Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics

TL;DR

This paper introduces a novel Riemannian geometric framework for constructing multimodal brain atlases by representing structural connectomes as metrics, enabling population-level analysis and integration of diverse neuroimaging data.

Contribution

It proposes representing connectomes as Riemannian metrics on an infinite-dimensional manifold and constructing atlases as Fréchet means within this space, integrating multimodal data.

Findings

Framework successfully applied to 2D connectome registration and atlas formation.

Constructed a 3D multimodal brain atlas combining T1 MRI and diffusion-based connectomes.

Demonstrated the geometric approach's potential for population-level neuroimaging analysis.

Abstract

The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitative insights into brain structure, there is controversy over whether they can provide quantitative information, especially at the population level. In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold. We equip this manifold with the Ebin metric, a natural metric structure for this space, to get a Riemannian manifold along with its associated geometric properties. We then use this Riemannian framework to apply object-oriented statistical analysis to define an atlas as the Fr\'echet mean of a population of Riemannian metrics. This formulation ties into the existing framework for diffeomorphic construction of image atlases, allowing us to construct a multimodal atlas by simultaneously integrating complementary white matter structure details from DWMRI and cortical details from T1-weighted MRI. We illustrate our framework with 2D data examples of connectome registration and atlas formation. Finally, we build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.