摘要
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.
基金
supported by the National Natural Science Foundation of China(62276049,61701078,61872068,and 62006038)
the Natural Science Foundation of Sichuan Province(2025ZNSFSC0487)
the Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200)
the National Key R&D Program of China(2023YFE0118600)
Sichuan Province Science and Technology Support Program(2019YJ0193,2021YFG0126,2021YFG0366,and 2022YFS0180)
Medico-Engineering Cooperation Funds from the University of Electronic Science and Technology of China(ZYGX2021YGLH014).
