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...
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| Published in | Frontiers in psychiatry Vol. 12; p. 790234 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
14.12.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1664-0640 1664-0640 |
| DOI | 10.3389/fpsyt.2021.790234 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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.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. 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. 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 ( = 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 -scores ( = 0.047). Significant relations were also inferred for the Social Awareness ( = 0.008) and Social Cognition ( = 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. |
| Author | Hirosawa, Tetsu Naito, Nobushige An, Kyung-min Iwasaki, Sumie Tanaka, Sanae Yaoi, Ken Soma, Daiki Kameya, Masafumi Sano, Masuhiko Hino, Shoryoku Nobukawa, Sou Hasegawa, Chiaki Kikuchi, Mitsuru Shiota, Yuka Yoshimura, Yuko |
| AuthorAffiliation | 2 Research Center for Child Mental Development, Kanazawa University , Kanazawa , Japan 5 Department of Computer Science, Chiba Institute of Technology , Narashino , Japan 3 Department of Neuropsychiatry, Ishikawa Prefectural Takamatsu Hospital , Kahoku , Japan 4 Faculty of Education, Institute of Human and Social Sciences, Kanazawa University , Kanazawa , Japan 1 Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University , Kanazawa , Japan |
| AuthorAffiliation_xml | – name: 5 Department of Computer Science, Chiba Institute of Technology , Narashino , Japan – name: 4 Faculty of Education, Institute of Human and Social Sciences, Kanazawa University , Kanazawa , Japan – name: 3 Department of Neuropsychiatry, Ishikawa Prefectural Takamatsu Hospital , Kahoku , Japan – name: 1 Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University , Kanazawa , Japan – name: 2 Research Center for Child Mental Development, Kanazawa University , Kanazawa , Japan |
| Author_xml | – sequence: 1 givenname: Daiki surname: Soma fullname: Soma, Daiki – sequence: 2 givenname: Tetsu surname: Hirosawa fullname: Hirosawa, Tetsu – sequence: 3 givenname: Chiaki surname: Hasegawa fullname: Hasegawa, Chiaki – sequence: 4 givenname: Kyung-min surname: An fullname: An, Kyung-min – sequence: 5 givenname: Masafumi surname: Kameya fullname: Kameya, Masafumi – sequence: 6 givenname: Shoryoku surname: Hino fullname: Hino, Shoryoku – sequence: 7 givenname: Yuko surname: Yoshimura fullname: Yoshimura, Yuko – sequence: 8 givenname: Sou surname: Nobukawa fullname: Nobukawa, Sou – sequence: 9 givenname: Sumie surname: Iwasaki fullname: Iwasaki, Sumie – sequence: 10 givenname: Sanae surname: Tanaka fullname: Tanaka, Sanae – sequence: 11 givenname: Ken surname: Yaoi fullname: Yaoi, Ken – sequence: 12 givenname: Masuhiko surname: Sano fullname: Sano, Masuhiko – sequence: 13 givenname: Yuka surname: Shiota fullname: Shiota, Yuka – sequence: 14 givenname: Nobushige surname: Naito fullname: Naito, Nobushige – sequence: 15 givenname: Mitsuru surname: Kikuchi fullname: Kikuchi, Mitsuru |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34970170$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3389_fpsyt_2023_1223147 crossref_primary_10_1007_s00429_023_02751_7 crossref_primary_10_1016_j_neuroscience_2024_11_034 crossref_primary_10_1371_journal_pone_0277989 crossref_primary_10_3389_fnsys_2022_932128 crossref_primary_10_1016_j_compbiomed_2023_106816 crossref_primary_10_3389_fpsyt_2024_1419815 crossref_primary_10_1016_j_ejpn_2023_09_005 crossref_primary_10_3389_fpsyt_2022_959763 crossref_primary_10_1097_RCT_0000000000001546 crossref_primary_10_3389_fpsyt_2023_1272120 |
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| Copyright | Copyright © 2021 Soma, Hirosawa, Hasegawa, An, Kameya, Hino, Yoshimura, Nobukawa, Iwasaki, Tanaka, Yaoi, Sano, Shiota, Naito and Kikuchi. Copyright © 2021 Soma, Hirosawa, Hasegawa, An, Kameya, Hino, Yoshimura, Nobukawa, Iwasaki, Tanaka, Yaoi, Sano, Shiota, Naito and Kikuchi. 2021 Soma, Hirosawa, Hasegawa, An, Kameya, Hino, Yoshimura, Nobukawa, Iwasaki, Tanaka, Yaoi, Sano, Shiota, Naito and Kikuchi |
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| Keywords | small-worldness graph theory MEG autism social communication |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Lisa Mash, Baylor College of Medicine, United States; Heng Chen, Guizhou University, China; Juan Kou, Sichuan Normal University, China This article was submitted to Neuroimaging and Stimulation, a section of the journal Frontiers in Psychiatry Edited by: Lizhu Luo, University of Electronic Science and Technology of China, China |
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| Title | Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach |
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