Unifying turbulent dynamics framework distinguishes different brain states

Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy...

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Detalles Bibliográficos
Autores: Escrichs, Anira, Sanz Perl Hernandez, Yonatan, Uribe, Carme, Camara, Estela, Türker, Basak, Pyatigorskaya, Nadya, López González, Ane, Pallavicini, Carla, Panda, Rajanikant, Annen, Jitka, Gosseries, Olivia, Laureys, Steven, Naccache, Lionel, Sitt, Jacobo D., Laufs, Helmut, Tagliazucchi, Enzo, Kringelbach, Morten L., Deco, Gustavo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/218045
Acceso en línea:http://hdl.handle.net/11336/218045
Access Level:acceso abierto
Palabra clave:Brain states
Consciousness
turbulent dynamics
whole brain models
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
Descripción
Sumario:Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto’s turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states.