Gao Jingjing, Tang Heping, Wang Zhengning, Li Yanling, Luo Na, Song Ming, Xie Sangma, Shi Weiyang, Yan Hao, Lu Lin, Yan Jun, Li Peng, Song Yuqing, Chen Jun, Chen Yunchun, Wang Huaning, Liu Wenming, Li Zhigang, Guo Hua, Wan Ping, Lv Luxian, Yang Yongfeng, Wang Huiling, Zhang Hongxing, Wu Huawang, Ning Yuping, Zhang Dai, Jiang Tianzi
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China.
Neurosci Bull. 2025 Mar 18. doi: 10.1007/s12264-025-01385-5.
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.
精神分裂症（SZ）是一种严重的精神疾病。本研究将扩散张量成像（DTI）数据与图神经网络相结合，以区分SZ患者和正常对照（NCs），并展示了整合分数各向异性和纤维数量脑网络特征的图神经网络的卓越性能，在区分SZ患者和NCs方面达到了73.79%的准确率。除了单纯的区分，我们的研究还通过可解释模型分析和基因表达分析，更深入地探讨了利用白质脑网络特征识别SZ患者的优势。这些分析揭示了脑成像标志物与遗传生物标志物之间复杂的相互关系，为SZ的神经病理学基础提供了新的见解。总之，我们的研究结果强调了将图神经网络应用于多模态DTI数据，通过对神经影像和遗传特征的综合分析来提高SZ检测的潜力。
