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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children
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| 英文论文题目: |
Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children
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| 论文题目英文: |
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| 作者: |
Wen, Hongwei
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| 论文出处: |
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| 刊物名称: |
HUMAN BRAIN MAPPING
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| 年: |
2017
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| 卷: |
38 |
| 期: |
8 |
| 页: |
3988-4008 |
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| 影响因子: |
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| 摘要: |
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. (c) 2017 Wiley Periodicals, Inc. |
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