Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children

• Published in: Human brain mapping Vol. 38; no. 8; pp. 3988 - 4008
• Main Authors: Wen, Hongwei, Liu, Yue, Rekik, Islem, Wang, Shengpei, Zhang, Jishui, Zhang, Yue, Peng, Yun, He, Huiguang
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.08.2017; John Wiley and Sons Inc
• Subjects: Abnormalities; Adolescent; Algorithms; Area Under Curve; Attention Deficit Disorder with Hyperactivity - complications; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Biomarkers; Brain; Brain - diagnostic imaging; Child; Child, Preschool; Children; Circuits; Classification; Cortex (parietal); Diffusion Magnetic Resonance Imaging - methods; diffusion MRI; Diffusion Tensor Imaging - methods; Female; Ganglia; Gilles de la Tourette syndrome; Graph theory; Humans; Image Processing, Computer-Assisted - methods; Integration; Learning algorithms; Magnetic resonance imaging; Male; Medical imaging; multiple kernel learning; Networks; Neural networks; Neural Pathways - diagnostic imaging; Neuroimaging; Neurology; Obsessive-Compulsive Disorder - complications; Obsessive-Compulsive Disorder - diagnostic imaging; Occipital lobe; probabilistic tractography; ROC Curve; Sensorimotor system; Shortest-path problems; Specialization; structural network; Substantia alba; Support Vector Machine; Theoretical analysis; topological organization; Tourette syndrome; Tourette Syndrome - classification; Tourette Syndrome - complications; Tourette Syndrome - diagnostic imaging; Visual cortex; Visual pathways; multiple kernel learning; graph theory; topological organization; diffusion MRI; structural network; probabilistic tractography; Tourette syndrome
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23643

Tourette syndrome (TS) is a childhood‐onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole‐brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug‐naive TS children and 41 age‐ and gender‐matched healthy children. The WM networks were constructed by estimating inter‐regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small‐world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network‐based statistical (NBS) analysis, primarily composed of the parieto‐occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988–4008, 2017. © 2017 Wiley Periodicals, Inc.