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Modeling topographic regularity in structural brain connectivity with application to tractogram filtering.

Junyan Wang1, Dogu Baran Aydogan1, Rohit Varma2

  • 1Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.

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Summary
This summary is machine-generated.

We developed a novel mathematical model for brain white matter fiber tracts, enhancing topographic regularity analysis. Our method effectively filters outliers in connectome imaging data, improving tract reconstruction accuracy.

Keywords:
Diffusion MRISpectral graph theoryTopographic regularityTractography

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Graph Theory

Background:

  • Topographic regularity is a key principle in brain connectivity, yet lacks robust mathematical characterization for in vivo imaging.
  • Existing methods for analyzing white matter tracts struggle with accurate outlier removal and topographic property assessment.

Purpose of the Study:

  • To introduce a general mathematical model for topographic regularity in white matter fiber bundles.
  • To develop and validate a novel tract filtering algorithm for improved connectome analysis.

Main Methods:

  • Proposed a group spectral graph analysis (GSGA) framework integrating spectral graph theory and tensor decomposition.
  • Introduced the group graph spectral distance (G^2SD) metric to quantify topographic regularity of fiber tracts.
  • Developed a tract filtering algorithm based on the GSGA framework to remove outliers from tractograms.

Main Results:

  • The novel tract filtering algorithm demonstrated superior performance compared to existing methods on simulated and real Human Connectome Project (HCP) data.
  • Quantitative analysis on 215 HCP subjects showed significant improvement in the retinotopy of reconstructed optic radiation bundles.
  • The developed software is publicly available on NITRC for broader research application.

Conclusions:

  • The proposed GSGA framework and G^2SD metric provide a robust mathematical foundation for analyzing topographic regularity in white matter tracts.
  • The developed tract filtering algorithm effectively enhances the quality and accuracy of tractograms derived from various tractography techniques.
  • This work advances the analysis of in vivo connectome data, particularly for visual pathway reconstruction.