TY - JOUR
T1 - Disrupted Topological Organization of Structural Networks revealed by Probabilistic Diffusion Tractography in Tourette Syndrome Children
AU - Wen, Hongwei
AU - Liu, Yue
AU - Rekik, Islem
AU - Wang, Shengpei
AU - Zhang, Jishui
AU - Zhang, Yue
AU - Peng, Yun
AU - He, Huiguang
N1 - Funding: National Natural Science Foundation of China (Grant Numbers: 91520202 , 61271151 , 31271161 , 81671651); Youth Innovation Promotion Association CAS Beijing Municipal Administration of Hospitals Incubating Program (Grant Number: PX2016035); Beijing Health System Top Level Health Technical Personnel Training Plan (Grant Number: 2015-3-082).
PY - 2017/7/5
Y1 - 2017/7/5
N2 - 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 gendermatched 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.
AB - 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 gendermatched 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.
KW - Tourette syndrome
KW - diffusion MRI
KW - probabilistic tractography
KW - structural network
KW - graph theory
KW - topological organization
KW - multiple kernel learning
U2 - 10.1002/hbm.23643
DO - 10.1002/hbm.23643
M3 - Article
C2 - 28474385
SN - 1065-9471
VL - 38
SP - 3988
EP - 4008
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 8
ER -