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Estimating Interactions of Functional Brain Connectivity by Hidden Markov Models

  • Xingjuan Li
  • Yu Li
  • Jiangtao Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

The brain activity reflected by functional magnetic resonance imaging (fMRI) is temporally organized as a combination of sensory inputs from environment and its own spontaneous activity. However, temporal patterns of brain activity in a large number of subjects remain unclear. We propose a regularized hidden Markov model (HMM) to estimate dynamic functional connectivity among distributed brain regions and discover repeating connectivity patterns from resting-state functional connectivity across a group of subjects. We found that functional brain connectivity are hierarchically organized and exhibit three repeated patterns across subjects with attention deficit hyperactivity disorder (ADHD). We have examined the temporal characteristics of functional connectivity by its occupancy. And we validated our method by comparing the classification performance with state-of-the-art methods using the same dataset. Experimental results show that our method can improve the classification performance compared to other functional connectivity modelling methods.

Keywords

Functional brain connectivity Dynamical modelling Hidden Markov models 

References

  1. 1.
    Bassett, D.S., Wymbs, N.F., Porter, M.A., Mucha, P.J., Carlson, J.M., Grafton, S.T.: Dynamic reconfiguration of human brain networks during learning. Proc. Nat. Acad. Sci. 108(18), 7641–7646 (2011)CrossRefGoogle Scholar
  2. 2.
    Betzel, R.F., Fukushima, M., He, Y., Zuo, X.N., Sporns, O.: Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. NeuroImage 127, 287–297 (2016)CrossRefGoogle Scholar
  3. 3.
    Binnewijzend, M.A.A., et al.: Brain network alterations in Alzheimer’s disease measured by Eigenvector centrality in fMRI are related to cognition and CSF biomarkers. Hum. Brain Map. 35(5), 2383–2393 (2014)CrossRefGoogle Scholar
  4. 4.
    Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L.: The brain’s default network. Ann. N. Y. Acad. Sci. 1124(1), 1–38 (2008)CrossRefGoogle Scholar
  5. 5.
    Calhoun, V.D., Adali, T., McGinty, V.B., Pekar, J.J., Watson, T.D., Pearlson, G.D.: fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis. NeuroImage 14(5), 1080–1088 (2001)CrossRefGoogle Scholar
  6. 6.
    Chai, X.J., Castañón, A.N., Öngür, D., Whitfield-Gabrieli, S.: Anticorrelations in resting state networks without global signal regression. Neuroimage 59(2), 1420–1428 (2012)CrossRefGoogle Scholar
  7. 7.
    Chang, C., Glover, G.H.: Time–frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50(1), 81–98 (2010)CrossRefGoogle Scholar
  8. 8.
    de Haan, W., van der Wiesje, M., Flier, T.K., Smits, L.L., Scheltens, P., Stam, C.J.: Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer’s disease. Neuroimage 59(4), 3085–3093 (2012)CrossRefGoogle Scholar
  9. 9.
    De Pasquale, F., et al.: Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Nat. Acad. Sci. 107(13), 6040–6045 (2010)CrossRefGoogle Scholar
  10. 10.
    Fan, J., McCandliss, B.D., Fossella, J., Flombaum, J.I., Posner, M.I.: The activation of attentional networks. Neuroimage 26(2), 471–479 (2005)CrossRefGoogle Scholar
  11. 11.
    Hindriks, R., et al.: Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage 127, 242–256 (2016)CrossRefGoogle Scholar
  12. 12.
    Hoekzema, E., et al.: An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD. Hum. Brain Map. 35(4), 1261–1272 (2014)CrossRefGoogle Scholar
  13. 13.
    Kiviniemi, V., et al.: A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Connect. 1(4), 339–347 (2011)CrossRefGoogle Scholar
  14. 14.
    Liégeois, R., et al.: Cerebral functional connectivity periodically (de) synchronizes with anatomical constraints. Brain Struct. Funct. 221(6), 2985–2997 (2016)CrossRefGoogle Scholar
  15. 15.
    Pa, J., Hickok, G.: A parietal–temporal sensory–motor integration area for the human vocal tract: Evidence from an fMRI study of skilled musicians. Neuropsychologia 46(1), 362–368 (2008)CrossRefGoogle Scholar
  16. 16.
    Preti, M., Bolton, T.A.W., Van De Ville, D.: The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160, 41–54 (2017)CrossRefGoogle Scholar
  17. 17.
    Rolls, E.T., Joliot, M., Tzourio-Mazoyer, N.: Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage 122, 1–5 (2015)CrossRefGoogle Scholar
  18. 18.
    Song, J., Nair, V.A., Gaggl, W., Prabhakaran, V.: Disrupted brain functional organization in epilepsy revealed by graph theory analysis. Brain Connect. 5(5), 276–283 (2015)CrossRefGoogle Scholar
  19. 19.
    Tang, W., et al.: Dynamic connectivity modulates local activity in the core regions of the default-mode network. Proc. Nat. Acad. Sci. 114(36), 9713–9718 (2017)CrossRefGoogle Scholar
  20. 20.
    Vidaurre, D., Smith, S.M., Woolrich, M.W.: Brain network dynamics are hierarchically organized in time. Proc. Nat. Acad. Sci. 114(48), 12827–12832 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Information Technology and Electrical EngineeringUniversity of QueenslandBrisbaneAustralia
  2. 2.School of Computer Science and TechnologyXidian UniversityXi’anChina

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