A Diagnosis Model for Early Tourette Syndrome Children Based on Brain Structural Network Characteristics
Wen Hongwei1,2; Yue Liu3,4; Jieqiong Wang1,2; Jishui Zhang3,4; Yun Peng3,4; Huiguang He1,2; He HG(何晖光)
2016-03
Conference NameSPIE Medical Imaging 2016: Computer-Aided Diagnosis
Source PublicationProceedings of SPIE
Volume97852R-97852R-9
Pages9785
Conference Date2016-02
Conference PlaceSan Diego, USA
AbstractTourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.
KeywordTourette Syndrome Dti Network Tractography Svm-rfe Automatic Classification High Accuracy
Subject Area模式识别与智能系统
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14682
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Corresponding AuthorHe HG(何晖光)
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, China
4.Beijing key Lab of Magnetic Imaging Device and Technique, Beijing Children’s Hospital, Capital Medical University, Beijing, China
Recommended Citation
GB/T 7714
Wen Hongwei,Yue Liu,Jieqiong Wang,et al. A Diagnosis Model for Early Tourette Syndrome Children Based on Brain Structural Network Characteristics[C],2016:9785.
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