Identification of Functional Connectivity Features in Depression Subtypes Using a Data-Driven Approach

  • Xingjuan LiEmail author
  • Samantha Burnham
  • Jurgen Fripp
  • Yu Li
  • Xue Li
  • Amir Fazlollahi
  • Pierrick Bourgeat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)


Biomarkers are not well understood in depression, partly because there is no golden rule of what is abnormal in which patients and how neurobiological information can be used to improve diagnosis. The heterogeneity of depression suggests that diverse circuit-level abnormalities in individuals lead to various symptoms. Investigating heterogeneous depression is crucial to understand disease mechanisms and provide personalised medicine. Dynamical functional connectivity (dFC), consisting of spatial-temporal characteristics of brain activity, has been shown to be effective in characterizing the circuit-level abnormalities in depression. However, most of the current studies on dFC are based on one-step mapping while ignoring hierarchical spatial-temporal information, which may leverage the power of diagnosis in depression subtypes. In this study, we propose Brain Network Gated Recurrent Units (BrainNetGRU) to discover hierarchical resting-state dFC features for the diagnosis of depression subtypes, using data from 770 depressive adults from the UK Biobank. Particularly, we devise diffusion convolutional filters and recurrent units to effectively learn distinctive dynamic brain connectivity for depression subtypes. Experimental results show that BrainNetGRU can identify three types of depression with an accuracy of 72.05%. In addition, BrainNetGRU shows that resting-state functional connections in default mode network (DMN), cingulo-opercular network (CON) and fronto-parietal network (FPN) are important in the diagnosis of depression subtypes.


Dynamical functional connectivity Depression subtypes Resting-state fMRI Deep learning 


  1. 1.
    Alfaro-Almagro, F., et al.: Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018)CrossRefGoogle Scholar
  2. 2.
    Barua, S., Islam, M.M., Yao, X., Murase, K.: MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405–425 (2012)CrossRefGoogle Scholar
  3. 3.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
  4. 4.
    Demirtaş, M., et al.: Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Hum. Brain Mapp. 37(8), 2918–2930 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Drysdale, A.T., et al.: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23(1), 28 (2017)CrossRefGoogle Scholar
  6. 6.
    Khambhati, A.N., Sizemore, A.E., Betzel, R.F., Bassett, D.S.: Modeling and interpreting mesoscale network dynamics. NeuroImage 180, 337–349 (2018)CrossRefGoogle Scholar
  7. 7.
    Li, X., Li, Y., Cui, J.: Estimating interactions of functional brain connectivity by hidden Markov models. In: Gan, G., Li, B., Li, X., Wang, S. (eds.) ADMA 2018. LNCS (LNAI), vol. 11323, pp. 403–412. Springer, Cham (2018). Scholar
  8. 8.
    Lieblich, S.M., Castle, D.J., Pantelis, C., Hopwood, M., Young, A.H., Everall, I.P.: High heterogeneity and low reliability in the diagnosis of major depression will impair the development of new drugs. BJPsych open 1(2), e5–e7 (2015)CrossRefGoogle Scholar
  9. 9.
    Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)CrossRefGoogle Scholar
  10. 10.
    Shen, X., et al.: Resting-state connectivity and its association with cognitive performance, educational attainment, and household income in the UK Biobank. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3(10), 878–886 (2018)CrossRefGoogle Scholar
  11. 11.
    Yan, W., Zhang, H., Sui, J., Shen, D.: Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 249–257. Springer, Cham (2018). Scholar
  12. 12.
    Zeng, L.L., et al.: Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135(5), 1498–1507 (2012)CrossRefGoogle Scholar
  13. 13.
    Zhu, Q., Li, H., Huang, J., Xu, X., Guan, D., Zhang, D.: Hybrid functional brain network with first-order and second-order information for computer-aided diagnosis of schizophrenia. Front. Neurosci. 13, 603 (2019)CrossRefGoogle Scholar
  14. 14.
    Zhu, X., et al.: Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biol. Psychiatry 71(7), 611–617 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CSIRO Australian eHealth Research CentreBrisbaneAustralia
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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