Advertisement

Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-State fMRI

  • Guoshi Li
  • Yujie Liu
  • Yanting Zheng
  • Ye Wu
  • Pew-Thian Yap
  • Shijun Qiu
  • Han ZhangEmail author
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11766)

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) studies have focused primarily on characterizing functional or effective connectivity of discrete brain regions. A major drawback of this approach is that it does not provide a mechanistic understanding of brain cognitive function or dysfunction at cellular and circuit levels. To overcome this limitation, we combined the methods of computational neuroscience with traditional macroscale connectomic analysis and developed a Multiscale Neural Model Inversion (MNMI) framework that links microscale circuit interaction with macroscale network dynamics and estimates both local coupling and inter-regional connections via stochastic optimization based on blood oxygen-level dependent (BOLD) rs-fMRI. We applied this method to the rs-fMRI data of 66 normal healthy subjects and 66 individuals with major depressive disorder (MDD) to identify potential biomarkers at both local circuit and global network level. Results suggest that the recurrent excitation and inhibition within the dorsal lateral prefrontal cortex (dlPFC) might be disrupted in MDD, consistent with the commonly accepted hypothetical model of MDD. In addition, recurrent excitation in the thalamus was found to be abnormally elevated, which may be responsible to abnormal thalamocortical oscillations often observed in MDD. Overall, our modeling approach holds the promise to overcome the limitation of traditional large-scale connectome modeling by providing hidden mechanistic insights into neuroanatomy, circuit dynamics and pathophysiology.

Keywords

Neural mass model Functional connectivity Resting-state fMRI Major depressive disorder Stochastic optimization 

Notes

Acknowledgements

This work was supported in part by NIH grants MH108560, EB022880, AG042599, and AG041721.

References

  1. 1.
    Sporns, O.: Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17(5), 652–660 (2014)CrossRefGoogle Scholar
  2. 2.
    van den Heuvel, M.P., Hulshoff Pol, H.E.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519–534 (2010)CrossRefGoogle Scholar
  3. 3.
    Li, K., et al.: Review of methods for functional brain connectivity detection using fMRI. Comput. Med. Imaging Graph. 33(2), 131–139 (2009)CrossRefGoogle Scholar
  4. 4.
    Friston, K.J., Harrison, L., Penny, W.: Dynamic causal modeling. NeuroImage 19(4), 1273–1302 (2003)CrossRefGoogle Scholar
  5. 5.
    Marreiros, A.C., Kiebel, S.J., Friston, K.J.: Dynamic causal modelling for fMRI: a two-state model. NeuroImage 39(1), 269–278 (2008)CrossRefGoogle Scholar
  6. 6.
    Friston, K.J., et al.: Dynamic causal modelling revisited. NeuroImage 199, 730–744 (2019)CrossRefGoogle Scholar
  7. 7.
    Wang, P., Kong, R., Kong, X., et al.: Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain. Sci. Adv. 5(1), eaat7854 (2019)CrossRefGoogle Scholar
  8. 8.
    Wilson, H.R., Cowan, J.D.: Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12(1), 1–24 (1972)CrossRefGoogle Scholar
  9. 9.
    Kimbrell, T.A., et al.: Regional cerebral glucose utilization in patients with a range of severities of unipolar depression. Biol. Psychiatry 51(3), 237–252 (2002)CrossRefGoogle Scholar
  10. 10.
    Kumari, V., et al.: Neural abnormalities during cognitive generation of affect in treatment-resistant depression. Biol. Psychiatry 54(8), 777–791 (2003)CrossRefGoogle Scholar
  11. 11.
    Smith, R.E., et al.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62(3), 1924–1938 (2012)CrossRefGoogle Scholar
  12. 12.
    Tournier, J., Calamante, F., Connelly, A.: Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In: Proceedings of the 18th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Stockholm, Sweden, p. 1670 (2010)Google Scholar
  13. 13.
    Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3), 968–980 (2006)CrossRefGoogle Scholar
  14. 14.
    Zheng, Y., et al.: Treatment-naïve first episode depression classification based on high-order brain functional network. J. Affect. Dis. 256, 33–41 (2019)CrossRefGoogle Scholar
  15. 15.
    Pandya, M., et al.: Where in the brain is depression? Curr. Psychiatry Rep. 14(6), 634–642 (2012)CrossRefGoogle Scholar
  16. 16.
    Price, J.L., Drevets, W.C.: Neurocircuitry of mood disorders. Neuropsychopharmacology 35(1), 192–216 (2010)CrossRefGoogle Scholar
  17. 17.
    Dutta, A., McKie, S., Deakin, J.F.: Resting state networks in major depressive disorder. Psychiatry Res. Neuroimaging 224(3), 139–151 (2014)CrossRefGoogle Scholar
  18. 18.
    Abeysuriya, R.G., et al.: A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Comput. Biol. 14(2), e1006007 (2018)CrossRefGoogle Scholar
  19. 19.
    Becker, R., et al.: Relating alpha power and phase to population firing and hemodynamic activity using a thalamo-cortical neural mass model. PLoS Comput. Biol. 11(9), 1–23 (2015)CrossRefGoogle Scholar
  20. 20.
    Leuchter, A.F., et al.: The relationship between brain oscillatory activity and therapeutic effectiveness of transcranial magnetic stimulation in the treatment of major depressive disorder. Front. Hum. Neurosci. 7, 37 (2013)CrossRefGoogle Scholar
  21. 21.
    Li, G., Henriquez, C.S., Fröhlich, F.: Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation. PLoS Comput. Biol. 13(10), e1005797 (2017)CrossRefGoogle Scholar
  22. 22.
    Llinás, R.R., et al.: Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc. Natl. Acad. Sci. U.S.A. 96(26), 15222–15227 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guoshi Li
    • 1
  • Yujie Liu
    • 1
    • 2
  • Yanting Zheng
    • 1
    • 2
  • Ye Wu
    • 1
  • Pew-Thian Yap
    • 1
  • Shijun Qiu
    • 3
  • Han Zhang
    • 1
    Email author
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouChina
  3. 3.Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina

Personalised recommendations