Abstract
When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-resting-state network (RSN) connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed.
Highlights
In functional brain connectivity analysis we implicitly make the assumption that a temporal correlation between two regions is indicative of an interaction between them
An Illustrative Example of the Magnitude- and Variance-Based Approach to Study Dynamic Functional Connectivity To illustrate the two strategies outlined in the introduction, we start by providing a simple schematic example
The connectivity times-series plotted in Figure 1A is meant to be viewed as an illustrative example for putative outcomes of the temporal evolution of changes in brain connectivity between two different pairs of brain regions during the length of a typical fMRI session
Summary
In functional brain connectivity analysis we implicitly make the assumption that a temporal correlation between two regions (or alternatively, nodes) is indicative of an interaction between them. Mean–variance relationship in dynamic rs-fMRI straightforward: a higher magnitude of correlation implies a larger degree of interaction between separate brain regions during the time span of measurement. This view of assessing functional brain connectivity has proven fruitful to identify functional networks during both resting-state and task conditions (Damoiseaux et al, 2006; Fox and Raichle, 2007). At a macroscopic level, studies of functional connectivity together with previous work on the brain’s anatomical circuitry form the foundational stones for the notion that the flow of information in the brain is both segregated and integrated (Sporns, 2011; Bullmore and Sporns, 2012)
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