Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)

Abstract

Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (fMRI) and diffusion MRI (dMRI) data, allows for subnetwork overlaps, has an intrinsic criterion for setting the number of subnetworks, and provides statistical control on false node inclusion in the identified subnetworks via the incorporation of stability selection. We thus refer to our technique as coupled stable overlapping replicator dynamics (CSORD). On synthetic data, we demonstrate that CSORD achieves significantly higher subnetwork identification accuracy than state-of-the-art techniques. On real data from the Human Connectome Project (HCP), we show that CSORD attains improved test-retest reliability on multiple network measures and superior task classification accuracy.

Keywords

Brain Connectivity dMRI fMRI Multimodal integration Overlapping community detection Replicator dynamics Stability selection 

Notes

Acknowledgments

Bernard Ng is supported by the Lucile Packard Foundation for Children’s Health, Stanford NIH-NCATS-CTSA UL1 TR001085 and Child Health Research Institute of Stanford University.

References

  1. 1.
    Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echoplanar MRI. Magn. Reson. Med. 34, 537–541 (1995)CrossRefGoogle Scholar
  2. 2.
    McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.-P., Kindermann, S.S., Bell, A.J., Sejnowski, T.J.: Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6, 160–188 (1998)CrossRefGoogle Scholar
  3. 3.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wu, K., Taki, Y., Sato, K., Sassa, Y., Inoue, K., Goto, R., Okada, K., Kawashima, R., He, Y., Evans, A.C., Fukuda, H.: The overlapping community structure of structural brain network in young healthy individuals. PLoS ONE 6, e19608 (2011)CrossRefGoogle Scholar
  5. 5.
    Damoiseaux, J.S., Greicius, M.D.: Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct. Funct. 213, 525–533 (2009)CrossRefGoogle Scholar
  6. 6.
    Ng, B., Varoquaux, G., Poline, J.B., Thirion, B.: Implications of inconsistencies between fMRI and dMRI on multimodal connectivity estimation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 652–659. Springer, Heidelberg (2013)Google Scholar
  7. 7.
    Ng, B., Varoquaux, G., Poline, J.B., Thirion, B.: A novel sparse graphical approach for multimodal brain connectivity inference. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 707–714. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    Hinne, M., Ambrogioni, L., Janssen, R.J., Heskes, T., van Gerven, M.A.: Structurally-informed bayesian functional connectivity analysis. NeuroImage 86, 294–305 (2014)CrossRefGoogle Scholar
  9. 9.
    Venkataraman, A., Rathi, Y., Kubicki, M., Westin, C.F., Golland, P.: Joint modeling of anatomical and functional connectivity for population studies. IEEE Trans. Med. Imaging 31, 164–182 (2012)CrossRefGoogle Scholar
  10. 10.
    Chen, H., Li, K., Zhu, D., Jiang, X., Yuan, Y., Lv, P., Zhang, T., Guo, L., Shen, D., Liu, T.: Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. IEEE Trans. Med. Imaging 32, 1576–1586 (2013)CrossRefGoogle Scholar
  11. 11.
    Dodero, L., Gozzi, A., Liska, A., Murino, V., Sona, D.: Group-Wise functional community detection through joint laplacian diagonalization. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 708–715. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Yoldemir, B., Ng, B., Abugharbieh, R.: Overlapping replicator dynamics for functional subnetwork identification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 682–689. Springer, Heidelberg (2013)Google Scholar
  13. 13.
    Schuster, P., Sigmund, K.: Replicator dynamics. J. Theor. Biol. 100, 533–538 (1983)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Karlin, S.: Mathematical models, problems, and controversies of evolutionary theory. Bulletin Ame. Math. Soc. 10, 221–273 (1984)MATHMathSciNetCrossRefGoogle Scholar
  15. 15.
    Ng, B., Yoldemir, B., Abugharbieh, R.: Stable overlapping replicator dynamics for subnetwork identification. In: NIPS Workshop on Networks, pp. 461–473 (2014)Google Scholar
  16. 16.
    Meinshausen, N., Bühlmann, P.: Stability selection. J. Roy. Statist. Soc. Ser. B 72, 417–473 (2010)CrossRefGoogle Scholar
  17. 17.
    Ng, B., McKeown, M.J., Abugharbieh, R.: Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection. IEEE Trans. Med. Imaging 31, 576–585 (2012)CrossRefGoogle Scholar
  18. 18.
    Lohmann, G., Bohn, S.: Using replicator dynamics for analyzing fMRI data of human brain. IEEE Trans. Med. Imaging 21, 485–492 (2002)CrossRefGoogle Scholar
  19. 19.
    Thirion, B., Pinel, P., Tucholka, A., Roche, A., Ciuciu, P., Mangin, J.-F., Poline, J.B.: Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies. IEEE Trans. Med. Imaging 26, 1256–1269 (2007)CrossRefGoogle Scholar
  20. 20.
    Torsello, A., Bulò, S.R., Pelillo, M.: Beyond partitions: allowing overlapping groups in pairwise clustering. In: Proceedings of International Conference Pattern Recognition, pp. 1–4 (2008)Google Scholar
  21. 21.
    Efron, B.: The jackknife, the bootstrap, and other resampling plans. Society of Industrial and Applied Mathematics CBMS-NSF Monographs (1982)Google Scholar
  22. 22.
    Lazar, M., Alexander, A.L.: Bootstrap white matter tractography (BOOT-TRAC). NeuroImage 24, 524–532 (2005)CrossRefGoogle Scholar
  23. 23.
    Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K.: The wu-minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)CrossRefGoogle Scholar
  24. 24.
    Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., Van Essen, D.C., Jenkinson, M.: The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105–124 (2013)CrossRefGoogle Scholar
  25. 25.
    Behzadi, Y., Restom, K., Liau, J., Liu, T.T.: A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90–101 (2007)CrossRefGoogle Scholar
  26. 26.
    Michel, V., Gramfort, A., Varoquaux, G., Eger, E., Keribin, C., Thirion, B.: A supervised clustering approach for fmri-based inference of brain states. Pattern Recogn. 45, 2041–2049 (2012)MATHCrossRefGoogle Scholar
  27. 27.
    Neher, P., Stieltjes, B., Reisert, M., Reicht, I., Meinzer, H., Fritzsche, K.: MITK global tractography. In: Proceeding of SPIE Medical Imaging, p. 83144D (2012)Google Scholar
  28. 28.
    Van Den Heuvel, M., Mandl, R., Hulshoff Pol, H.: Normalized cut group clustering of resting-state fMRI data. PLoS ONE 3, e2001 (2008)CrossRefGoogle Scholar
  29. 29.
    Kuhn, H.W.: The hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)CrossRefGoogle Scholar
  30. 30.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  31. 31.
    Wang, Q., Fleury, E.: Overlapping community structure and modular overlaps in complex networks. In: Ozyer, T., Erdem, Z., Rokne, J., Khoury, S. (eds.) LNSN, pp. 15–40. Springer, Netherlands (2013)Google Scholar
  32. 32.
    Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., Beckmann, C.F.: Correspondence of the brain’s functional architecture during activation and rest. In: Proceedings National Academy of Sciences, U.S.A. 106, pp. 13040–13045 (2009)Google Scholar
  33. 33.
    Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007)CrossRefGoogle Scholar
  34. 34.
    Van Den Heuvel, M., Hulshoff Pol, H.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharm. 20, 519–534 (2010)CrossRefGoogle Scholar
  35. 35.
    Seger, C.A.: The visual corticostriatal loop through the tail of the caudate: circuitry and function. Front. Syst. Neurosci. 7, 104 (2013)CrossRefGoogle Scholar
  36. 36.
    Palmer, S.J., Li, J., Wang, Z.J., McKeown, M.J.: Joint amplitude and connectivity compensatory mechanisms in parkinson’s disease. Neuroscience 166, 1110–1118 (2010)CrossRefGoogle Scholar
  37. 37.
    Wen, X., Yao, L., Fan, T., Wu, X., Liu, J.: The spatial pattern of basal ganglia network: a resting state fMRI study. In: Proceedings of Complex Medical Engineering, pp. 43–46 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Burak Yoldemir
    • 1
  • Bernard Ng
    • 2
    • 3
  • Rafeef Abugharbieh
    • 1
  1. 1.Biomedical Signal and Image Computing LabThe University of British ColumbiaVancouverCanada
  2. 2.Parietal TeamINRIA SaclaySaclayFrance
  3. 3.Functional Imaging in Neuropsychiatric Disorders LabStanford UniversityStanfordUSA

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