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An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification

Autism Spectrum Disorder (ASD) is one common developmental disorder with great variations in symptoms and severity, making the diagnosis of ASD a challenging task. Existing deep learning models using brain connectivity features to classify ASD still suffer from degraded performance for multi-center...

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Autores principales: Chen, Yueying, Liu, Aiping, Fu, Xueyang, Wen, Jie, Chen, Xun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8854990/
https://www.ncbi.nlm.nih.gov/pubmed/35185454
http://dx.doi.org/10.3389/fnins.2021.828512
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author Chen, Yueying
Liu, Aiping
Fu, Xueyang
Wen, Jie
Chen, Xun
author_facet Chen, Yueying
Liu, Aiping
Fu, Xueyang
Wen, Jie
Chen, Xun
author_sort Chen, Yueying
collection PubMed
description Autism Spectrum Disorder (ASD) is one common developmental disorder with great variations in symptoms and severity, making the diagnosis of ASD a challenging task. Existing deep learning models using brain connectivity features to classify ASD still suffer from degraded performance for multi-center data due to limited feature representation ability and insufficient interpretability. Given that Graph Convolutional Network (GCN) has demonstrated superiority in learning discriminative representations of brain connectivity networks, in this paper, we propose an invertible dynamic GCN model to identify ASD and investigate the alterations of connectivity patterns associated with the disease. In order to select explainable features from the model, invertible blocks are introduced in the whole network, and we are able to reconstruct the input dynamic features from the network's output. A pre-screening of connectivity features is adopted to reduce the redundancy of the input information, and a fully-connected layer is added to perform classification. The experimental results on 867 subjects show that our proposed method achieves superior disease classification performance. It provides an interpretable deep learning model for brain connectivity analysis and is of great potential in studying brain-related disorders.
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spelling pubmed-88549902022-02-19 An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification Chen, Yueying Liu, Aiping Fu, Xueyang Wen, Jie Chen, Xun Front Neurosci Neuroscience Autism Spectrum Disorder (ASD) is one common developmental disorder with great variations in symptoms and severity, making the diagnosis of ASD a challenging task. Existing deep learning models using brain connectivity features to classify ASD still suffer from degraded performance for multi-center data due to limited feature representation ability and insufficient interpretability. Given that Graph Convolutional Network (GCN) has demonstrated superiority in learning discriminative representations of brain connectivity networks, in this paper, we propose an invertible dynamic GCN model to identify ASD and investigate the alterations of connectivity patterns associated with the disease. In order to select explainable features from the model, invertible blocks are introduced in the whole network, and we are able to reconstruct the input dynamic features from the network's output. A pre-screening of connectivity features is adopted to reduce the redundancy of the input information, and a fully-connected layer is added to perform classification. The experimental results on 867 subjects show that our proposed method achieves superior disease classification performance. It provides an interpretable deep learning model for brain connectivity analysis and is of great potential in studying brain-related disorders. Frontiers Media S.A. 2022-02-04 /pmc/articles/PMC8854990/ /pubmed/35185454 http://dx.doi.org/10.3389/fnins.2021.828512 Text en Copyright © 2022 Chen, Liu, Fu, Wen and Chen. 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 Neuroscience
Chen, Yueying
Liu, Aiping
Fu, Xueyang
Wen, Jie
Chen, Xun
An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification
title An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification
title_full An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification
title_fullStr An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification
title_full_unstemmed An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification
title_short An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification
title_sort invertible dynamic graph convolutional network for multi-center asd classification
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8854990/
https://www.ncbi.nlm.nih.gov/pubmed/35185454
http://dx.doi.org/10.3389/fnins.2021.828512
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