TY - JOUR
T1 - Unifying turbulent dynamics framework distinguishes different brain states
AU - Escrichs, Anira
AU - Perl, Yonatan Sanz
AU - Uribe, Carme
AU - Camara, Estela
AU - Türker, Basak
AU - Pyatigorskaya, Nadya
AU - López-González, Ane
AU - Pallavicini, Carla
AU - Panda, Rajanikant
AU - Annen, Jitka
AU - Gosseries, Olivia
AU - Laureys, Steven
AU - Naccache, Lionel
AU - Sitt, Jacobo D.
AU - Laufs, Helmut
AU - Tagliazucchi, Enzo
AU - Kringelbach, Morten L.
AU - Deco, Gustavo
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85133132076&partnerID=8YFLogxK
U2 - 10.1038/s42003-022-03576-6
DO - 10.1038/s42003-022-03576-6
M3 - Article
C2 - 35768641
AN - SCOPUS:85133132076
SN - 2399-3642
VL - 5
JO - Communications Biology
JF - Communications Biology
IS - 1
M1 - 638
ER -