Skip to main content
SpringerLink
Log in

Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification

  • Conference paper
  • First Online:
Information Processing in Medical Imaging (IPMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9123))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Hinne, M., Ambrogioni, L., Janssen, R.J., Heskes, T., van Gerven, M.A.: Structurally-informed bayesian functional connectivity analysis. NeuroImage 86, 294–305 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Schuster, P., Sigmund, K.: Replicator dynamics. J. Theor. Biol. 100, 533–538 (1983)

    Article  MathSciNet  Google Scholar 

  14. Karlin, S.: Mathematical models, problems, and controversies of evolutionary theory. Bulletin Ame. Math. Soc. 10, 221–273 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  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. Meinshausen, N., Bühlmann, P.: Stability selection. J. Roy. Statist. Soc. Ser. B 72, 417–473 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Lohmann, G., Bohn, S.: Using replicator dynamics for analyzing fMRI data of human brain. IEEE Trans. Med. Imaging 21, 485–492 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Efron, B.: The jackknife, the bootstrap, and other resampling plans. Society of Industrial and Applied Mathematics CBMS-NSF Monographs (1982)

    Google Scholar 

  22. Lazar, M., Alexander, A.L.: Bootstrap white matter tractography (BOOT-TRAC). NeuroImage 24, 524–532 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. Van Den Heuvel, M., Mandl, R., Hulshoff Pol, H.: Normalized cut group clustering of resting-state fMRI data. PLoS ONE 3, e2001 (2008)

    Article  Google Scholar 

  29. Kuhn, H.W.: The hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)

    Article  Google Scholar 

  30. Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010)

    Article  Google Scholar 

  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. 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. Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  35. Seger, C.A.: The visual corticostriatal loop through the tail of the caudate: circuitry and function. Front. Syst. Neurosci. 7, 104 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

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.

Author information

Authors and Affiliations

  1. Biomedical Signal and Image Computing Lab, The University of British Columbia, Vancouver, BC, Canada

    Burak Yoldemir & Rafeef Abugharbieh

  2. Parietal Team, INRIA Saclay, Saclay, France

    Bernard Ng

  3. Functional Imaging in Neuropsychiatric Disorders Lab, Stanford University, Stanford, CA, USA

    Bernard Ng

Authors
  1. Burak Yoldemir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernard Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rafeef Abugharbieh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Burak Yoldemir .

Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, University College London, London, United Kingdom

    Sebastien Ourselin

  2. Centre for Medical Image Computing, University College London, London, United Kingdom

    Daniel C. Alexander

  3. Dept. of Radiology, Harvard Medical School Brigham and Women's Hospital, Boston, Massachusetts, USA

    Carl-Fredrik Westin

  4. Centre for Medical Image Computing, University College London, London, United Kingdom

    M. Jorge Cardoso

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Yoldemir, B., Ng, B., Abugharbieh, R. (2015). Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19992-4_61

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19991-7

  • Online ISBN: 978-3-319-19992-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation