridley, Jean-Philippe Ranjeva, Fabrice Bartolomei, Viktor Jirsa, Maxime Guye
Summary: Whole brain ionic and metabolic imaging is a potential tool for brain disease characterization. This study used sodium MRI and H-1-MR spectroscopic imaging to evaluate changes in epileptogenic networks. The study found a significant increase in sodium signal and a decrease in NAA levels in the epileptogenic zone.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Spase Petkoski, Petra Ritter, Viktor K. Jirsa
Summary: Diffusion-weighted magnetic resonance imaging (MRI) data is used to analyze the structural connectivity of the brain at different ages. The study finds that there is a significant decrease in streamlines in frontal regions and long inter-hemispheric links. The average length of tracts also decreases, but clustering remains unaffected. Age-related changes in functional connectivity (FC) are identified through functional MRI, indicating a more stable dynamic functional connectivity (dFC) but wider range and variance of metaconnectivity (MC) features.
Article
Neurosciences
Emahnuel Troisi Lopez, Roberta Minino, Marianna Liparoti, Arianna Polverino, Antonella Romano, Rosa De Micco, Fabio Lucidi, Alessandro Tessitore, Enrico Amico, Giuseppe Sorrentino, Viktor Jirsa, Pierpaolo Sorrentino
Summary: The clinical connectome fingerprint (CCF) is a method for assessing brain dynamics and can identify individuals based on brain networks. This study examines the performance of CCF in Parkinson's disease (PD) patients and healthy controls. It finds that PD patients have reduced identifiability compared to controls, and this reduction can be used to predict motor impairment. The findings suggest that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.
HUMAN BRAIN MAPPING
(2023)
Article
Clinical Neurology
Gian Marco Duma, Alberto Danieli, Giovanni Mento, Valerio Vitale, Raffaella Scotto Opipari, Viktor Jirsa, Paolo Bonanni, Pierpaolo Sorrentino
Summary: This study used neuronal avalanches to compare basal brain activity between patients with temporal lobe epilepsy (TLE) and controls. It was found that the propagation patterns of neuronal avalanches were altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.
Article
Behavioral Sciences
Borana Dollomaja, Julia Makhalova, Huifang Wang, Fabrice Bartolomei, Viktor Jirsa, Christophe Bernard
Summary: The mechanisms behind the generation and propagation of status epilepticus (SE) in the brain remain unknown. An individualized approach is needed to analyze seizures at the whole brain level. Personalized brain models using the Epileptor mathematical construct in The Virtual Brain (TVB) were used to study SE at the whole brain scale. By replicating SEEG recording patterns, the study found that SE propagation correlates with the patient's structural connectome and depends on the overall network state, highlighting its emergent property. This individual brain virtualization approach has the potential to develop novel interventional strategies to halt SE.
EPILEPSY & BEHAVIOR
(2023)
Article
Clinical Neurology
Viktor Jirsa, Huifang Wang, Paul Triebkorn, Meysam Hashemi, Jayant Jha, Jorge Gonzalez-Martinez, Maxime Guye, Julia Makhalova, Fabrice Bartolomei
Summary: Individuals with drug-resistant focal epilepsy can benefit from surgical treatment, but a presurgical evaluation is necessary to assess the feasibility of surgery without causing neurological deficits. Virtual brains, created using data from MRI, offer a computer simulation of seizures and brain imaging signals. By combining virtual brains with machine learning, it is possible to estimate the epileptogenic zone and understand seizure onset dynamics. However, the current models have limitations such as low spatial resolution, and further research is needed to validate their predictive power and potential clinical applications.
Article
Computer Science, Artificial Intelligence
Meysam Hashemi, Anirudh N. Vattikonda, Jayant Jha, Viktor Sip, Marmaduke M. Woodman, Fabrice Bartolomei, Viktor K. Jirsa
Summary: Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders.
Article
Neurosciences
Damien Depannemaecker, Aitakin Ezzati, Huifang E. Wang, Viktor Jirsa, Christophe Bernard
Summary: Epilepsy is a complex disease that can be studied using theoretical and computational models. Theoretical frameworks help classify seizures based on their dynamical properties, while computational models have potential for clinical applications. These models can provide insights into seizure mechanisms and aid in developing accurate diagnostic and personalized medicine tools. Considering glial cells is important in understanding epilepsy, and this type of approach provides valuable knowledge.
NEUROBIOLOGY OF DISEASE
(2023)
Article
Cell Biology
Yonatan Sanz Perl, Carla Pallavicini, Juan Piccinini, Athena Demertzi, Vincent Bonhomme, Charlotte Martial, Rajanikant Panda, Naji Alnagger, Jitka Annen, Olivia Gosseries, Agustin Ibanez, Helmut Laufs, Jacobo D. Sitt, Viktor K. Jirsa, Morten L. Kringelbach, Steven Laureys, Gustavo Deco, Enzo Tagliazucchi
Summary: Researchers use whole-brain modeling, data augmentation, and deep learning to determine a mapping representing states of consciousness in a low-dimensional space. They reveal an orderly trajectory from wakefulness to patients with brain injury in a latent space, where coordinates represent metrics related to functional modularity and structure-function coupling. The effects of model perturbations are investigated, providing a geometrical interpretation for the stability and reversibility of states.
Article
Mathematical & Computational Biology
Matthew P. Szuromi, Viktor K. Jirsa, William C. Stacey
Summary: Electrical stimulation is a popular method for terminating epileptic seizures, but its efficacy is inconsistent. A computational model of seizure dynamics shows that different bursting classes have different responses to aborting stimulation. The model predicts the optimal termination method for each dynamic class.
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2023)
Article
Neurosciences
Pierpaolo Sorrentino, Emahnuel Troisi Lopez, Antonella Romano, Carmine Granata, Marie Constance Corsi, Giuseppe Sorrentino, Viktor Jirsa
Summary: This study demonstrates that the non-linear part of brain signals carries individual-specific information, playing a crucial role in differentiation. By using neuronal avalanches to characterize fast dynamics between individuals, and comparing with Pearson's correlation, the study shows that selecting the moments and places where neuronal avalanches spread can improve differentiation.
Article
Neuroimaging
Lorenzo Cipriano, Emahnuel Troisi Lopez, Marianna Liparoti, Roberta Minino, Antonella Romano, Arianna Polverino, Francesco Ciaramella, Michele Ambrosanio, Simona Bonavita, Viktor Jirsa, Giuseppe Sorrentino, Pierpaolo Sorrentino
Summary: Brain connectome fingerprinting is a valid approach in assessing individual connectivity and predicting clinical impairment in neurodegenerative diseases. However, its performance and clinical utility in MS field have not yet been investigated.
NEUROIMAGE-CLINICAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Daniela Gandolfi, Jonathan Mapelli, Sergio M. G. Solinas, Paul Triebkorn, Egidio D'Angelo, Viktor Jirsa, Michele Migliore
Summary: A computational method is proposed to generate a full-scale dataset of neurons in the CA1 region of the human hippocampus, providing a resource for the neuroscience community. The method includes the reconstruction of the CA1 region and the generation of a connectivity matrix based on morphological properties of hippocampal neurons.
NATURE COMPUTATIONAL SCIENCE
(2023)
Review
Psychiatry
Gunter Schumann, Ole A. Andreassen, Tobias Banaschewski, Vince D. Calhoun, Nicholas Clinton, Sylvane Desrivieres, Ragnhild Eek Brandlistuen, Jianfeng Feng, Soeren Hese, Esther Hitchen, Per Hoffmann, Tianye Jia, Viktor Jirsa, Andre F. Marquand, Frauke Nees, Markus M. Noethen, Gaia Novarino, Elli Polemiti, Markus Ralser, Michael Rapp, Kerstin Schepanski, Tamara Schikowski, Mel Slater, Peter Sommer, Bernd Carsten Stahl, Paul M. Thompson, Sven Twardziok, Dennis van der Meer, Henrik Walter, Lars Westlye
Summary: Climate change, pollution, urbanization, socioeconomic inequality, and the COVID-19 pandemic have affected brain health. Research using innovative data integration tools can identify brain mechanisms underlying environment-related mental illness and inform innovative treatments. This research will lead to the development of objective biomarkers and evidence-based interventions to improve outcomes of environment-related mental illness.
Meeting Abstract
Mathematical & Computational Biology
Pierpaolo Sorrentino, Spase Petkoski, Fabio Baselice, Viktor Jirsa, Maddalena Sparaco, Emahnuel Troisi Lopez, Elisabetta Signoriello, Simona Bonavita, Maria Agnese Pirozzi, Mario Quarantelli, Giuseppe Sorrentino
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2023)