Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI

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

Abstract

Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9\(\%\) better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.

Notes

Acknowledgments

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. 319456. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).

References

  1. 1.
    Bassett, D.S., Bullmore, E., Verchinski, B.A., Mattay, V.S., Weinberger, D.R., Meyer-Lindenberg, A.: Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28(37), 9239–9248 (2008)CrossRefGoogle Scholar
  2. 2.
    Beckmann, C., Smith, S.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23(2), 137–152 (2004)CrossRefGoogle Scholar
  3. 3.
    Bellec, P., Rosa-Neto, P., Lyttelton, O.C., Benali, H., Evans, A.C.: Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51(3), 1126–1139 (2010)CrossRefGoogle Scholar
  4. 4.
    Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995)CrossRefGoogle Scholar
  5. 5.
    Blumensath, T., Jbabdi, S., Glasser, M.F., Van Essen, D.C., Ugurbil, K., Behrens, T.E., Smith, S.M.: Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. NeuroImage 76, 313–324 (2013)CrossRefGoogle Scholar
  6. 6.
    Craddock, R.C., James, G., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914–1928 (2012)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Golland, Y., Golland, P., Bentin, S., Malach, R.: Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems. Neuropsychologia 46(2), 540–553 (2008)CrossRefGoogle Scholar
  9. 9.
    Gordon, E.M., Laumann, T.O., Adeyemo, B., Huckins, J.F., Kelley, W.M., Petersen, S.E.: Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex (2014)Google Scholar
  10. 10.
    van den Heuvel, M., Mandl, R., Hulshoff Pol, H.: Normalized cut group clustering of resting-state fMRI data. PLoS ONE 3(4), e2001 (2008)CrossRefGoogle Scholar
  11. 11.
    Jenatton, R., Gramfort, A., Michel, V., Obozinski, G., Bach, F., Thirion, B.: Multi-scale mining of fMRI data with hierarchical structured sparsity. In: IEEE International Workshop on Pattern Recognition in NeuroImaging, pp. 69–72. IEEE Computer Society, Washington (2011)Google Scholar
  12. 12.
    Langs, G., Sweet, A., Lashkari, D., Tie, Y., Rigolo, L., Golby, A.J., Golland, P.: Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients. NeuroImage 103, 462–475 (2014)CrossRefGoogle Scholar
  13. 13.
    Lombaert, H., Sporring, J., Siddiqi, K.: Diffeomorphic spectral matching of cortical surfaces. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 376–389. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  14. 14.
    Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E.: Functional network organization of the human brain. Neuron 72(4), 665–678 (2011)CrossRefGoogle Scholar
  15. 15.
    de Reus, M.A., van den Heuvel, M.P.: The parcellation-based connectome: limitations and extensions. NeuroImage 80, 397–404 (2013)CrossRefGoogle Scholar
  16. 16.
    Salvador, R., Suckling, J., Coleman, M.R., Pickard, J.D., Menon, D., Bullmore, E.: Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb. Cortex 15(9), 1332–1342 (2005)CrossRefGoogle Scholar
  17. 17.
    Shen, X., Tokoglu, F., Papademetris, X., Constable, R.T.: Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage 82, 403–415 (2013)CrossRefGoogle Scholar
  18. 18.
    Smith, S.M., Vidaurre, D., Beckmann, C.F., Glasser, M.F., Jenkinson, M., Miller, K.L., Nichols, T.E., Robinson, E.C., Salimi-Khorshidi, G., Woolrich, M.W., Barch, D.M., Ugurbil, K., Van Essen, D.C.: Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17(12), 666–682 (2013)CrossRefGoogle Scholar
  19. 19.
    Sporns, O., Tononi, G., Ktter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)CrossRefGoogle Scholar
  20. 20.
    Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M.D.: Network analysis of intrinsic functional brain connectivity in alzheimer’s disease. PLoS Comput. Biol. 4(6), e1000100 (2008)CrossRefGoogle Scholar
  21. 21.
    Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.B.: Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fMRI datasets. Hum. Brain Mapp. 27(8), 678–693 (2006)CrossRefGoogle Scholar
  22. 22.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)CrossRefGoogle Scholar
  23. 23.
    Varoquaux, G., Gramfort, A., Pedregosa, F., Michel, V., Thirion, B.: Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 562–573. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  24. 24.
    Wig, G.S., Laumann, T.O., Cohen, A.L., Power, J.D., Nelson, S.M., Glasser, M.F., Miezin, F.M., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Parcellating an individual subject’s cortical and subcortical brain structures using snowball sampling of resting-state correlations. Cereb. Cortex 24(8), 2036–2054 (2013)CrossRefGoogle Scholar
  25. 25.
    Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonLondonUK

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