Measures of Tractography Convergence

  • Daniel C. Moyer
  • Paul ThompsonEmail author
  • Greg Ver Steeg
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


In the present work, we use information theory to understand the empirical convergence rate of tractography, a widely-used approach to reconstruct anatomical fiber pathways in the living brain. Based on diffusion MRI data, tractography is the starting point for many methods to study brain connectivity. Of the available methods to perform tractography, most reconstruct a finite set of streamlines, or 3D curves, representing probable connections between anatomical regions, yet relatively little is known about how the sampling of this set of streamlines affects downstream results, and how exhaustive the sampling should be. Here we provide a method to measure the information theoretic surprise (self-cross entropy) for tract sampling schema. We then empirically assess four streamline methods. We demonstrate that the relative information gain is very low after a moderate number of streamlines have been generated for each tested method. The results give rise to several guidelines for optimal sampling in brain connectivity analyses.


Tractography Cross entropy Simulation 



This work was supported by NIH Grant U54 EB020403 and DARPA grant W911NF-16-1-0575, as well as the NSF Graduate Research Fellowship Program Grant Number DGE-1418060.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daniel C. Moyer
    • 1
    • 2
  • Paul Thompson
    • 1
    Email author
  • Greg Ver Steeg
    • 2
  1. 1.Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos AngelesUSA
  2. 2.Information Sciences InstituteMarina del ReyUSA

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