RT Journal Article
T1 What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics.
A1 Soler-Toscano, Fernando
A1 Galadí, Javier A
A1 Escrichs, Anira
A1 Sanz Perl, Yonatan
A1 López-González, Ane
A1 Sitt, Jacobo D
A1 Annen, Jitka
A1 Gosseries, Olivia
A1 Thibaut, Aurore
A1 Panda, Rajanikant
A1 Esteban, Francisco J
A1 Laureys, Steven
A1 Kringelbach, Morten L
A1 Langa, José A
A1 Deco, Gustavo
AB The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or 'information structure'), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.
YR 2022
FD 2022-09-06
LK http://hdl.handle.net/10668/20412
UL http://hdl.handle.net/10668/20412
LA en
DS RISalud
RD Apr 7, 2025