DeepAI AI Chat
Log In Sign Up

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

12/09/2020
by   Yanxin Jin, et al.
0

Graphical models have been used extensively for modeling brain connectivity networks. However, unmeasured confounders and correlations among measurements are often overlooked during model fitting, which may lead to spurious scientific discoveries. Motivated by functional magnetic resonance imaging (fMRI) studies, we propose a novel method for constructing brain connectivity networks with correlated replicates and latent effects. In a typical fMRI study, each participant is scanned and fMRI measurements are collected across a period of time. In many cases, subjects may have different states of mind that cannot be measured during the brain scan: for instance, some subjects may be awake during the first half of the brain scan, and may fall asleep during the second half of the brain scan. To model the correlation among replicates and latent effects induced by the different states of mind, we assume that the correlated replicates within each independent subject follow a one-lag vector autoregressive model, and that the latent effects induced by the unmeasured confounders are piecewise constant. The proposed method results in a convex optimization problem which we solve using a block coordinate descent algorithm. Theoretical guarantees are established for parameter estimation. We demonstrate via extensive numerical studies that our method is able to estimate latent variable graphical models with correlated replicates more accurately than existing methods.

READ FULL TEXT
05/11/2020

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

With the wide adoption of functional magnetic resonance imaging (fMRI) b...
04/10/2017

Integrating Additional Knowledge Into Estimation of Graphical Models

In applications of graphical models, we typically have more information ...
09/30/2019

A random covariance model for bi-level graphical modeling with application to resting-state fMRI data

This paper considers a novel problem, bi-level graphical modeling, in wh...
11/10/2017

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

In this work, we present an additive model for space-time data that spli...
10/16/2018

Joint Nonparametric Precision Matrix Estimation with Confounding

We consider the problem of precision matrix estimation where, due to ext...
05/24/2020

Fused graphical lasso for brain networks with symmetries

Neuroimaging is the growing area of neuroscience devoted to produce data...
06/24/2019

Inverse reinforcement learning conditioned on brain scan

We outline a way for an agent to learn the dispositions of a particular ...