Bi Xia-An, Liu Yingchao, Jiang Qin, Shu Qing, Sun Qi, Dai Jianhua
College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Front Hum Neurosci. 2018 Jun 26;12:257. doi: 10.3389/fnhum.2018.00257. eCollection 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 NN cluster, which consists of multiple NNs was proposed to classify ASD patients and TC in this article. Fifty ASD patients and 42 TC were selected from autism brain imaging data exchange (ABIDE) database. First, five different NNs were applied to build five types of random NN clusters. Second, the accuracies of the five types of random NN clusters were compared to select the highest one. The random Elman NN 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 (FG) and the insula (INS). The proposed method provides a new perspective to improve classification performance and it is meaningful for the diagnosis of ASD.
由于自闭症谱系障碍（ASD）具有高度遗传性、普遍性和广泛性，因此ASD的临床诊断至关重要。在现有文献中，通常使用单个神经网络（NN）基于功能磁共振成像数据将ASD患者与典型对照组（TC）进行分类，但其准确性不是很高。因此，本文提出了一种名为随机NN聚类的新方法，该方法由多个NN组成，用于对ASD患者和TC进行分类。从自闭症脑成像数据交换（ABIDE）数据库中选取了50名ASD患者和42名TC。首先，应用五种不同的NN构建五种类型的随机NN聚类。其次，比较这五种类型随机NN聚类的准确性，以选择最高的一种。随机埃尔曼NN聚类的准确性最高，因此选择埃尔曼NN作为最佳基础分类器。然后，我们利用ASD患者和TC之间的显著特征找出异常脑区，包括辅助运动区、中央扣带回和旁扣带回、梭状回（FG）和脑岛（INS）。所提出的方法为提高分类性能提供了一个新的视角，并对ASD的诊断具有重要意义。
