Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model

TL;DR

This paper introduces the Variance Design General Linear Model (VDGLM), a new framework for analyzing BOLD signal variance in fMRI data, revealing that working memory tasks decrease BOLD variance across the brain.

Contribution

The paper presents the VDGLM, a flexible model that simultaneously assesses mean and variance effects in fMRI data, enabling more comprehensive analysis of neural activity.

Findings

Working memory engagement reduces BOLD variance.

VDGLM can distinguish between mean and variance effects.

Framework applicable to various fMRI studies.

Abstract

Typical fMRI studies have focused on either the mean trend in the blood-oxygen-level-dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed. We introduce the Variance Design General Linear Model (VDGLM), a novel framework that facilitates the detection of variance effects. We designed the framework for general use in any fMRI study by modeling both mean and variance in BOLD activation as a function of experimental design. The flexibility of this approach allows the VDGLM to i) simultaneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrate the use of the VDGLM in a working memory application and show that engagement in a working memory task is associated with whole-brain decreases in BOLD variance.