The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster - Bi, Xia-an; Liu, Yingchao; Jiang, Qin; Shu, Qing; Sun, Qi; Dai, Jianhua (Frontiers in Human Neuroscience, 2018)
As the autism spectrum disorder (ASD) is highly heritable, pervasive and prevalent, the clinical diagnosis of ASD is vital. In the existing literature, a single neural network (NN) is generally used to classify ASD patients from typical controls (TC) based on functional MRI data and the accuracy is not very high. Thus, the new method named as the random neural network cluster which consists of multiple neural networks (NNs) was proposed to classify ASD patients and TC in this paper. 50 ASD patients and 42 TC were selected from Autism Brain Imaging Data Exchange (ABIDE) database. Firstly, five different NNs were applied to build five types of random neural network clusters. Secondly, the accuracies of the five types of random neural network clusters were compared to select the highest one. The random Elman neural network cluster had the highest accuracy, thus Elman NN was selected as the best base classifier. Then, we used the significant features between ASD patients and TC to find out abnormal brain regions which include the supplementary motor area, the median cingulate and paracingulate gyri, the fusiform gyrus and the insula. The proposed method provides a new perspective to improve classification performance and it is meaningful for the diagnosis of ASD.