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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9407401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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