Analyzing Brain Structural Connectivity as Continuous Random Functions

TL;DR

This paper introduces a novel continuous random function framework for analyzing brain structural connectivity, providing a more flexible and detailed characterization of population variability than traditional methods.

Contribution

The authors develop an efficient algorithm to model latent continuous connectivity functions, outperforming existing atlas-based approaches in connectivity analysis.

Findings

Outperforms state-of-the-art atlas-based methods

Identifies localized cortical regions with significant group differences

Provides a flexible continuous modeling framework

Abstract

This work considers a continuous framework to characterize the population-level variability of structural connectivity. Our framework assumes the observed white matter fiber tract endpoints are driven by a latent random function defined over a product manifold domain. To overcome the computational challenges of analyzing such complex latent functions, we develop an efficient algorithm to construct a data-driven reduced-rank function space to represent the latent continuous connectivity. Using real data from the Human Connectome Project, we show that our method outperforms state-of-the-art approaches applied to the traditional atlas-based structural connectivity matrices on connectivity analysis tasks of interest. We also demonstrate how our method can be used to identify localized regions and connectivity patterns on the cortical surface associated with significant group differences. Code will be made available at https://github.com/sbci-brain.