Skip to main content
SpringerLink
Log in

Topological changes in white matter connectivity network in patients with Parkinson’s disease and depression

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

Depression is the most common non-motor symptom accompanying Parkinson’s disease (PD) with high prevalence but unclear pathophysiological mechanism. Relatively little is known about the topological patterns of white matter structural networks in depressed patients with PD. In this study, we used diffusion-tensor imaging (DTI) and graph theory approaches to explore the brain structural connectome in non-depressed patients with PD (n = 47), depressed patients with PD (n = 20) and healthy controls (n = 46). All three groups exhibited small-world topology. Compared with healthy controls, non-depressed patients with PD and depressed patients with PD showed a significant reduction of network efficiency in the cortico-subcortical circuits. Moreover, depressed patients with PD exhibited higher network efficiency in fronto-limbic system, compared to non-depressed patients with PD. To sum up, our data indicated a disrupted integrity in the large-scale brain systems in depressed patients with PD patients. The structural connectome provided a basis for functional alterations in depressed patients with PD that may advance our current understanding of pathophysiological mechanism underlying Parkinson’s disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ajilore, O., Lamar, M., et al. (2014). Graph theory analysis of cortical-subcortical networks in late-life depression. The American Journal of Geriatric Psychiatry, 22(2), 195–206.

    Article  PubMed  Google Scholar 

  • Bai, F., Shu, N., et al. (2012). Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. The Journal of Neuroscience, 32(12), 4307–4318.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cui, Z., Zhong, S., et al. (2013). PANDA: A pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience, 7, 42.

    PubMed  PubMed Central  Google Scholar 

  • Fornito, A., Zalesky, A., et al. (2015). The connectomics of brain disorders. Nature Reviews. Neuroscience, 16(3), 159–172.

    Article  CAS  PubMed  Google Scholar 

  • Gibb, W. R., & Lees, A. J. (1988). The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease. Journal of Neurology, Neurosurgery, and Psychiatry, 51(6), 745–752.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gou, L., Zhang, W., et al. (2018). Structural brain network alteration and its correlation with structural impairments in patients with depression in de novo and drug-naive Parkinson's disease. Frontiers in Neurology, 9, 608.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hagmann, P., Cammoun, L., et al. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.

    Article  PubMed  PubMed Central  Google Scholar 

  • He, Y., Chen, Z., et al. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. The Journal of Neuroscience, 28(18), 4756–4766.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Honey, C. J., Sporns, O., et al. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 2035–2040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hosseini, S. H., Hoeft, F., et al. (2012). GAT: A graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One, 7(7), e40709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hou, Y., Wei, Q., et al. (2018). Impaired topographic organization in cognitively unimpaired drug-naive patients with rigidity-dominant Parkinson's disease. Parkinsonism & Related Disorders, 56, 52–57.

    Article  Google Scholar 

  • Hu, X., Song, X., et al. (2015). Abnormal functional connectivity of the amygdala is associated with depression in Parkinson's disease. Movement Disorders, 30(2), 238–244.

    Article  PubMed  Google Scholar 

  • Huang, P., Xu, X., et al. (2014). Disrupted white matter integrity in depressed versus non-depressed Parkinson's disease patients: A tract-based spatial statistics study. Journal of the Neurological Sciences, 346(1-2), 145–148.

    Article  PubMed  Google Scholar 

  • Jin, C., Gao, C., et al. (2011). A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression. Neuroscience Letters, 503(2), 105–109.

    Article  CAS  PubMed  Google Scholar 

  • Lee, T., Lee, H. B., et al. (2016). Increased suicide risk and clinical correlates of suicide among patients with Parkinson's disease. Parkinsonism & Related Disorders, 32, 102–107.

    Article  Google Scholar 

  • Leentjens, A. F., Verhey, F. R., et al. (2000). The validity of the Hamilton and Montgomery-Asberg depression rating scales as screening and diagnostic tools for depression in Parkinson's disease. International Journal of Geriatric Psychiatry, 15(7), 644–649.

    Article  CAS  PubMed  Google Scholar 

  • Liu, Y., Duan, Y., et al. (2012). Altered topological organization of white matter structural networks in patients with neuromyelitis optica. PLoS One, 7(11), e48846.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mears, D., & Pollard, H. B. (2016). Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. Journal of Neuroscience Research, 94(6), 590–605.

    Article  CAS  PubMed  Google Scholar 

  • Mori, S., & van Zijl, P. C. (2002). Fiber tracking: Principles and strategies - a technical review. NMR in Biomedicine, 15(7-8), 468–480.

    Article  PubMed  Google Scholar 

  • Nigro, S., Riccelli, R., et al. (2016). Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging. Human Brain Mapping, 37(12), 4500–4510.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Petrovic, I. N., Stefanova, E., et al. (2012). White matter lesions and depression in patients with Parkinson's disease. Journal of the Neurological Sciences, 322(1-2), 132–136.

    Article  PubMed  Google Scholar 

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.

    Article  Google Scholar 

  • Sheng, K., Fang, W., et al. (2014). Altered spontaneous brain activity in patients with Parkinson's disease accompanied by depressive symptoms, as revealed by regional homogeneity and functional connectivity in the prefrontal-limbic system. PLoS One, 9(1), e84705.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shu, N., Liu, Y., et al. (2009). Altered anatomical network in early blindness revealed by diffusion tensor tractography. PLoS One, 4(9), e7228.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sporns, O., Chialvo, D. R., et al. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8(9), 418–425.

    Article  PubMed  Google Scholar 

  • Suo, X., Lei, D., et al. (2017). Anatomic insights into disrupted small-world networks in pediatric posttraumatic stress disorder. Radiology, 282(3), 826–834.

    Article  PubMed  Google Scholar 

  • Surdhar, I., Gee, M., et al. (2012). Intact limbic-prefrontal connections and reduced amygdala volumes in Parkinson's disease with mild depressive symptoms. Parkinsonism & Related Disorders, 18(7), 809–813.

    Article  Google Scholar 

  • Tadayonnejad, R., & Ajilore, O. (2014). Brain network dysfunction in late-life depression: A literature review. Journal of Geriatric Psychiatry and Neurology, 27(1), 5–12.

    Article  PubMed  Google Scholar 

  • Tinaz, S., Lauro, P. M., et al. (2017). Changes in functional organization and white matter integrity in the connectome in Parkinson's disease. Neuroimage Clin, 13, 395–404.

    Article  PubMed  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440–442.

    Article  CAS  PubMed  Google Scholar 

  • Wen, M. C., Chan, L. L., et al. (2016). Depression, anxiety, and apathy in Parkinson's disease: Insights from neuroimaging studies. European Journal of Neurology, 23(6), 1001–1019.

    Article  PubMed  Google Scholar 

  • Zhang, T. M., Yu, S. Y., et al. (2016). Nonmotor symptoms in patients with Parkinson disease: A cross-sectional observational study. Medicine (Baltimore), 95(50), e5400.

    Article  Google Scholar 

  • Zhang, Z., Liao, W., et al. (2011). Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain, 134(Pt 10), 2912–2928.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to thank all the participants. In addition, this work was supported by the National Natural Science Foundation of China (61131003, 81571348, 81701671), the Nanjing Medical University of Science and Technology Development fund projects (2016NJMU075), National key research and development plan (2016YFC1306600,2017YFC1310302), and Jiangsu Natural Science Foundation (BK20151077).

Funding

The National Natural Science Foundation of China funded this study (no. 61131003, 81,571,348, 81,701,671).

Author information

Author notes

  1. Xiao Hu and Long Qian equally contributed to this work.

Authors and Affiliations

  1. Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China

    Xiao Hu

  2. Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, 210029, China

    Xiao Hu & Xiangrong Zhang

  3. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China

    Long Qian & Yaoyu Zhang

  4. GE Healthcare, MR Research China, Beijing, 100088, China

    Long Qian

  5. Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China

    Yuanyuan Xu & Weiguo Liu

  6. Department of Biomedical Engineering, Peking University, Beijing, 100871, China

    Li Zheng & Yijun Liu

  7. Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China

    Xiangrong Zhang

  8. Department of Biomedical Engineering, Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, China

    Yi Zhang

Authors
  1. Xiao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Long Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yaoyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanyuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yijun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiangrong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Weiguo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yi Zhang or Weiguo Liu.

Ethics declarations

Conflict of interest

The authors reported no biomedical financial interests or potential conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, X., Qian, L., Zhang, Y. et al. Topological changes in white matter connectivity network in patients with Parkinson’s disease and depression. Brain Imaging and Behavior 14, 2559–2568 (2020). https://doi.org/10.1007/s11682-019-00208-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-019-00208-2

Keywords

Search

Navigation