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

Kristen M. CampbellUniversity of Utah, Salt Lake City, Haocheng DaiUniversity of Utah, Salt Lake City, Zhe Su University of California Los Angeles, Martin BauerFlorida State University, Tallahassee, P. Thomas FletcherUniversity of Virginia, Charlottesville, Sarang C. JoshiUniversity of Utah, Salt Lake City
IPMI 2021 special issue
Publication date: 2022/06/16
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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 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.

Keywords

Riemannian metrics · Multimodal atlas · Structural connectome · metric matching · white matter atlas · diffusion atlas · diffeomorphic metric registration

Bibtex @article{melba:2022:016:campbell, title = "Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics", authors = "Campbell, Kristen M. and Dai, Haocheng and Su, Zhe and Bauer, Martin and Fletcher, P. Thomas and Joshi, Sarang C.", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "IPMI 2021 special issue", year = "2022" }

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