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Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions
Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized corti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959420/ https://www.ncbi.nlm.nih.gov/pubmed/35356058 http://dx.doi.org/10.3389/fnins.2022.838347 |
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author | Qiu, Wenyuan Ma, Liang Jiang, Tianzi Zhang, Yu |
author_facet | Qiu, Wenyuan Ma, Liang Jiang, Tianzi Zhang, Yu |
author_sort | Qiu, Wenyuan |
collection | PubMed |
description | Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders. |
format | Online Article Text |
id | pubmed-8959420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89594202022-03-29 Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions Qiu, Wenyuan Ma, Liang Jiang, Tianzi Zhang, Yu Front Neurosci Neuroscience Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders. Frontiers Media S.A. 2022-03-09 /pmc/articles/PMC8959420/ /pubmed/35356058 http://dx.doi.org/10.3389/fnins.2022.838347 Text en Copyright © 2022 Qiu, Ma, Jiang and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Qiu, Wenyuan Ma, Liang Jiang, Tianzi Zhang, Yu Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions |
title | Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions |
title_full | Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions |
title_fullStr | Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions |
title_full_unstemmed | Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions |
title_short | Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions |
title_sort | unrevealing reliable cortical parcellation of individual brains using resting-state functional magnetic resonance imaging and masked graph convolutions |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959420/ https://www.ncbi.nlm.nih.gov/pubmed/35356058 http://dx.doi.org/10.3389/fnins.2022.838347 |
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