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Title: Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Abstract

Introduction Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10more » salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git .« less

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research
OSTI Identifier:
1963970
Grant/Contract Number:  
2018B030335001; 2020B0101130020; 2020B0404010002; 2019A1515110427; 201903010032; 202103000032; 202206060005; 202206080005; 202206010077; 202206010034; 2020KSYS001
Resource Type:
Published Article
Journal Name:
Frontiers in Neuroscience (Online)
Additional Journal Information:
Journal Name: Frontiers in Neuroscience (Online) Journal Volume: 17; Journal ID: ISSN 1662-453X
Publisher:
Frontiers Media SA
Country of Publication:
Switzerland
Language:
English

Citation Formats

Chen, Xiaoyi, Ke, Pengfei, Huang, Yuanyuan, Zhou, Jing, Li, Hehua, Peng, Runlin, Huang, Jiayuan, Liang, Liqin, Ma, Guolin, Li, Xiaobo, Ning, Yuping, Wu, Fengchun, and Wu, Kai. Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis. Switzerland: N. p., 2023. Web. doi:10.3389/fnins.2023.1140801.
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Chen, Xiaoyi, Ke, Pengfei, Huang, Yuanyuan, Zhou, Jing, Li, Hehua, Peng, Runlin, Huang, Jiayuan, Liang, Liqin, Ma, Guolin, Li, Xiaobo, Ning, Yuping, Wu, Fengchun, & Wu, Kai. Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis. Switzerland. https://doi.org/10.3389/fnins.2023.1140801
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Chen, Xiaoyi, Ke, Pengfei, Huang, Yuanyuan, Zhou, Jing, Li, Hehua, Peng, Runlin, Huang, Jiayuan, Liang, Liqin, Ma, Guolin, Li, Xiaobo, Ning, Yuping, Wu, Fengchun, and Wu, Kai. Thu . "Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis". Switzerland. https://doi.org/10.3389/fnins.2023.1140801.
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@article{osti_1963970,
title = {Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis},
author = {Chen, Xiaoyi and Ke, Pengfei and Huang, Yuanyuan and Zhou, Jing and Li, Hehua and Peng, Runlin and Huang, Jiayuan and Liang, Liqin and Ma, Guolin and Li, Xiaobo and Ning, Yuping and Wu, Fengchun and Wu, Kai},
abstractNote = {Introduction Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git .},
doi = {10.3389/fnins.2023.1140801},
journal = {Frontiers in Neuroscience (Online)},
number = ,
volume = 17,
place = {Switzerland},
year = {Thu Mar 30 00:00:00 EDT 2023},
month = {Thu Mar 30 00:00:00 EDT 2023}
}
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https://doi.org/10.3389/fnins.2023.1140801
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