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Proceedings Paper

A diagnosis model for early Tourette syndrome children based on brain structural network characteristics
Author(s): Hongwei Wen; Yue Liu; Jieqiong Wang; Jishui Zhang; Yun Peng; Huiguang He
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Paper Abstract

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.

Paper Details

Date Published: 24 March 2016
PDF: 9 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852R (24 March 2016); doi: 10.1117/12.2217308
Show Author Affiliations
Hongwei Wen, Institute of Automation (China)
Yue Liu, Capital Medical Univ. (China)
Jieqiong Wang, Institute of Automation (China)
Jishui Zhang, Capital Medical Univ. (China)
Yun Peng, Capital Medical Univ. (China)
Huiguang He, Institute of Automation (China)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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