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Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach
Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712628/ https://www.ncbi.nlm.nih.gov/pubmed/34970170 http://dx.doi.org/10.3389/fpsyt.2021.790234 |
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author | Soma, Daiki Hirosawa, Tetsu Hasegawa, Chiaki An, Kyung-min Kameya, Masafumi Hino, Shoryoku Yoshimura, Yuko Nobukawa, Sou Iwasaki, Sumie Tanaka, Sanae Yaoi, Ken Sano, Masuhiko Shiota, Yuka Naito, Nobushige Kikuchi, Mitsuru |
author_facet | Soma, Daiki Hirosawa, Tetsu Hasegawa, Chiaki An, Kyung-min Kameya, Masafumi Hino, Shoryoku Yoshimura, Yuko Nobukawa, Sou Iwasaki, Sumie Tanaka, Sanae Yaoi, Ken Sano, Masuhiko Shiota, Yuka Naito, Nobushige Kikuchi, Mitsuru |
author_sort | Soma, Daiki |
collection | PubMed |
description | Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60–89 months old) and for 25 typically developing (TD) control children (10 girls, 60–91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan–Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band (p = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total t-scores (p = 0.047). Significant relations were also inferred for the Social Awareness (p = 0.008) and Social Cognition (p = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD. |
format | Online Article Text |
id | pubmed-8712628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87126282021-12-29 Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach Soma, Daiki Hirosawa, Tetsu Hasegawa, Chiaki An, Kyung-min Kameya, Masafumi Hino, Shoryoku Yoshimura, Yuko Nobukawa, Sou Iwasaki, Sumie Tanaka, Sanae Yaoi, Ken Sano, Masuhiko Shiota, Yuka Naito, Nobushige Kikuchi, Mitsuru Front Psychiatry Psychiatry Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60–89 months old) and for 25 typically developing (TD) control children (10 girls, 60–91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan–Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band (p = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total t-scores (p = 0.047). Significant relations were also inferred for the Social Awareness (p = 0.008) and Social Cognition (p = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD. Frontiers Media S.A. 2021-12-14 /pmc/articles/PMC8712628/ /pubmed/34970170 http://dx.doi.org/10.3389/fpsyt.2021.790234 Text en Copyright © 2021 Soma, Hirosawa, Hasegawa, An, Kameya, Hino, Yoshimura, Nobukawa, Iwasaki, Tanaka, Yaoi, Sano, Shiota, Naito and Kikuchi. 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 | Psychiatry Soma, Daiki Hirosawa, Tetsu Hasegawa, Chiaki An, Kyung-min Kameya, Masafumi Hino, Shoryoku Yoshimura, Yuko Nobukawa, Sou Iwasaki, Sumie Tanaka, Sanae Yaoi, Ken Sano, Masuhiko Shiota, Yuka Naito, Nobushige Kikuchi, Mitsuru Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
title | Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
title_full | Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
title_fullStr | Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
title_full_unstemmed | Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
title_short | Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
title_sort | atypical resting state functional neural network in children with autism spectrum disorder: graph theory approach |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712628/ https://www.ncbi.nlm.nih.gov/pubmed/34970170 http://dx.doi.org/10.3389/fpsyt.2021.790234 |
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