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On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spat...

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Autores principales: Omidvarnia, Amir, Liégeois, Raphaël, Amico, Enrico, Preti, Maria Giulia, Zalesky, Andrew, Van De Ville, Dimitri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407401/
https://www.ncbi.nlm.nih.gov/pubmed/36010812
http://dx.doi.org/10.3390/e24081148
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author Omidvarnia, Amir
Liégeois, Raphaël
Amico, Enrico
Preti, Maria Giulia
Zalesky, Andrew
Van De Ville, Dimitri
author_facet Omidvarnia, Amir
Liégeois, Raphaël
Amico, Enrico
Preti, Maria Giulia
Zalesky, Andrew
Van De Ville, Dimitri
author_sort Omidvarnia, Amir
collection PubMed
description Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.
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spelling pubmed-94074012022-08-26 On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI Omidvarnia, Amir Liégeois, Raphaël Amico, Enrico Preti, Maria Giulia Zalesky, Andrew Van De Ville, Dimitri Entropy (Basel) Article Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition. MDPI 2022-08-18 /pmc/articles/PMC9407401/ /pubmed/36010812 http://dx.doi.org/10.3390/e24081148 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Omidvarnia, Amir
Liégeois, Raphaël
Amico, Enrico
Preti, Maria Giulia
Zalesky, Andrew
Van De Ville, Dimitri
On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
title On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
title_full On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
title_fullStr On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
title_full_unstemmed On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
title_short On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
title_sort on the spatial distribution of temporal complexity in resting state and task functional mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407401/
https://www.ncbi.nlm.nih.gov/pubmed/36010812
http://dx.doi.org/10.3390/e24081148
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