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A Submodular Approach to Create Individualized Parcellations of the Human Brain

  • Mehraveh SalehiEmail author
  • Amin Karbasi
  • Dustin Scheinost
  • R. Todd Constable
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Recent studies on functional neuroimaging (e.g. fMRI) attempt to model the brain as a network. A conventional functional connectivity approach for defining nodes in the network is grouping similar voxels together, a method known as functional parcellation. The majority of previous work on human brain parcellation employs a group-level analysis by collapsing data from the entire population. However, these methods ignore the large amount of inter-individual variability and uniqueness in connectivity. This is particularly relevant for patient studies or even developmental studies where a single functional atlas may not be appropriate for all individuals or conditions. To account for the individual differences, we developed an approach to individualized parcellation. The algorithm starts with an initial group-level parcellation and forms the individualized ones using a local exemplar-based submodular clustering method. The utility of individualized parcellations is further demonstrated through improvement in the accuracy of a predictive model that predicts IQ using functional connectome.

Keywords

Functional parcellation Subject variability Submodularity 

Notes

Acknowledgments

The authors wish to thank Xilin Shen and Emily S. Finn for providing the code for group-level parcellation and CPM method. Data were provided by the Human Connectome Project, WU-Minn Consortium (principal investigators, D. Van Essen and K. Ugurbil; 1U54MH091657). This work was supported by grants from NIH MH111424, and DARPA (D16AP00046).

References

  1. 1.
    Warren, W.B., Hansen, J.A., Brensinger, C.M., Richard, J., Gur, R.E., Gur, R.C.: Development of abbreviated nine-item forms of the Raven’s standard progressive matrices test. Assessment 19(3), 354–369 (2012)CrossRefGoogle Scholar
  2. 2.
    Craddock, R.C., James, G.A., 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
  3. 3.
    Feige, U.: A threshold of ln n for approximating set cover. J. ACM (JACM) 45(4), 634–652 (1998)CrossRefzbMATHGoogle Scholar
  4. 4.
    Finn, E.S., Shen, X., Scheinost, D., Rosenberg, M.D., Huang, J., Chun, M.M., Papademetris, X., Constable, R.T.: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neurosci. 18, 1664–1671 (2015)CrossRefGoogle Scholar
  5. 5.
    Laumann, T.O., Gordon, E.M., Adeyemo, B., Snyder, A.Z., Joo, S.J., Chen, M.-Y., Gilmore, A.W., McDermott, K.B., Nelson, S.M., Dosenbach, N.U.F., et al.: Functional system and areal organization of a highly sampled individual human brain. Neuron 87(3), 657–670 (2015)CrossRefGoogle Scholar
  6. 6.
    Meunier, D., Lambiotte, R., Fornito, A., Ersche, K.D., Bullmore, E.T.: Hierarchical modularity in human brain functional networks. Hierarchy Dyn. Neural Netw. 1(2) (2010)Google Scholar
  7. 7.
    Minoux, M.: Accelerated greedy algorithms for maximizing submodular set functions. In: Stoer, J. (ed.) Optimization Techniques. LNCIS, vol. 7, pp. 234–243. Springer, Berlin (1978). doi: 10.1007/BFb0006501 CrossRefGoogle Scholar
  8. 8.
    Mirzasoleiman, B., Karbasi, A., Sarkar, R., Krause, A.: Distributed submodular maximization. J. Mach. Learn. Res. 17(238), 1–44 (2016)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Nemhauser, G.L., Wolsey, L.A.: Best algorithms for approximating the maximum of a submodular set function. Math. Oper. Res. 3(3), 177–188 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    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
  11. 11.
    Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K., WU-Minn HCP Consortium, et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)Google Scholar
  12. 12.
    Wang, D., Buckner, R.L., Fox, M.D., Holt, D.J., Holmes, A.J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K., et al.: Parcellating cortical functional networks in individuals. Technical report. Nature Publishing Group (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mehraveh Salehi
    • 1
    Email author
  • Amin Karbasi
    • 1
  • Dustin Scheinost
    • 2
  • R. Todd Constable
    • 2
  1. 1.Department of Electrical EngineeringYale UniversityNew HavenUSA
  2. 2.Department of Radiology and Biomedical ImagingYale UniversityNew HavenUSA

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