Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals
Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical c...
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Frontiers Media S.A.
2021-05-01
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Series: | Frontiers in Human Neuroscience |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2021.657857/full |
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doaj-c149c829af3749a09a80cb0506df5d69 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Di Chen Di Chen Tianye Jia Tianye Jia Tianye Jia Yuning Zhang Yuning Zhang Yuning Zhang Miao Cao Miao Cao Eva Loth Chun-Yi Zac Lo Chun-Yi Zac Lo Wei Cheng Wei Cheng Zhaowen Liu Weikang Gong Barbara Jacquelyn Sahakian Jianfeng Feng Jianfeng Feng Jianfeng Feng Jianfeng Feng |
spellingShingle |
Di Chen Di Chen Tianye Jia Tianye Jia Tianye Jia Yuning Zhang Yuning Zhang Yuning Zhang Miao Cao Miao Cao Eva Loth Chun-Yi Zac Lo Chun-Yi Zac Lo Wei Cheng Wei Cheng Zhaowen Liu Weikang Gong Barbara Jacquelyn Sahakian Jianfeng Feng Jianfeng Feng Jianfeng Feng Jianfeng Feng Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals Frontiers in Human Neuroscience autism spectrum disorder functional magnetic resonance imaging high-functioning autism neural biomarker autism diagnostic observation schedule |
author_facet |
Di Chen Di Chen Tianye Jia Tianye Jia Tianye Jia Yuning Zhang Yuning Zhang Yuning Zhang Miao Cao Miao Cao Eva Loth Chun-Yi Zac Lo Chun-Yi Zac Lo Wei Cheng Wei Cheng Zhaowen Liu Weikang Gong Barbara Jacquelyn Sahakian Jianfeng Feng Jianfeng Feng Jianfeng Feng Jianfeng Feng |
author_sort |
Di Chen |
title |
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals |
title_short |
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals |
title_full |
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals |
title_fullStr |
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals |
title_full_unstemmed |
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals |
title_sort |
neural biomarkers distinguish severe from mild autism spectrum disorder among high-functioning individuals |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2021-05-01 |
description |
Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments. |
topic |
autism spectrum disorder functional magnetic resonance imaging high-functioning autism neural biomarker autism diagnostic observation schedule |
url |
https://www.frontiersin.org/articles/10.3389/fnhum.2021.657857/full |
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doaj-c149c829af3749a09a80cb0506df5d692021-05-06T04:17:16ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612021-05-011510.3389/fnhum.2021.657857657857Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning IndividualsDi Chen0Di Chen1Tianye Jia2Tianye Jia3Tianye Jia4Yuning Zhang5Yuning Zhang6Yuning Zhang7Miao Cao8Miao Cao9Eva Loth10Chun-Yi Zac Lo11Chun-Yi Zac Lo12Wei Cheng13Wei Cheng14Zhaowen Liu15Weikang Gong16Barbara Jacquelyn Sahakian17Jianfeng Feng18Jianfeng Feng19Jianfeng Feng20Jianfeng Feng21Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, ChinaInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, ChinaCentre for Population Neuroscience and Precision Medicine, MRC SGDP Centre, IoPPN, King’s College London, London, United KingdomCentre for Population Neuroscience and Precision Medicine, MRC SGDP Centre, IoPPN, King’s College London, London, United KingdomState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaSchool of Psychology, University of Southampton, Southampton, United KingdomInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, ChinaSackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, IoPPN, King’s College London, London, United KingdomInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, ChinaInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, ChinaInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaDepartment of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United KingdomInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, ChinaSchool of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, ChinaDepartment of Computer Science, University of Warwick, Coventry, United KingdomSeveral previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments.https://www.frontiersin.org/articles/10.3389/fnhum.2021.657857/fullautism spectrum disorderfunctional magnetic resonance imaginghigh-functioning autismneural biomarkerautism diagnostic observation schedule |