Evaluating methods for measuring background connectivity in slow event-related functional MRI designs

Abstract

Resting-state functional MRI (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patters are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses. Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task- evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task- related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses. We found that the organization of large-scale cortical networks and individuals idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.