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Found 10 papers, 6 papers with code
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
We applied the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition.
Interpretable AI for relating brain structural and functional connectomes
One of the central problems in neuroscience is understanding how brain structure relates to function.
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level.
Amplitude Mean of Functional Data on $\mathbb{S}^2$
Manifold-valued functional data analysis (FDA) recently becomes an active area of research motivated by the raising availability of trajectories or longitudinal data observed on non-linear manifolds.
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets
In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.
Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects
This paper studies change-points in human brain functional connectivity (FC) and seeks patterns that are common across multiple subjects under identical external stimulus.
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices
Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task.
Low-Rank Representation over the Manifold of Curves
However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.
Bayesian Clustering of Shapes of Curves
The elastic-inner product matrix obtained from the data is modeled using a Wishart distribution whose parameters are assigned carefully chosen prior distributions to allow for automatic inference on the number of clusters.
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories
Using a natural Riemannain metric on vector bundles of SPDMs, we compute geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle, with respect to the re-parameterization group.
