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Front. Physiol. | doi: 10.3389/fphys.2018.01646

Analysis of Asperger Syndrome Using Genetic-evolutionary Random Support Vector Machine Cluster

 Xia-an Bi1*, Qi Sun2, Yingchao Liu2, Yang Wang2 and Xianhao Luo2
  • 1College of Information Science and Engineering, Hunan Normal University, China
  • 2College of Information Science and Engineering, Hunan Normal University, China

Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Patients with AS retain normal language function but lack of social communication ability. Diagnosis and pathological analysis of AS through fMRI data, especially resting state fMRI data, is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of cluster and genetic evolution to improve the performance of the model. Functional connectivity is used as a sample feature of this study. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in the autistic brain imaging data exchange (ABIDE) database. the classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences between AS and normal people, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS. Meanwhile, because the model has excellent generalization performance, our model may provide a universal framework for other brain science research.

Keywords: genetic-evolutionary random SVM cluster, functional connectivity, Classification, Asperger Syndrome, abnormal brain regions

Received: 01 Aug 2018; Accepted: 31 Oct 2018.

Edited by:

Zhixiong Zhong, Minnan Normal University, China

Reviewed by:

Ji-Hong Chen, McMaster University, Canada
Jing Zhao, Yuan Ze University, Taiwan  

Copyright: © 2018 Bi, Sun, Liu, Wang and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Xia-an Bi, Hunan Normal University, College of Information Science and Engineering, Changsha, China,