Abstract
Tourette syndrome (TS) is a childhood-onset neurological disorder. To date, accurate TS diagnosis remains challenging due to its varied clinical expressions and dependency on qualitative description of symptoms. Therefore, identifying accurate and objective neuroimaging biomarkers may help improve early TS diagnosis. As resting-state functional MRI (rs-fMRI) has been demonstrated as a promising neuroimaging tool for TS diagnosis, previous rs-fMRI studies on TS revealed functional connectivity (FC) changes in a few local brain networks or circuits. However, no study explored the disrupted topological organization of whole-brain FC networks in TS children. Meanwhile, very few studies have examined brain functional networks using machine-learning methods for diagnostics. In this study, we construct individual whole-brain, ROI-level FC networks for 29 drug-naive TS children and 37 healthy children. Then, we use graph theory analysis to investigate the topological disruptions between groups. The identified disrupted regions in FC networks not only involved the sensorimotor association regions but also the visual, default-mode and language areas, all highly related to TS. Furthermore, we propose a novel classification framework based on similarity network fusion (SNF) algorithm, to both diagnose an individual subject and explore the discriminative power of FC network topological properties in distinguishing between TS children and controls. We achieved a high accuracy of 88.79%, and the involved discriminative regions for classification were also highly related to TS. Together, both the disrupted topological properties between groups and the discriminative topological features for classification may be considered as comprehensive and helpful neuroimaging biomarkers for assisting the clinical TS diagnosis.
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Abbreviations
- TS:
-
Tourette syndrome
- rs-fMRI:
-
Resting-state functional magnetic resonance imaging
- FC:
-
Functional connectivity
- DTI:
-
Fiffusion tensor imaging
- WM:
-
White matter
- YGTSS:
-
Yale Global Tic Severity Scale
- SNF:
-
Similarity network fusion
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Acknowledgements
We thank Dr. Hao Huang at University of Pennsylvania for consultation and support on MR pulse sequences.
This work was supported by the National Natural Science Foundation of China (91520202, 61271151, 31271161, 81671651), Youth Innovation Promotion Association CAS and Beijing Municipal Administration of Hospitals Incubating Program (PX2016035), and Beijing Health System Top level Health Technical Personnel Training Plan (2015-3-082).
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Wen, H., Liu, Y., Rekik, I. et al. Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children. Mol Neurobiol 55, 3251–3269 (2018). https://doi.org/10.1007/s12035-017-0519-1
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DOI: https://doi.org/10.1007/s12035-017-0519-1