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Topographic Regularity for Tract Filtering in Brain Connectivity

  • Junyan Wang
  • Dogu Baran Aydogan
  • Rohit Varma
  • Arthur W. Toga
  • Yonggang ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10265)

Abstract

The preservation of the spatial relationships among axonal pathways has long been studied and known to be critical for many functions of the brain. Being a fundamental property of the brain connections, there is an intuitive understanding of topographic regularity in neuroscience but yet to be systematically explored in connectome imaging research. In this work, we propose a general mathematical model for topographic regularity of fiber bundles that is consistent with its neuroanatomical understanding. Our model is based on a novel group spectral graph analysis (GSGA) framework motivated by spectral graph theory and tensor decomposition. GSGA provides a common set of eigenvectors for the graphs formed by topographic proximity measures whose preservation along individual tracts in return is modeled as topographic regularity. To demonstrate the application of this novel measure of topographic regularity, we apply it to filter fiber tracts from connectome imaging. Using large-scale data from the Human Connectome Project (HCP), we show that our novel algorithm can achieve better performance than existing methods on the filtering of both individual bundles and whole brain tractograms.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Junyan Wang
    • 1
  • Dogu Baran Aydogan
    • 1
  • Rohit Varma
    • 2
  • Arthur W. Toga
    • 1
  • Yonggang Shi
    • 1
    Email author
  1. 1.Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of University of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Ophthalmology, USC Roski Eye InstituteKeck School of Medicine of University of Southern CaliforniaLos AngelesUSA

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