Measures of Tractography Convergence

TL;DR

This paper uses information theory to analyze how quickly tractography methods converge in sampling brain pathways, providing guidelines for optimal sampling to improve brain connectivity studies.

Contribution

It introduces an information-theoretic measure to evaluate tractography convergence and empirically assesses streamline methods to inform sampling strategies.

Findings

Low information gain after moderate streamline sampling

Guidelines for optimal sampling in brain connectivity analysis

Empirical assessment of four tractography methods

Abstract

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.