Structural Connectome Atlas Construction in the Space of Riemannian
Metrics
- URL: http://arxiv.org/abs/2103.05730v1
- Date: Tue, 9 Mar 2021 21:46:02 GMT
- Title: Structural Connectome Atlas Construction in the Space of Riemannian
Metrics
- Authors: Kristen M. Campbell (1), Haocheng Dai (1), Zhe Su (2), Martin Bauer
(3), P. Thomas Fletcher (4), Sarang C. Joshi (1 and 5) ((1) Scientific
Computing and Imaging Institute, University of Utah, (2) Department of
Neurology, University of California Los Angeles, (3) Department of
Mathematics, Florida State University, (4) Electrical & Computer Engineering,
University of Virginia, (5) Department of Bioengineering, University of Utah)
- Abstract summary: We analyze connectomes as points in an infinite-dimensional manifold using the Ebin metric.
We demonstrate connectome registration and atlas formation using connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The structural connectome is often represented by fiber bundles generated
from various types of tractography. We propose a method of analyzing
connectomes by representing them as a Riemannian metric, thereby viewing them
as points in an infinite-dimensional manifold. After equipping this space with
a natural metric structure, the Ebin metric, we apply object-oriented
statistical analysis to define an atlas as the Fr\'echet mean of a population
of Riemannian metrics. We demonstrate connectome registration and atlas
formation using connectomes derived from diffusion tensors estimated from a
subset of subjects from the Human Connectome Project.
Related papers
- Geodesic Calculus on Latent Spaces [4.023417156982924]
We develop tools for a discrete Riemannian calculus approximating classical geometric operators.<n>We learn an approximate projection onto the latent manifold by minimizing a denoising objective.<n>We evaluate our approach on various autoencoders trained on synthetic and real data.
arXiv Detail & Related papers (2025-10-10T15:25:03Z) - An entropy formula for the Deep Linear Network [0.0]
Main tools are the use of group actions to analyze overparametrization.<n>The foliation of the balanced manifold in the parameter space by group orbits is used to define and compute a Boltzmann entropy.<n>The main technical step is an explicit construction of an orthonormal basis for the tangent space of the balanced manifold.
arXiv Detail & Related papers (2025-09-11T01:40:46Z) - Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models [63.331590876872944]
We propose a method for deriving Riemannian metrics directly from pretrained Energy-Based Models.<n>These metrics define spatially varying distances, enabling the computation of geodesics.<n>We show that EBM-derived metrics consistently outperform established baselines.
arXiv Detail & Related papers (2025-05-23T12:18:08Z) - Sigma Flows for Image and Data Labeling and Learning Structured Prediction [2.4699742392289]
This paper introduces the sigma flow model for the prediction of structured labelings of data observed on Riemannian manifold.
The approach combines the Laplace-Beltrami framework for image denoising and enhancement, introduced by Sochen, Kimmel and Malladi about 25 years ago, and the assignment flow approach introduced and studied by the authors.
arXiv Detail & Related papers (2024-08-28T17:04:56Z) - Product Geometries on Cholesky Manifolds with Applications to SPD Manifolds [65.04845593770727]
We present two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold.
Our metrics are easy to use, computationally efficient, and numerically stable.
arXiv Detail & Related papers (2024-07-02T18:46:13Z) - Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure.
We introduce a new class of manifold, named soft manifold, that can solve this situation.
Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
arXiv Detail & Related papers (2023-11-29T12:48:33Z) - The Fisher-Rao geometry of CES distributions [50.50897590847961]
The Fisher-Rao information geometry allows for leveraging tools from differential geometry.
We will present some practical uses of these geometric tools in the framework of elliptical distributions.
arXiv Detail & Related papers (2023-10-02T09:23:32Z) - A singular Riemannian geometry approach to Deep Neural Networks I.
Theoretical foundations [77.86290991564829]
Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis.
We study a particular sequence of maps between manifold, with the last manifold of the sequence equipped with a Riemannian metric.
We investigate the theoretical properties of the maps of such sequence, eventually we focus on the case of maps between implementing neural networks of practical interest.
arXiv Detail & Related papers (2021-12-17T11:43:30Z) - Integrated Construction of Multimodal Atlases with Structural
Connectomes in the Space of Riemannian Metrics [9.067368638784355]
We show that a structural connectome can be represented as a point on an infinite-dimensional manifold.
We then apply this framework to apply object-oriented statistical analysis to define an atlas.
We build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects.
arXiv Detail & Related papers (2021-09-20T19:39:10Z) - A Unifying and Canonical Description of Measure-Preserving Diffusions [60.59592461429012]
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework.
We develop a geometric theory that improves and generalises this construction to any manifold.
arXiv Detail & Related papers (2021-05-06T17:36:55Z) - Operator-valued formulas for Riemannian Gradient and Hessian and
families of tractable metrics [0.0]
We provide a formula for a quotient of a manifold embedded in an inner product space with a non-constant metric function.
We extend the list of potential metrics that could be used in manifold optimization and machine learning.
arXiv Detail & Related papers (2020-09-21T20:15:57Z) - Geometry of Similarity Comparisons [51.552779977889045]
We show that the ordinal capacity of a space form is related to its dimension and the sign of its curvature.
More importantly, we show that the statistical behavior of the ordinal spread random variables defined on a similarity graph can be used to identify its underlying space form.
arXiv Detail & Related papers (2020-06-17T13:37:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.