太原理工大学学报2018，Vol.49Issue(3)：454-461,8.DOI:10.16355/j.cnki.issn1007-9432tyut.2018.03.014
最小生成树脑网络分析及自闭症分类研究
Brain Network Analysis Based on Minimum Spanning Tree and Classification in Autism
摘要
Abstract
There'is a huge difference in the clinical characterization of autism in different ages, which is difficult to be detected on the basis of imaging indicators.In order to solve this problem, this study employed the minimum spanning tree analysis method based on the static state func-tional brain network.Node attributes,such as degree,betweenness centrality and eccentricity, were used to analyze the differences between different age groups (children-adolescents,adoles-cents-adults).At the same time,with the significant regions as the feature,a the multi-parame-ter optimization framework was proposed and a model with high diagnostic accuracy of autism was constructed.The results show that significant differences existed between the two groups. The classification accuracy was 80.38% and 81.88% respectively.This research provides an im-portant method and a new idea for imaging analysis and auxiliary diagnosis of austism patients with different age.关键词
最小生成树/复杂网络/自闭症/分类Key words
minimum spanning tree(MST)/complex network/autism spectrum disorder (ASD)/classification分类
信息技术与安全科学引用本文复制引用
程超,党伟超,白尚旺,潘理虎,刘春霞..最小生成树脑网络分析及自闭症分类研究[J].太原理工大学学报,2018,49(3):454-461,8.基金项目
山西省中科院科技合作项目(20141101001) （20141101001）
"十二五"山西省科技重大专项项目(20121101001) （20121101001）
山西省重点研发计划(一般)工业项目(201703D121042-1) （一般）
山西省社会发展科技项目(20140313020-1) （20140313020-1）
