Node-Based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs

  • Bernard NgEmail author
  • Anna-Clare Milazzo
  • Andre Altmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)


Despite that the bulk of our knowledge on brain function is established around brain regions, current methods for comparing connectivity graphs largely take an edge-based approach with the aim of identifying discriminative connections. In this paper, we explore a node-based Gaussian Graphical Model (NBGGM) that facilitates identification of brain regions attributing to connectivity differences seen between a pair of graphs. To enable group analysis, we propose an extension of NBGGM via incorporation of stability selection. We evaluate NBGGM on two functional magnetic resonance imaging (fMRI) datasets pertaining to within and between-group studies. We show that NBGGM more consistently selects the same brain regions over random data splits than using node-based graph measures. Importantly, the regions found by NBGGM correspond well to those known to be involved for the investigated conditions.


Brain Connectivity fMRI Node-Based Gaussian Graphical Model 


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  1. 1.
    Li, K., Guo, L., Nie, J., Li, G., Liu, T.: Review of methods for functional brain connectivity detection using fMRI. Comput. Med. Imaging Graph. 33, 131–139 (2009)CrossRefGoogle Scholar
  2. 2.
    Friston, K., Holmes, A., Worsley, K., Poline, J.B., Frith, C., Frackowiak, R.: Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapp. 2, 189–210 (1995)CrossRefGoogle Scholar
  3. 3.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  4. 4.
    Venkataraman, A., Kubicki, M., Golland, P.: From connectivity models to region labels: identifying foci of a neurological disorder. IEEE Trans. Med. Imaging 32, 2078–2098 (2013)CrossRefGoogle Scholar
  5. 5.
    Mohan, K., London, P., Fazel, M., Witten, D., Lee, S.: Node-based learning of multiple Gaussian graphical models. J. Mach. Learn. Research 15, 445–488 (2014)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Meinshausen, N., Bühlmann, P.: Stability Selection. J. Roy. Statist. Soc. Ser. B 72, 417–473 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Royal Stat. Soc. Series B 68, 49–67 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Shirer, W.R., Ryali, S., Rykhlevskaia, E., Menon, V., Greicius, M.D.: Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22, 158–165 (2012)CrossRefGoogle Scholar
  9. 9.
    Nichols, T., Hayasaka, S.: Controlling the familywise error rate in functional neuroimaging: A comparative review. Stat. Methods Med. Research 12, 419–446 (2003)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Damasio, A.R., Grabowski, T.J., Bechara, A., Damasio, H., Ponto, L.L., Parvizi, J., Hichwa, R.D.: Subcortical and cortical brain activity during the feeling of self-generated emotions. Nat. Neurosci. 3, 1049–1056 (2000)CrossRefGoogle Scholar
  11. 11.
    Vogt, B.A.: Pain and emotion interactions in subregions of the cingulate gyrus. Nat. Rev. Neurosci. 6, 533–544 (2005)CrossRefGoogle Scholar
  12. 12.
    Ochsner, K.N., Knierim, K., Ludlow, D.H., Hanelin, J., Ramachandran, T., Glover, G., Mackey, S.C.: Reflecting upon feelings: an fMRI study of neural systems supporting the attribution of emotion to self and other. J. Cogn. Neurosci. 16, 1746–1772 (2004)CrossRefGoogle Scholar
  13. 13.
    Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V.: Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. USA 101, 4637–4642 (2004)CrossRefGoogle Scholar
  14. 14.
    Seghier, M.L.: The angular gyrus: Multiple functions and multiple subdivisions. Neuroscientist 19, 43–61 (2013)CrossRefGoogle Scholar
  15. 15.
    Ranasinghe, K.G., Hinkley, L.B., Beagle, A.J., Mizuiri, D., Dowling, A.F., Honma, S.M., Finucane, M.M., Scherling, C., Miller, B.L., Nagarajan, S.S., Vossel, K.A.: Regional functional connectivity predicts distinct cognitive impairments in Alzheimer’s disease spectrum. Neuroimage Clin. 23, 385–395 (2014)CrossRefGoogle Scholar

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Authors and Affiliations

  • Bernard Ng
    • 1
    • 2
    Email author
  • Anna-Clare Milazzo
    • 1
  • Andre Altmann
    • 1
  1. 1.FIND LabStanford UniversityStanfordUSA
  2. 2.Parietal Team, NeurospinINRIA SaclayParisFrance

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