基于视觉认知任务的注意缺陷多动障碍患儿与正常儿童脑功能网络差异研究  

Comparative research on brain networks of children with attention deficit hyperactivity disorder and normal children based on visual cognitive tasks

作　　者：宋志伟 李文杰[1,2] 毕卉 王苏弘[3] 邹凌[1,2] SONG Zhiwei;LI Wenjie;BI Hui;WANG Suhong;ZOU Ling(School of Information Science and Engineering,Changzhou University,Changzhou,Jiangsu 213164,P.R China;Key Laboratory of Biomedical Information Technology,Changzhou University,Changzhou,Jiangsu 213164,P.R China;Brain Science Research Center,The Third Affiliated Hospital of Soochow University,Changzhou,Jiangsu 213003,P.R China)

机构地区：[1]常州大学信息科学与工程学院,江苏常州213164 [2]常州大学生物医学信息技术重点实验室,江苏常州213164 [3]苏州大学附属第三医院脑科学研究中心,江苏常州213003

出　　处：《生物医学工程学杂志》2020年第5期749-755,764,共8页Journal of Biomedical Engineering

基　　金：江苏省科技厅社会发展项目(BE2018638);常州市社会发展项目(CE20195025);首批中外合作办学平台联合科研项目“人机智能与交互国际联合实验室”;江苏省研究生培养创新计划项目(KYCX20_2552)。

摘　　要：针对注意缺陷多动障碍(ADHD)儿童与正常儿童在执行任务状态下的脑网络的差异性,本文采用视觉功能区网络特征进行了比较研究,提取的试验数据为受试者执行猜题任务时,视觉捕捉范式获取的功能性磁共振成像(fMRI)数据,受试者包括23名ADHD患儿[年龄:(8.27±2.77)岁]与23名正常儿童[年龄:(8.70±2.58)岁]。首先,本文利用fMRI数据构建视觉区脑功能网络;然后,获取视觉区脑功能网络的特征指标,包括:度分布、平均最短路径、网络密度、聚集系数、介数等,并与传统全脑网络进行对比分析;最后,利用机器学习算法中的支持向量机(SVM)等分类器对特征指标进行分类以区分ADHD儿童与正常儿童。本研究采用视觉区脑功能网络特征进行分类,分类精度最高达到96%,与传统的构建全脑网络方法相比,精度提高了10%左右。试验结果表明,使用视觉区脑功能网络分析法能够更好地区分ADHD儿童与正常儿童。该方法对ADHD儿童与正常儿童脑网络的区分具有一定的帮助,有利于ADHD儿童的辅助诊断。Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder(ADHD)and normal children in the task-executing state,this paper conducted a comparative study using the network features of the visual function area.Functional magnetic resonance imaging(fMRI)data of 23 children with ADHD[age:(8.27±2.77)years]and 23 normal children[age:(8.70±2.58)years]were obtained by the visual capture paradigm when the subjects were performing the guessing task.First,fMRI data were used to build a visual area brain function network.Then,the visual area brain function network characteristic indicators including degree distribution,average shortest path,network density,aggregation coefficient,intermediary,etc.were obtained and compared with the traditional whole brain network.Finally,support vector machines(SVM)and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children.In this study,visual brain function network features were used for classification,with a classification accuracy of up to 96%.Compared with the traditional method of constructing a whole brain network,the accuracy was improved by about 10%.The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children.This method has certain help to distinguish the brain network between ADHD children and normal children,and is helpful for the auxiliary diagnosis of ADHD children.

关 键 词：注意缺陷多动障碍 脑网络 视觉脑区 子网络 分类 

分 类 号：R749.94[医药卫生—神经病学与精神病学]