| Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children | |
Wen, Hongwei1,2,3 ; Liu, Yue5; Rekik, Islem7,8; Wang Shengpei1,2,3; Chen, Zhiqiang1,2,3; Zhang, Jishui6; Zhang, Yue5; Peng, Yun5; He, Huiguang1,2,3,4
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| 发表期刊 | PATTERN RECOGNITION
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| 2017-03-01 | |
| 卷号 | 63期号:*页码:601-611 |
| 文章类型 | Article |
| 摘要 | Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. To date, TS diagnosis remains somewhat limited and studies using advanced diagnostic methods are of great importance. In this paper, we introduce an automatic classification framework for accurate identification of TS children based on multi-modal and multi-type features, which is robust and easy to implement. We present in detail the feature extraction, feature selection, and classifier training methods. In addition, in order to exploit complementary information revealed by different feature modalities, we integrate multi-modal image features using multiple kernel learning (MKL). The performance of our framework has been validated in classifying 44 TS children and 48 age-and gender-matched healthy children. When combining features using MKL, the classification accuracy reached 94.24% using nested cross-validation. Most discriminative brain regions were mostly located in the cortico-basal ganglia, frontal cortico-cortical circuits, which are thought to be highly related to TS pathology. These results show that our method is reliable for early TS diagnosis, and promising for prognosis and treatment outcome. |
| 关键词 | Tourette Syndrome Dti Tbss Svm Mkl |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.patcog.2016.09.039 |
| 关键词[WOS] | VOXEL-BASED MORPHOMETRY ; ALZHEIMERS-DISEASE ; FUNCTIONAL CONNECTIVITY ; WHITE-MATTER ; TIC SEVERITY ; ABNORMALITIES ; BRAIN ; SCALE ; CLASSIFICATION ; SELECTION |
| 收录类别 | SCI ; SSCi |
| 语种 | 英语 |
| 项目资助者 | National Natural Science Foundation of China(61271151 ; Youth Innovation Promotion Association CAS ; Beijing Municipal Administration of Hospitals Incubating Program(PX2016035) ; Beijing Health System Top Level Health Technical Personnel Training Plan(2015-3-082) ; 91520202 ; 31271161) |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000389785900051 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/13369 |
| 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 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China 5.Capital Med Univ, Beijing Childrens Hosp, Dept Radiol, Beijing, Peoples R China 6.Capital Med Univ, Beijing Childrens Hosp, Dept Neurol, Beijing, Peoples R China 7.Univ N Carolina, Dept Radiol, Chapel Hill, NC USA 8.Univ N Carolina, BRIC, Chapel Hill, NC USA |
| 第一作者单位 | 中国科学院自动化研究所; 类脑智能研究中心 |
| 推荐引用方式 GB/T 7714 | Wen, Hongwei,Liu, Yue,Rekik, Islem,et al. Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children[J]. PATTERN RECOGNITION,2017,63(*):601-611. |
| APA | Wen, Hongwei.,Liu, Yue.,Rekik, Islem.,Wang Shengpei.,Chen, Zhiqiang.,...&He, Huiguang.(2017).Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children.PATTERN RECOGNITION,63(*),601-611. |
| MLA | Wen, Hongwei,et al."Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children".PATTERN RECOGNITION 63.*(2017):601-611. |
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| 1-s2.0-S003132031630（1101KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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