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Extracting default mode network based on graph neural network for resting state fMRI study
Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406295/ https://www.ncbi.nlm.nih.gov/pubmed/37555154 http://dx.doi.org/10.3389/fnimg.2022.963125 |
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author | Wang, Donglin Wu, Qiang Hong, Don |
author_facet | Wang, Donglin Wu, Qiang Hong, Don |
author_sort | Wang, Donglin |
collection | PubMed |
description | Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously. |
format | Online Article Text |
id | pubmed-10406295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104062952023-08-08 Extracting default mode network based on graph neural network for resting state fMRI study Wang, Donglin Wu, Qiang Hong, Don Front Neuroimaging Neuroimaging Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously. Frontiers Media S.A. 2022-09-07 /pmc/articles/PMC10406295/ /pubmed/37555154 http://dx.doi.org/10.3389/fnimg.2022.963125 Text en Copyright © 2022 Wang, Wu and Hong. 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 | Neuroimaging Wang, Donglin Wu, Qiang Hong, Don Extracting default mode network based on graph neural network for resting state fMRI study |
title | Extracting default mode network based on graph neural network for resting state fMRI study |
title_full | Extracting default mode network based on graph neural network for resting state fMRI study |
title_fullStr | Extracting default mode network based on graph neural network for resting state fMRI study |
title_full_unstemmed | Extracting default mode network based on graph neural network for resting state fMRI study |
title_short | Extracting default mode network based on graph neural network for resting state fMRI study |
title_sort | extracting default mode network based on graph neural network for resting state fmri study |
topic | Neuroimaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406295/ https://www.ncbi.nlm.nih.gov/pubmed/37555154 http://dx.doi.org/10.3389/fnimg.2022.963125 |
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