Advertisement

Uncovering Dynamic Functional Connectivity of Parkinson’s Disease Using Topological Features and Sparse Group Lasso

  • Kin Ming Puk
  • Wei Xiang
  • Shouyi Wang
  • Cao (Danica) Xiao
  • W. A. Chaovalitwongse
  • Tara Madhyastha
  • Thomas Grabowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

Neuro-degenerative diseases such as Parkinson’s Disease (PD) are clinically found to cause alternations and failures in brain connectivity. In this work, a new classification framework using dynamic functional connectivity and topological features is proposed, and it is shown that such framework can give better insights over discriminative difference of the disease itself. After utilizing sparse group lasso with anatomically labeled resting-state fMRI signal, both discriminating brain regions and voxels within can be identified easily. To give an overview of the effectiveness of such framework, the classification performance with the network features extracted on dynamic functional network is quantitatively evaluated. Experimental results show that either single feature of clustering coefficient or combined feature group of characteristic path length, diameter, eccentricity and radius perform well in classifying PD, and as a result the identified feature can lead to better interpretation for clinical purposes.

Keywords

Parkinson’s disease Functional Magnetic Resonance Imaging (fMRI) Dynamic functional connectivity Sparse group lasso Classification 

References

  1. 1.
    Aftabuddin, M., Kundu, S.: AMINONET-a tool to construct and visualize amino acid networks, and to calculate topological parameters. J. Appl. Crystallogr. 43(2), 367–369 (2010)CrossRefGoogle Scholar
  2. 2.
    Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., Zhou, C.: Synchronization in complex networks. Phys. Rep. 469(3), 93–153 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Börner, K., Sanyal, S., Vespignani, A.: Network science. Ann. Rev. Inf. Sci. Technol. 41(1), 537–607 (2007)CrossRefGoogle Scholar
  4. 4.
    Byun, H.Y., Lu, J.J., Mayberg, H.S., Günay, C.: Classification of resting state fMRI datasets using dynamic network clusters. In: Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)Google Scholar
  5. 5.
    Chai, B., Walther, D., Beck, D., Fei-Fei, L.: Exploring functional connectivities of the human brain using multivariate information analysis. In: Advances in Neural Information Processing Systems, pp. 270–278 (2009)Google Scholar
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  7. 7.
    Estrada, E.: The Structure of Complex Networks: Theory and Applications. Oxford University Press, New York (2012)zbMATHGoogle Scholar
  8. 8.
    Matthew Hutchison, R., et al.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013)CrossRefGoogle Scholar
  9. 9.
    Ioannides, A.A.: Dynamic functional connectivity. Curr. Opin. Neurobiol. 17(2), 161–170 (2007)CrossRefGoogle Scholar
  10. 10.
    Lancaster, J.L., et al.: Automated talairach atlas labels for functional brain mapping. Hum. Brain Mapp. 10(3), 120–131 (2000)CrossRefGoogle Scholar
  11. 11.
    Lancaster, J.L., et al.: Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum. Brain Mapp. 5(4), 238 (1997)CrossRefGoogle Scholar
  12. 12.
    Liu, J., Ji, S., Ye, J., et al.: SLEP: sparse learning with efficient projections. Arizona State Univ. 6, 491 (2009)Google Scholar
  13. 13.
    Loewe, K., Grueschow, M., Stoppel, C.M., Kruse, R., Borgelt, C.: Fast construction of voxel-level functional connectivity graphs. BMC Neurosci. 15(1), 1 (2014)CrossRefGoogle Scholar
  14. 14.
    Duncan Luce, R., Perry, A.D.: A method of matrix analysis of group structure. Psychometrika 14(2), 95–116 (1949)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Madhyastha, T.M., Askren, M.K., Boord, P., Grabowski, T.J.: Dynamic connectivity at rest predicts attention task performance. Brain Connectivity 5(1), 45–59 (2015)CrossRefGoogle Scholar
  16. 16.
    Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10(9), 424–430 (2006)CrossRefGoogle Scholar
  17. 17.
    Onnela, J.-P., Saramäki, J., Kertész, J., Kaski, K.: Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E 71(6), 065103 (2005)CrossRefGoogle Scholar
  18. 18.
    Opsahl, T.: Triadic closure in two-mode networks: redefining the global and local clustering coefficients. Soc. Netw. 35(2), 159–167 (2013)CrossRefGoogle Scholar
  19. 19.
    Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31(2), 155–163 (2009)CrossRefGoogle Scholar
  20. 20.
    Papo, D., Buldú, J.M., Boccaletti, S., Bullmore, E.T.: Complex network theory and the brain. Phil. Trans. R. Soc. B 369(1653), 20130520 (2014)CrossRefGoogle Scholar
  21. 21.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Patt. Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  22. 22.
    Power, J.D., et al.: Functional network organization of the human brain. Neuron 72(4), 665–678 (2011)CrossRefGoogle Scholar
  23. 23.
    Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P., Van De Ville, D.: Decoding brain states from fmri connectivity graphs. Neuroimage 56(2), 616–626 (2011)CrossRefGoogle Scholar
  24. 24.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  25. 25.
    Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)CrossRefGoogle Scholar
  26. 26.
    Telesford, Q.K., Joyce, K.E., Hayasaka, S., Burdette, J.H., Laurienti, P.J.: The ubiquity of small-world networks. Brain Connectivity 1(5), 367–375 (2011)CrossRefGoogle Scholar
  27. 27.
    Van Wijk, B.C.M., Stam, C.J., Daffertshofer, A.: Comparing brain networks of different size and connectivity density using graph theory. PloS ONE 5(10), e13701 (2010)CrossRefGoogle Scholar
  28. 28.
    Varoquaux, G., Gramfort, A., Poline, J.-B., Thirion, B.: Brain covariance selection: better individual functional connectivity models using population prior. In: Advances in Neural Information Processing Systems, pp. 2334–2342 (2010)Google Scholar
  29. 29.
    Wang, Z., Alahmadi, A., Zhu, D., Li, T.: Brain functional connectivity analysis using mutual information. In: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 542–546. IEEE (2015)Google Scholar
  30. 30.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of śmall-worldńetworks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kin Ming Puk
    • 1
  • Wei Xiang
    • 2
  • Shouyi Wang
    • 1
  • Cao (Danica) Xiao
    • 3
  • W. A. Chaovalitwongse
    • 4
  • Tara Madhyastha
    • 5
  • Thomas Grabowski
    • 6
  1. 1.Department of Industrial, Manufacturing and Systems EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  3. 3.AI for Healthcare, IBM ResearchYorktown HeightsUSA
  4. 4.Industrial EngineeringUniversity of Arkansas FayettevilleUSA
  5. 5.Integrated Brain Imaging CenterUniversity of WashingtonSeattleUSA
  6. 6.Departments of Radiology and NeurologyUniversity of WashingtonSeattleUSA

Personalised recommendations