What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

Description

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.

Abstract

Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía P20_00592

Abstract

Ministerio de Ciencia, Innovación y Universidades PGC2018-096540-B-I00

Abstract

Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía US-1254251

Abstract

NextGenerationEU Ayudas Margarita Salas, MSALAS-2022-19827

Abstract

Universidad de Jaén PAIUJA-EI_CTS02_2021

Abstract

Fundación Alicia Koplowitz OTR08262-2021

Abstract

Ministerio de Ciencia, Innovación y Universidades PID2019-105772GB-I00 /AEI/10.13039/501100011033

Abstract

Horizonte 2020 Marie Sklodowska-Curie grant 896354

Abstract

Swiss National Science Foundation Sinergia grant no. 170873

Abstract

Fonds de la Recherche Scientifique (FNRS)

Abstract

European Union's Horizon 2020 Human Brain Project SGA3

Abstract

European Union's Horizon 2020 Luminous project H2020-FETOPEN-2014-2015-RIA

Abstract

European Union's FP7 Programme FP7-HEALTH-602150

Abstract

National Natural Science Foundation of China Joint Research Project 81471100

Abstract

H2020 Marie Skłodowska-Curie Actions EU-2020-MSCA-RISE-778234

Additional details