1.Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.
Jingjing GAO ; Heping TANG ; Zhengning WANG ; Yanling LI ; Na LUO ; Ming SONG ; Sangma XIE ; Weiyang SHI ; Hao YAN ; Lin LU ; Jun YAN ; Peng LI ; Yuqing SONG ; Jun CHEN ; Yunchun CHEN ; Huaning WANG ; Wenming LIU ; Zhigang LI ; Hua GUO ; Ping WAN ; Luxian LV ; Yongfeng YANG ; Huiling WANG ; Hongxing ZHANG ; Huawang WU ; Yuping NING ; Dai ZHANG ; Tianzi JIANG
Neuroscience Bulletin 2025;41(6):933-950
Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans
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Schizophrenia/pathology*
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Diffusion Tensor Imaging/methods*
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Male
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Female
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Adult
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Brain/metabolism*
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Young Adult
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Middle Aged
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White Matter/pathology*
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Gene Expression
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Nerve Net/diagnostic imaging*
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Graph Neural Networks
Result Analysis
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