Continuous and Atlas-free Analysis of Brain Structural Connectivity

TL;DR

This paper introduces an atlas-free, functional data approach to analyze brain structural connectivity, avoiding arbitrary parcellation and capturing detailed connectivity patterns with improved accuracy.

Contribution

It proposes a novel smooth random function model for brain connectivity, along with a data-driven reduced-rank algorithm, advancing beyond traditional atlas-based methods.

Findings

Outperforms state-of-the-art atlas-based methods in connectivity analysis tasks

Detects localized brain regions and connectivity differences between groups

Provides a flexible, computationally efficient framework for high-dimensional brain data

Abstract

Brain structural networks are often represented as discrete adjacency matrices with elements summarizing the connectivity between pairs of regions of interest (ROIs). These ROIs are typically determined a-priori using a brain atlas. The choice of atlas is often arbitrary and can lead to a loss of important connectivity information at the sub-ROI level. This work introduces an atlas-free framework that overcomes these issues by modeling brain connectivity using smooth random functions. In particular, we assume that the observed pattern of white matter fiber tract endpoints is driven by a latent random function defined over a product manifold domain. To facilitate statistical analysis of these high dimensional functional data objects, we develop a novel algorithm to construct a data-driven reduced-rank function space that offers a desirable trade-off between computational complexity and flexibility. Using real data from the Human Connectome Project, we show that our method outperforms state-of-the-art approaches that use the traditional atlas-based structural connectivity representation on a variety of connectivity analysis tasks. We further demonstrate how our method can be used to detect localized regions and connectivity patterns associated with group differences.