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Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention...

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Autores principales: Gao, Jingjing, Chen, Mingren, Li, Yuanyuan, Gao, Yachun, Li, Yanling, Cai, Shimin, Wang, Jiaojian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877487/
https://www.ncbi.nlm.nih.gov/pubmed/33584183
http://dx.doi.org/10.3389/fnins.2020.629630
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author Gao, Jingjing
Chen, Mingren
Li, Yuanyuan
Gao, Yachun
Li, Yanling
Cai, Shimin
Wang, Jiaojian
author_facet Gao, Jingjing
Chen, Mingren
Li, Yuanyuan
Gao, Yachun
Li, Yanling
Cai, Shimin
Wang, Jiaojian
author_sort Gao, Jingjing
collection PubMed
description Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.
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spelling pubmed-78774872021-02-12 Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks Gao, Jingjing Chen, Mingren Li, Yuanyuan Gao, Yachun Li, Yanling Cai, Shimin Wang, Jiaojian Front Neurosci Neuroscience Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network. Frontiers Media S.A. 2021-01-28 /pmc/articles/PMC7877487/ /pubmed/33584183 http://dx.doi.org/10.3389/fnins.2020.629630 Text en Copyright © 2021 Gao, Chen, Li, Gao, Li, Cai and Wang. http://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
Gao, Jingjing
Chen, Mingren
Li, Yuanyuan
Gao, Yachun
Li, Yanling
Cai, Shimin
Wang, Jiaojian
Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
title Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
title_full Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
title_fullStr Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
title_full_unstemmed Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
title_short Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
title_sort multisite autism spectrum disorder classification using convolutional neural network classifier and individual morphological brain networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877487/
https://www.ncbi.nlm.nih.gov/pubmed/33584183
http://dx.doi.org/10.3389/fnins.2020.629630
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