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 | |
会议名称 | SPIE Medical Imaging 2017: Computer-Aided Diagnosis |
会议录名称 | Proceedings of SPIE |
卷号 | 10134 |
页码 | 101341Q-101341Q-13 |
会议日期 | 2017-02 |
会议地点 | Orlando, USA |
摘要 |
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. |
关键词 | Tourette Syndrome Diffusion Mri Probabilistic Tractography Structural Connectivity Graph Theoretical Analysis Similarity Network Fusion Support Vector Machine |
学科领域 | Medical Imaging |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14662 |
专题 | 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 |
通讯作者 | He Huiguang |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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