What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics.
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...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010412 |
| _version_ | 1811265699332489216 |
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| author | Fernando Soler-Toscano Javier A Galadí Anira Escrichs Yonatan Sanz Perl Ane López-González Jacobo D Sitt Jitka Annen Olivia Gosseries Aurore Thibaut Rajanikant Panda Francisco J Esteban Steven Laureys Morten L Kringelbach José A Langa Gustavo Deco |
| author_facet | Fernando Soler-Toscano Javier A Galadí Anira Escrichs Yonatan Sanz Perl Ane López-González Jacobo D Sitt Jitka Annen Olivia Gosseries Aurore Thibaut Rajanikant Panda Francisco J Esteban Steven Laureys Morten L Kringelbach José A Langa Gustavo Deco |
| author_sort | Fernando Soler-Toscano |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T20:28:44Z |
| format | Article |
| id | doaj.art-b9af8b9441ef49ada4c663659e432a1c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T20:28:44Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-b9af8b9441ef49ada4c663659e432a1c2022-12-22T03:17:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101041210.1371/journal.pcbi.1010412What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics.Fernando Soler-ToscanoJavier A GaladíAnira EscrichsYonatan Sanz PerlAne López-GonzálezJacobo D SittJitka AnnenOlivia GosseriesAurore ThibautRajanikant PandaFrancisco J EstebanSteven LaureysMorten L KringelbachJosé A LangaGustavo DecoThe 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.https://doi.org/10.1371/journal.pcbi.1010412 |
| spellingShingle | Fernando Soler-Toscano Javier A Galadí Anira Escrichs Yonatan Sanz Perl Ane López-González Jacobo D Sitt Jitka Annen Olivia Gosseries Aurore Thibaut Rajanikant Panda Francisco J Esteban Steven Laureys Morten L Kringelbach José A Langa Gustavo Deco What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. PLoS Computational Biology |
| title | What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. |
| title_full | What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. |
| title_fullStr | What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. |
| title_full_unstemmed | What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. |
| title_short | What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. |
| title_sort | what lies underneath precise classification of brain states using time dependent topological structure of dynamics |
| url | https://doi.org/10.1371/journal.pcbi.1010412 |
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