Discriminant Analysis in Schizophrenia based on Neural Network

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.