Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children

作者：

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.

摘要

•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.

论文关键词：Tourette syndrome,DTI,TBSS,SVM,MKL

论文评审过程：Received 19 January 2016, Revised 26 August 2016, Accepted 21 September 2016, Available online 22 September 2016, Version of Record 27 November 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.039