Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

11/10/2017
by   Kristjan Greenewald, et al.
0

In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.

READ FULL TEXT
research
10/04/2017

Bayesian Analysis of fMRI data with Spatially-Varying Autoregressive Orders

Statistical modeling of fMRI data is challenging as the data are both sp...
research
12/09/2020

Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data

Graphical models have been used extensively for modeling brain connectiv...
research
09/16/2020

Efficient Variational Bayesian Structure Learning of Dynamic Graphical Models

Estimating time-varying graphical models are of paramount importance in ...
research
03/19/2014

A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI

Brain networks has attracted the interests of many neuroscientists. From...
research
02/14/2022

Two Gaussian regularization methods for time-varying networks

We model time-varying network data as realizations from multivariate Gau...
research
01/15/2023

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

This thesis focuses on data that has complex spatio-temporal structure a...

Please sign up or login with your details

Forgot password? Click here to reset