A computational geometry approach for modeling neuronal fiber pathways

TL;DR

This paper introduces a computational geometry-based algorithm for modeling neuronal fiber pathways that simplifies white matter connectivity analysis and aids in distinguishing Alzheimer's disease from normal controls.

Contribution

The paper presents a novel, efficient geometric modeling method for neuronal fibers that improves tractography analysis and supports disease differentiation.

Findings

Effective modeling of neuronal fiber trajectories

Ability to distinguish Alzheimer's patients from controls

Open-source software implementation available

Abstract

We propose a novel and efficient algorithm to model high-level topological structures of neuronal fibers. Tractography constructs complex neuronal fibers in three dimensions that exhibit the geometry of white matter pathways in the brain. However, most tractography analysis methods are time consuming and intractable. We develop a computational geometry-based tractography representation that aims to simplify the connectivity of white matter fibers. Given the trajectories of neuronal fiber pathways, we model the evolution of trajectories that encodes geometrically significant events and calculate their point correspondence in the 3D brain space. Trajectory inter-distance is used as a parameter to control the granularity of the model that allows local or global representation of the tractogram. Using diffusion MRI data from Alzheimer's patient study, we extract tractography features from our model for distinguishing the Alzheimer's subject from the normal control. Software implementation of our algorithm is available on GitHub.