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Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children

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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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  1. Hongwei Wen and Yue Liu contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Hongwei Wen, Shengpei Wang, Zhiqiang Chen & Huiguang He

  2. Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Hongwei Wen, Shengpei Wang, Zhiqiang Chen & Huiguang He

  3. University of Chinese Academy of Sciences, Beijing, China

    Hongwei Wen, Shengpei Wang, Zhiqiang Chen & Huiguang He

  4. Department of Radiology, Beijing Children’s Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China

    Yue Liu, Yue Zhang & Yun Peng

  5. CVIP, Computing, School of Science and Engineering, University of Dundee, Dundee, UK

    Islem Rekik

  6. Department of Neurology, Beijing Children’s Hospital, Capital Medical University, Beijing, China

    Jishui Zhang

  7. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China

    Huiguang He

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  1. Hongwei Wen
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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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