A Continuous Model of Cortical Connectivity

TL;DR

This paper introduces a continuous, probabilistic model for brain connectivity using Poisson processes, improving the assessment of cortical parcellations and demonstrating higher reliability over traditional methods.

Contribution

The authors develop a novel continuous connectome model based on Poisson point processes and efficient kernel density estimation, enhancing reliability and computational efficiency.

Findings

Higher test-retest reliability of connectomes

Effective assessment of cortical parcellations

Fast parameter estimation method

Abstract

We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here a product space of cortical white matter boundaries. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivty. We further present empirical results that suggest the discrete connectomes derived from our model have substantially higher test-retest reliability compared to standard methods.