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A Hybrid Point-Sets Clustering Approach to Identification of Resting State Functional Network

  • Can Feng
  • Tianming Liu
  • Liang Xiao
  • Zhihui Wei
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

Study of functional brain networks via resting state fMRI (rsfMRI) data has received increasing interest in the literature recently. Data-driven voxel-wise clustering approaches have been the mainstream approaches to automatic identification of resting state functional networks (rsFN). A basic assumption in these methods is that within each rsFN, voxels should be highly correlated with each other in terms of rsfMRI time courses. In this paper, we relax this assumption and assume that voxels in each rsFN do not necessarily have similar rsfMRI time series, but have either spatial vicinity or functional coherence. Consequently, we designed and implemented a hybrid point-sets clustering approach to identifying rsFNs based on rsfMRI data. Our experimental results show that this hybrid point-sets clustering method can identify meaningful and consistent rsFNs such as default mode network and motor network across subjects.

Keywords

functional network rsfMRI functional connectivity 

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References

  1. 1.
    Biswal, B., et al.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995)CrossRefGoogle Scholar
  2. 2.
    Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007)CrossRefGoogle Scholar
  3. 3.
    Luca, M.D., et al.: fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 29, 1359–1367 (2006)CrossRefGoogle Scholar
  4. 4.
    Raichle, M.E., et al.: A default mode of brain function. Proc. Natl. Acad. Sci. USA 98, 676–682 (2001)CrossRefGoogle Scholar
  5. 5.
    Damoiseaux, J.S., et al.: Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. USA 103, 13848–13853 (2006)CrossRefGoogle Scholar
  6. 6.
    Martijn, V.D.H., et al.: Normalized Cut Group Clustering of Resting-State fMRI Data. PLoS One 3(4) (2008)Google Scholar
  7. 7.
    Bradford, C.D., Reisa, A.: Sperling, Large-scale functional brain network abnormalities in Alzheimer’s disease: Insights from functional neuroimaging. Behavioural Neurology 21, 63–75 (2009)Google Scholar
  8. 8.
    Greicius, M.D., et al.: Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. USA 101, 4637–4642 (2004)Google Scholar
  9. 9.
    Liu, Y., et al.: Regional homogeneity, functional connectivity and imaging markers of Alzheimer’s disease: A review of resting-state fMRI studies. Neuropsychologia 46, 1648–1656 (2008)CrossRefGoogle Scholar
  10. 10.
    Zang, Y., et al.: Regional homogeneity approach to fMRI data analysis. NeuroImage 22(1), 394–400 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Liu, T., et al.: Brain Tissue Segmentation Based on DTI Data. NeuroImage 38(1), 114–123 (2007)CrossRefGoogle Scholar
  12. 12.
    Frey, B.J., Dueck, D.: Clustering by Passing Messages between Data Points. Science 315, 972–976 (2007)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Can Feng
    • 1
  • Tianming Liu
    • 2
  • Liang Xiao
    • 1
  • Zhihui Wei
    • 1
  1. 1.Department of Computer ScienceNanjing University of Science & TechnologyNan JingChina
  2. 2.Department of Computer ScienceThe University of GeorgiaAthensUSA

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