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Proceedings Paper

Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children
Author(s): Hongwei Wen; Yue Liu; Shengpei Wang; Zuoyong Li; Jishui Zhang; Yun Peng; Huiguang He
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Paper 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.

Paper Details

Date Published: 3 March 2017
PDF: 13 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341Q (3 March 2017); doi: 10.1117/12.2251426
Show Author Affiliations
Hongwei Wen, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Yue Liu, Beijing Children’s Hospital, Capital Medical Univ. (China)
Shengpei Wang, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Zuoyong Li, Minjiang Univ. (China)
Jishui Zhang, Beijing Children’s Hospital, Capital Medical Univ. (China)
Yun Peng, Beijing Children’s Hospital, Capital Medical Univ. (China)
Huiguang He, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Ctr. for Excellence in Brain Science and Intelligence Technology (China)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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