International Conference on Medical Image Computing and Computer-Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 604-611 | Cite as

Kernel-Based Analysis of Functional Brain Connectivity on Grassmann Manifold

  • Luca Dodero
  • Fabio Sambataro
  • Vittorio Murino
  • Diego Sona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

Functional Magnetic Resonance Imaging (fMRI) is widely adopted to measure brain activity, aiming at studying brain functions both in healthy and pathological subjects. Discrimination and identification of functional alterations in the connectivity, characterizing mental disorders, are getting increasing attention in neuroscience community.

We present a kernel-based method allowing to classify functional networks and characterizing those features that are significantly discriminative between two classes.

We used a manifold approach based on Grassmannian geometry and graph Laplacians, which permits to learn a set of sub-connectivities that can be used in combination with Support Vector Machine (SVM) to classify functional connectomes and for identifying neuroanatomically different connections.

We tested our approach on a real dataset of functional connectomes with subjects affected by Autism Spectrum Disorder (ASD), finding consistent results with the models of aberrant connections in ASD.

Keywords

Manifold Grassmann fMRI Classification Connectivity Autism 

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References

  1. 1.
    Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)Google Scholar
  2. 2.
    Satterthwaite, T.D., Wolf, D.H., Ruparel, K., Erus, G., Elliott, M.A., Eickhoff, S.B., Gennatas, E.D., Jackson, C., Prabhakaran, K., Alex, Smith, o.: Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage 83, 45–57 (2013)CrossRefGoogle Scholar
  3. 3.
    Craddock, R.C., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine 62(6), 1619–1628 (2009)CrossRefGoogle Scholar
  4. 4.
    Eavani, H., Satterthwaite, T.D., Filipovych, R., Gur, R.E., Gur, R.C., Davatzikos, C.: Identifying sparse connectivity patterns in the brain using resting-state fmri. NeuroImage 105, 286–299 (2015)CrossRefGoogle Scholar
  5. 5.
    Ng, B., Dressler, M.: Transport on riemannian manifold for functional connectivity-based classification. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2014. LNCS, vol. 8674, pp. 405–412. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Dodero, L., Quang, M.H., Biagio, M.S., Murino, V., Sona, D.: Kernel-based classification for brain connectivity graphs on the riemannian manifold of positive definite matrices. In: IEEE International Symposium of Biomedical Imaging ISBI 2015 (2015)Google Scholar
  7. 7.
    Calhoun, V.D., Liu, J., Adalı, T.: A review of group ica for fmri data and ica for joint inference of imaging, genetic, and erp data. Neuroimage 45(1), 163 (2009)CrossRefGoogle Scholar
  8. 8.
    Fan, Y., Liu, Y., Wu, H., Hao, Y., Liu, H., Liu, Z., Jiang, T.: Discriminant analysis of functional connectivity patterns on grassmann manifold. Neuroimage 56(4), 2058–2067 (2011)CrossRefGoogle Scholar
  9. 9.
    Ghanbari, Y., Bloy, L., Shankar, V., Edgar, J.C., Roberts, T.L., Schultz, R., Verma, R.: Functionally driven brain networks using multi-layer graph clustering. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2014. LNCS, vol. 8675, pp. 113–120. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    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.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2014. LNCS, vol. 8674, pp. 708–715. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Hamm, J., Lee, D.D.: Grassmann discriminant analysis: a unifying view on subspace-based learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 376–383. ACM (2008)Google Scholar
  12. 12.
    Brown, J.A., Rudie, J.D., Bandrowski, A., Van Horn, J.D., Bookheimer, S.Y.: The ucla multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Frontiers in Neuroinformatics 6 (2012)Google Scholar
  13. 13.
    Power, J.D., Schlaggar, B.L., Petersen, S.E.: Studying brain organization via spontaneous fmri signal. Neuron 84(4), 681–696 (2014)CrossRefGoogle Scholar
  14. 14.
    Rudie, J.D., Brown, J.A., Beck-Pancer, D., Hernandez, L.M., Dennis, E.L., Thompson, P.M., Bookheimer, S.Y., Dapretto, M.: Altered functional and structural brain network organization in autism. NeuroImage: Clinical (2012)Google Scholar
  15. 15.
    Xia, M., Wang, J., He, Y.: Brainnet viewer: a network visualization tool for human brain connectomics. PloS One 8(7), e68910 (2013)Google Scholar
  16. 16.
    Nebel, M.B., Eloyan, A., Barber, A.D., Mostofsky, S.H.: Precentral gyrus functional connectivity signatures of autism. Frontiers in Systems Neuroscience 8 (2014)Google Scholar
  17. 17.
    Di Martino, A., Kelly, C., Grzadzinski, R., Zuo, X.N., Mennes, M., Mairena, M.A., Lord, C., Castellanos, F.X., Milham, M.P.: Aberrant striatal functional connectivity in children with autism. Biological Psychiatry 69(9), 847–856 (2011)CrossRefGoogle Scholar
  18. 18.
    Noonan, S.K., Haist, F., Müller, R.A.: Aberrant functional connectivity in autism: evidence from low-frequency bold signal fluctuations. Brain Research 1262, 48–63 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Luca Dodero
    • 1
  • Fabio Sambataro
    • 2
  • Vittorio Murino
    • 1
    • 4
  • Diego Sona
    • 1
    • 3
  1. 1.Pattern Analysis and Computer Vision, PAVISIstituto Italiano di TecnologiaGenovaItaly
  2. 2.pRED, NORD DTA, Hoffmann-La Roche, Ltd BaselBaselSwitzerland
  3. 3.NeuroInformatics Laboratory, Fondazione Bruno KesslerTrentoItaly
  4. 4.Department of Computer ScienceUniversity of VeronaVeronaItaly

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