Multi-threshold White Matter Structural Networks Fusion for Accurate Diagnosis of Tourette Syndrome Children
Wen Hongwei1,2; Yue Liu4; Shengpei Wang1,2; Zuoyong Li5; Jishui Zhang6; Yun Peng4; Huiguang He1,2,3; He Huiguang
2017-03
Conference NameSPIE Medical Imaging 2017: Computer-Aided Diagnosis
Source PublicationProceedings of SPIE
Volume10134
Pages101341Q-101341Q-13
Conference Date2017-02
Conference PlaceOrlando, USA
Abstract

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

KeywordTourette Syndrome Diffusion Mri Probabilistic Tractography Structural Connectivity Graph Theoretical Analysis Similarity Network Fusion Support Vector Machine
Subject AreaMedical Imaging
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14662
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 Huiguang
Affiliation1.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
4.Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, China
5.Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, 350121, China
6.Department of Neurology, Beijing Children’s Hospital, Capital Medical University, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wen Hongwei,Yue Liu,Shengpei Wang,et al. Multi-threshold White Matter Structural Networks Fusion for Accurate Diagnosis of Tourette Syndrome Children[C],2017:101341Q-101341Q-13.
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