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

TL;DR

This paper introduces a new convex optimization-based method for constructing brain connectivity networks from fMRI data, accounting for correlated replicates and unmeasured confounders, improving accuracy over existing methods.

Contribution

The paper proposes a novel approach that models correlated fMRI replicates and latent effects, with theoretical guarantees and improved estimation accuracy.

Findings

Method accurately estimates latent variable graphical models.

Outperforms existing methods in numerical studies.

Handles correlated replicates and unmeasured confounders effectively.

Abstract

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.