| 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)
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| 2016-03 | |
| 会议名称 | SPIE Medical Imaging 2016: Computer-Aided Diagnosis |
| 会议录名称 | Proceedings of SPIE |
| 卷号 | 97852R-97852R-9 |
| 页码 | 9785 |
| 会议日期 | 2016-02 |
| 会议地点 | San Diego, USA |
| 摘要 | Tourette 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. |
| 关键词 | Tourette Syndrome Dti Network Tractography Svm-rfe Automatic Classification High Accuracy |
| 学科领域 | 模式识别与智能系统 |
| 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/173211/14682 |
| 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 HG(何晖光) |
| 作者单位 | 1.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 |
| 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,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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| 发表的SPIE Medical Imag（674KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
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