基于神经网络的精神分裂症判别分析

Discriminant Analysis in Schizophrenia based on Neural Network

本文主要利用神经网络进行精神分裂症患者和正常人的判别分析,以63例精神分裂症患者和57例正常人的4005条功能连接作为原始特征空间,尝试用不同的降维方法,不同的神经网络模型来寻找最优分类模型.结果表明:用Mann-Whitney U检验选取病人与正常人差异最大的特征作为输入,用Elman神经网络模型作分类的效果最佳,正确率94.17%,置换检验p<0.001,敏感度92.06%,特异度96.49%.对于得到最高分类正确率的神经网络模型,我们找出了34条对分类取到最大贡献作用的共识功能连接,里面包含了26个脑区,这26个脑区中尤以丘脑所对应的功能连接边数最多,其次是扣带回和额叶.

In this paper, we use neural network to classify schizophrenia patients and healthy control subjects. Based on 4005 high dimensions feature space consist of functional connectivity about 63 schizophrenic patients and 57 healthy control as the original data, attempting to try different dimensionality reduction methods, different neural network model to find the optimal classification model. The results show that using the Mann-Whitney U test to select the more discrimination features as input and using Elman neural network model for classification to get the best results, can reach a highest accuracy of 94.17%, with the sensitivity being 92.06% and the specificity being 96.49%. For the best classification neural network model, we identified 34 consensus functional connectivities that exhibit high discriminative power in classification, which includes 26 brain regions, particularly in the thalamus regions corresponding to the maximum number of functional connectivity edges, followed by the cingulate gyrus and frontal region.