Journal Article

Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs

Frank, Lea E., Zeithamova, Dagmar

2023

Overview

Introduction Resting‐state functional magnetic resonance imaging (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 patterns are thought to persist across task states, suggesting task‐based fMRI can be similarly leveraged for individual differences analyses. Method 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. Result We found that the organization of large‐scale cortical networks and individual's 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. Conclusion Together, our results highlight new avenues for the analysis of task‐based fMRI datasets and the utility of each background connectivity method. In this manuscript, we measured the similarity of resting‐state connectivity profiles to those obtained during slow event‐related functional magnetic resonance imaging (fMRI). Importantly, background connectivity relies on the removal of task‐evoked signals to isolate intrinsic functional interactions. Here, we compared two common methods for removing task‐evoked activity: (1) applying a low‐pass filter to the fMRI time‐series signal, or (2) extracting the residuals from a general linear model of task‐evoked responses.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: background connectivity / Brain - diagnostic imaging / Brain - physiology / Brain Mapping / Cognition / connectivity fingerprint / Connectome - methods / Humans / individual differences / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Medical imaging / Method / Nerve Net - diagnostic imaging / Nerve Net - physiology / Neuroimaging / Rest - physiology / resting‐state functional connectivity / task‐based functional connectivity