Identifying Relationships in Functional and Structural Connectome Data Using a Hypergraph Learning Method

  • Brent C. Munsell
  • Guorong Wu
  • Yue Gao
  • Nicholas Desisto
  • Martin Styner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

The brain connectome provides an unprecedented degree of information about the organization of neuronal network architecture, both at a regional level, as well as regarding the entire brain network. Over the last several years the neuroimaging community has made tremendous advancements in the analysis of structural connectomes derived from white matter fiber tractography or functional connectomes derived from time-series blood oxygen level signals. However, computational techniques that combine structural and functional connectome data to discover complex relationships between fiber density and signal synchronization, including the relationship with health and disease, has not been consistently performed. To overcome this shortcoming, a novel connectome feature selection technique is proposed that uses hypergraphs to identify connectivity relationships when structural and functional connectome data is combined. Using publicly available connectome data from the UMCD database, experiments are provided that show SVM classifiers trained with structural and functional connectome features selected by our method are able to correctly identify autism subjects with 88 % accuracy. These results suggest our combined connectome feature selection approach may improve outcome forecasting in the context of autism.

References

  1. 1.
    Greicius, M.D., Supekar, K., Menon, V., Dougherty, R.F.: Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19(1), 72–78 (2009)CrossRefGoogle Scholar
  2. 2.
    Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59(3), 2142–2154 (2012)CrossRefGoogle Scholar
  3. 3.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
  4. 4.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  5. 5.
    Rudie, J., Brown, J., Beck-Pancer, D., Hernandez, L., Dennis, E., Thompson, P., Bookheimer, S., Dapretto, M.: Altered functional and structural brain network organization in autism. NeuroImage Clin. 2, 79–94 (2013)CrossRefGoogle Scholar
  6. 6.
    Saur, D., Schelter, B., Schnell, S., Kratochvil, D., Kpper, H., Kellmeyer, P., Kmmerer, D., Klppel, S., Glauche, V., Lange, R., Mader, W., Feess, D., Timmer, J., Weiller, C.: Combining functional and anatomical connectivity reveals brain networks for auditory language comprehension. NeuroImage 49(4), 3187–3197 (2010)CrossRefGoogle Scholar
  7. 7.
    Sporns, O.: The human connectome: origins and challenges. Neuroimage 80, 53–61 (2013)CrossRefGoogle Scholar
  8. 8.
    Teipel, S.J., Bokde, A.L., Meindl, T., Amaro, E., Soldner, J., Reiser, M.F., Herpertz, S.C., Moller, H.J., Hampel, H.: White matter microstructure underlying default mode network connectivity in the human brain. Neuroimage 49(3), 2021–2032 (2010)CrossRefGoogle Scholar
  9. 9.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58, 267–288 (1994)MathSciNetMATHGoogle Scholar
  10. 10.
    Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zhu, D., Li, K., Terry, D.P., Puente, A.N., Wang, L., Shen, D., Miller, L.S., Liu, T.: Connectome-scale assessments of structural and functional connectivity in MCI. Hum. Brain Mapp. 35(7), 2911–2923 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Brent C. Munsell
    • 1
  • Guorong Wu
    • 2
  • Yue Gao
    • 3
  • Nicholas Desisto
    • 1
  • Martin Styner
    • 4
  1. 1.Department of Computer ScienceCollege of CharlestonCharlestonUSA
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.School of SoftwareTsinghua UniversityBeijingChina
  4. 4.Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUSA

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