Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
This work addresses the problem of quantitative population-level analysis of brain structural connectomes for researchers in neuroimaging, though it appears incremental by extending existing diffeomorphic atlas frameworks to a Riemannian representation.
The authors tackled the challenge of enabling population-level statistical analysis of structural connectomes by representing them as Riemannian metrics on a manifold, allowing the construction of multimodal atlases that integrate white matter and cortical details. They demonstrated this framework with 2D examples and built a 3D atlas using data from the Human Connectome Project.
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 Fréchet 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.