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
AbstractTourette 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.
KeywordTourette Syndrome Dti Tbss Svm Mkl
WOS HeadingsScience & Technology ; Technology
Indexed BySCI ; SSCi
Funding OrganizationNational 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000389785900051
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Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  类脑智能研究中心
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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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