Structural Connectome Atlas Construction in the Space of Riemannian Metrics

Campbell, Kristen M.; Dai, Haocheng; Su, Zhe; Bauer, Martin; Fletcher, P. Thomas; Joshi, Sarang

doi:10.1007/978-3-030-78191-0_23

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…We have not yet studied the generalizability of the network, i.e., it is our aim to further develop the paradigm to train a single network that will efficiently output a metric structure directly from DWMRI data. With the ability to robustly and efficiently model the white matter of the brain as a Riemannian manifold, one can directly apply geometrical statistical techniques such as statistical atlas construction [3], principal geodesic analysis [7], and longitudinal regression [6] to precisely study the variability and differences in white matter architecture.…”

Section: Discussionmentioning

confidence: 99%

“…In order to strengthen the adherence of geodesics to the white matter pathways, Hao et al [12] developed an adaptive Riemannian metric by applying a conformal scalar field to the inverse of the diffusion tensor, which necessitates solving a Poisson equation on the Riemannian manifold. Campbell et al [3] further advanced the Riemannian formulation of structural connectomes by introducing methods for diffeomorphic image registration and atlas building using the Ebin metric on the space of Riemannian metrics. These geodesic approaches have several advantages over traditional tractography, including improved robustness to imaging noise and the ability to find tracts between two given anatomical endpoints in cases where traditional tractography fails.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning the Shape of the Brain Connectome

Dai¹,

Bauer²,

Fletcher³

et al. 2022

Preprint

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To statistically study the variability and differences between normal and abnormal brain connectomes, a mathematical model of the neural connections is required. In this paper, we represent the brain connectome as a Riemannian manifold, which allows us to model neural connections as geodesics. We show for the first time how one can leverage deep neural networks to estimate a Riemannian metric of the brain that can accommodate fiber crossings and is a natural modeling tool to infer the shape of the brain from DWMRI. Our method achieves excellent performance in geodesic-white-matter-pathway alignment and tackles the long-standing issue in previous methods: the inability to recover the crossing fibers with high fidelity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning the Shape of the Brain Connectome

Dai¹,

Bauer²,

Fletcher³

et al. 2022

Preprint

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“…Finally, we simultaneously estimate an integrated multimodal white matter pathway and T1 MRI-based image atlas for the first time. This article is an extended version of the Information Processing in Medical Imaging (IPMI) conference paper (Campbell et al, 2021), where we expand the results of the conference proceedings in several major directions. Most importantly, the joint white matter pathway and T1 MRI-based image atlas model is newly introduced and, in contrast to Campbell et al (2021)…”

Section: Contributions Of the Articlementioning

confidence: 99%

“…To meet these goals, we describe the metric matching framework presented by Campbell et al (2021) in more detail and then extend it by combining diffeomorphic metric matching with diffeomorphic image matching to enable the construction of both a multimodal white and gray matter atlas simultaneously for the first time. This formulation preserves geodesics transformed by diffeomorphisms, which meets our objective to use local diffusion data and maintain the integrity of long-range connectomics as inferred by tractography (Cheng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Campbell

Dai

et al. 2022

Melba

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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.

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“…Since the action of D(M) on the space of Riemannian metrics is non-transitive, this means that the regularization (1.8) is an outer distance corresponding to an H 1 Riemannian distance on D(M). Recently, the idea to act on Riemannian metrics is also explored for applications in computational anatomy of the brain [CDS+21].…”

Section: Introductionmentioning

confidence: 99%