Multipattern graph convolutional network-based autism spectrum disorder identification

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) pr...

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Veröffentlicht in:Cerebral cortex (New York, N.Y. 1991) Jg. 34; H. 3
Hauptverfasser: Zhou, Wenhao, Sun, Mingxiang, Xu, Xiaowen, Ruan, Yudi, Sun, Chenhao, Li, Weikai, Gao, Xin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2024
Schlagworte:
Autism Spectrum Disorder - diagnostic imaging
Autistic Disorder
Brain - diagnostic imaging
Databases, Factual
Humans
ROC Curve
autism spectrum disorder
multipattern
resting-state fMRI
brain connectivity networks
graph convolution network
ISSN:1460-2199, 1460-2199
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Zusammenfassung:The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.
Bibliographie:ObjectType-Article-1
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content type line 23
ISSN:1460-2199
1460-2199
DOI:10.1093/cercor/bhae064