Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children. (March 2017)

• Record Type: Journal Article
• Authors: Wen, Hongwei; Liu, Yue; Rekik, Islem; Wang, Shengpei; Chen, Zhiqiang; Zhang, Jishui; Zhang, Yue; Peng, Yun; He, Huiguang
• Is Part Of: Pattern recognition. Volume 63(2017:Mar.)
• Page Start: 601
• Page End: 611
• Publication Date: 2017-03
• Subjects: TS Tourette syndrome -- DTI diffusion tensor imaging -- WM white matter -- SVM support vector machine -- MKL multiple kernel learning -- TBSS Tract-Based Spatial Statistics; Tourette syndrome -- DTI -- TBSS -- SVM -- MKL; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.09.039
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Highlights: We combine VBM and TBSS analysis to investigate GM/WM changes in TS children. We apply most-representative-subject TBSS procedure suitable for young children. We integrate multi-modal image features using multiple kernel learning. We achieved an excellent accuracy of 94.24%. We identify the most discriminative ROIs and features for classification.