An Empirical Study of Continuous Connectivity Degree Sequence Equivalents

TL;DR

This paper introduces a parcellation-free connectivity model using Poisson point processes to analyze continuous spatial graphs in brain connectivity, comparing different tractography methods.

Contribution

It presents a novel continuous connectivity model that estimates intensity functions for spatial graphs without relying on parcellation, expanding analysis capabilities.

Findings

Model produces subject-specific continuous connectivity maps.

Differences observed between tractography methods in local intensity functions.

Provides a new framework for analyzing spatial continuum graphs.

Abstract

In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes. This model produces for each subject a continuous bivariate intensity function that represents for every possible pair of points the relative rate at which we observe tracts terminating at those points. We fit this model to explore degree sequence equivalents for spatial continuum graphs, and to investigate the local differences between estimated intensity functions for two different tractography methods. This is a companion paper to Moyer et al. (2016), where the model was originally defined.