Revisiting the standard for modeling functional brain network activity: Application to consciousness.

Autor: Grigis A; Université Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France., Gomez C; Université Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France.; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, Gif-sur-Yvette, France., Frouin V; Université Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France., Duchesnay E; Université Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France., Uhrig L; Université Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France.; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, Gif-sur-Yvette, France., Jarraya B; Université Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France.; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, Gif-sur-Yvette, France.; Université Paris-Saclay (UVSQ), Neuroscience Pole, Foch Hospital, Suresnes, France.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Dec 16; Vol. 19 (12), pp. e0314598. Date of Electronic Publication: 2024 Dec 16 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0314598
Abstrakt: Functional connectivity (FC) of resting-state fMRI time series can be estimated using methods that differ in their temporal sensitivity (static vs. dynamic) and the number of regions included in the connectivity estimation (derived from a prior atlas). This paper presents a novel framework for identifying and quantifying resting-state networks using resting-state fMRI recordings. The study employs a linear latent variable model to generate spatially distinct brain networks and their associated activities. It specifically addresses the atlas selection problem, and the statistical inference and multivariate analysis of the obtained brain network activities. The approach is demonstrated on a dataset of resting-state fMRI recordings from monkeys under different anesthetics using static FC. Our results suggest that two networks, one fronto-parietal and cingular and another temporo-parieto-occipital (posterior brain) strongly influences shifts in consciousness, especially between anesthesia and wakefulness. Interestingly, this observation aligns with the two prominent theories of consciousness: the global neural workspace and integrated information theories of consciousness. The proposed method is also able to decipher the level of anesthesia from the brain network activities. Overall, we provide a framework that can be effectively applied to other datasets and may be particularly useful for the study of disorders of consciousness.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Grigis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje