结合脑功能连接和图论的多特征融合方法用于精神分裂症分类及神经影像标志物筛选

Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.

作者信息

Wang Chang, Ren Yaning, Zhang Rui, Zhang Jiyuan, Li Xiao, Chen Xiangyu, Shen Jiefen, Zhao Zongya, Yang Yongfeng, Ren Wenjie, Yu Yi

机构信息

Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Henan Key Laboratory of Biological Psychiatry, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Xinxiang Engineering Technology Research Center of Intelligent Medical Imaging Diagnosis, Xinxiang, China.

出版信息

J Psychiatr Res. 2025 Mar;183:260-268. doi: 10.1016/j.jpsychires.2025.02.025. Epub 2025 Feb 21.

DOI:10.1016/j.jpsychires.2025.02.025

PMID:40010076

Abstract

BACKGROUND

The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. The schizophrenia classification and neuroimaging markers screening using FC and GT feature fusion are blank.

METHODS

We proposed multi-feature fusion method combining functional connectivity and graph topology for schizophrenia classification and neuroimaging markers screening. Firstly, we acquired and preprocessed the private rs-fMRI data from the second affiliated hospital of Xinxiang Medical University in china. Secondly, we calculated the functional connectivity matrix and graph topology features. Thirdly, we used the two-sample t-test and the minimum absolute contraction selection operator (LASSO) to extract the features with statistical differences. Lastly, we used machine learning to classify schizophrenia and screen neuroimaging markers.

RESULTS

The result showed that the SVM model with the best feature (i.e., FC and GT) has the best performance (ACC = 0.935(95 percent confidence interval, 0.932 to 0.938), SEN = 0.920(95 percent confidence interval, 0.917 to 0.922), SPE = 0.950(95 percent confidence interval, 0.946 to 0.954), F1 = 0.935(95 percent confidence interval, 0.933 to 0.938), AUC = 0.935(95 percent confidence interval, 0.932 to 0.937)). We also found that the differences in FC and GT features are mainly located in the default network, the attention network, and the subcortical network. The feature strength of FC and GT showed a general decline in patients with SZ, and the node clustering coefficient of the thalamus and the FC of Putamen_L and Frontal_Mid_Orb_R showed an increase.

CONCLUSION

It demonstrated that the multi-feature fusion has the advantage in distinguishing SZ from healthy individuals providing new insights into the underlying pathogenesis of SZ.

摘要

背景

精神分裂症（SZ）患者脑功能连接（FC）和图论拓扑（GT）的异常尚不清楚。研究人员提出了通过结合FC或GT的机器学习算法，以从健康对照中识别SZ。利用FC和GT特征融合进行精神分裂症分类及神经影像标志物筛选尚属空白。

方法

我们提出了一种结合功能连接和图论拓扑的多特征融合方法，用于精神分裂症分类及神经影像标志物筛选。首先，我们获取并预处理了来自中国新乡医学院第二附属医院的静息态功能磁共振成像（rs-fMRI）数据。其次，我们计算了功能连接矩阵和图论拓扑特征。第三，我们使用两样本t检验和最小绝对收缩选择算子（LASSO）来提取具有统计学差异的特征。最后，我们使用机器学习对精神分裂症进行分类并筛选神经影像标志物。

结果

结果显示，具有最佳特征（即FC和GT）的支持向量机（SVM）模型具有最佳性能（准确率（ACC）=0.935（95%置信区间，0.932至0.938），灵敏度（SEN）=0.920（95%置信区间，0.917至0.922），特异度（SPE）=0.950（95%置信区间，0.946至0.954），F1值=0.935（95%置信区间，0.933至0.938），曲线下面积（AUC）=0.935（95%置信区间，0.932至0.937））。我们还发现，FC和GT特征的差异主要位于默认网络、注意网络和皮质下网络。SZ患者的FC和GT特征强度总体呈下降趋势，丘脑的节点聚类系数以及壳核_L和额中眶回_R的FC呈增加趋势。