Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Clusella, Pau
• dc.contributor.author Deco, Gustavo
• dc.contributor.author Kringelbach, Morten L.
• dc.contributor.author Ruffini, Giulio
• dc.contributor.author García Ojalvo, Jordi
• dc.date.accessioned 2023-06-14T13:47:27Z
• dc.date.available 2023-06-14T13:47:27Z
• dc.date.issued 2023
• dc.description.abstract Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown. In this work we establish a simple framework for the emergence of complex brain dynamics, including high-dimensional chaos and travelling waves. The model consists of a complex network of 90 brain regions, whose structural connectivity is obtained from tractography data. The activity of each brain area is governed by a Jansen neural mass model and we normalize the total input received by each node so it amounts the same across all brain areas. This assumption allows for the existence of an homogeneous invariant manifold, i.e., a set of different stationary and oscillatory states in which all nodes behave identically. Stability analysis of these homogeneous solutions unveils a transverse instability of the synchronized state, which gives rise to different types of spatiotemporal dynamics, such as chaotic alpha activity. Additionally, we illustrate the ubiquity of this route towards complex spatiotemporal activity in a network of next generation neural mass models. Altogehter, our results unveil the bifurcation landscape that underlies the emergence of function from structure in the brain.
• dc.description.sponsorship PC, GD, GR, and JGO have received funding from the Future and Emerging Technologies Programme (FET) of the European Union’s Horizon 2020 research and innovation programme (project NEUROTWIN, grant agreement No 101017716). JGO also acknowledges financial support from the Spanish Ministry of Science and Innovation and FEDER (grant PID2021-127311NB-I00), by the “Maria de Maeztu” Programme for Units of Excellence in R&D (grant CEX2018-000792-M), and by the Generalitat de Catalunya (ICREA Academia programme). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• dc.format.mimetype application/pdf
• dc.identifier.citation Clusella P, Deco G, Kringelbach ML, Ruffini G, Garcia-Ojalvo J. Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks. PLoS Comput Biol. 2023 Apr 12;19(4):e1010781. DOI: 10.1371/journal.pcbi.1010781
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1010781
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/57164
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLoS Comput Biol. 2023 Apr 12;19(4):e1010781
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-127311NB-I00
• dc.rights © 2023 Clusella et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Neurons
• dc.subject.keyword Neural networks
• dc.subject.keyword Eigenvalues
• dc.subject.keyword Traveling waves
• dc.subject.keyword Eigenvectors
• dc.subject.keyword Membrane potential
• dc.subject.keyword Manifolds
• dc.subject.keyword Simulation and modeling
• dc.title Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion