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

Abstract

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.

Keywords: background connectivity; connectivity fingerprint; individual differences; resting-state functional connectivity; task-based functional connectivity.

FIGURE 1

Overview of preprocessing and analysis pipeline. Resting‐state and passive viewing data were subjected to the same preprocessing steps (see Section 2.2.4). ROI‐to‐ROI connectivity was measured from the preprocessed resting‐state data, while data from the passive viewing task were subjected to an additional step of preprocessing. Task‐related activity was removed from the passive viewing data using the low‐pass filter (LPF) and FIR residuals (FIR) approaches. Connectivity was calculated from the LPF data and from the FIR data, resulting in two sets of task‐based connections that were then compared to the “gold standard” rest connectivity.

FIGURE 2

Group‐averaged ROI‐to‐ROI correlation matrices for each method. The ROI‐to‐ROI correlation matrices are shown for the resting‐state data, task‐based LPF data, and the task‐based FIR data. The selected ROIs are from the Schaefer et al. (2018) parcellation scheme and are organized into seven cortical networks (Default, Frontoparietal Control, Limbic, Ventral Attention, Dorsal Attention, Somatomotor, and Visual; Yeo et al., 2011). The background connectivity matrices displayed are averaged across the four runs of passive viewing. For ease of interpretation, the matrices display the raw correlations prior to Fisher z transformation. For each subject, pairwise correlations were conducted between the three matrices to determine how well each background connectivity method reproduces the given individual's pattern of connections found during rest. The median and range of these within‐subject correlations are displayed.

FIGURE 3

Similarity in individual patterns of connectivity. For each subject, pairwise correlations were conducted to assess the similarity between the individual patterns of connections produced by each method.(A) The distribution of within‐subject similarity scores for each comparison. Each dot represents a participant. The box represents the interquartile range (Q1–Q3), the middle bar represents median (Q2), and the whiskers represent the minimum and maximum values. Points outside of the whisker (>1.5 × interquartile range) are defined as outliers. All pairwise comparisons are statistically significant at p < .001 (rest–LPF < rest–FIR < LPF–FIR). (B) A scatter plot showing a strong correlation between rest–LPF similarity scores and rest–FIR similarity scores (r = .93, p < .001). Those who showed low similarity between rest and task connectivity patterns did so irrespective of the background connectivity method (LPF vs. FIR residuals). Note that all subjects showed higher rest–FIR similarity than rest–LPF similarity (shown by all dots above the line x = y). (C, D) Scatter plots showing the correlation between average DVARS during rest and subject‐specific similarity scores between rest and task (C: rest × FIR similarity, D: rest × LPF similarity). The correlations were numerically positive but not statistically significant. Thus, low similarity in connectivity estimates between rest and task in some subjects was not clearly attributable to motion.

FIGURE 4

Reproducing resting‐state network structure. (A) Mean within‐network and between‐network connectivity estimates for each method are plotted with data points for individual subjects. Error bars denote ±1 SE. (B) The mean within‐network minus between‐network connectivity differences, plotted for each method. Data points for individual subjects are included. Error bars denote ±1 SE.

FIGURE 5

Stability of individual differences for each ROI‐to‐ROI connection. For each unique ROI‐to‐ROI connection, individual differences in connectivity estimates were correlated between rest and LPF, rest and FIR, and LPF and FIR datasets.

FIGURE 6

Stability of individual differences between the methods. For each ROI‐to‐ROI connection, individual differences in connectivity were calculated between two methods (rest × LPF, rest × FIR, and LPF × FIR). (A) The boxplot of similarity scores (Spearman rho) for all unique ROI‐to‐ROI connections. The box represents the interquartile range (Q1–Q3), the middle bar represents median (Q2), and the whiskers represent the minimum and maximum values. All pairwise comparisons are statistically significant at p < .001 (rest–LPF < rest–FIR < LPF–FIR). (B) Scatterplot showing a strong correlation between rest–LPF similarity and rest–FIR similarity (r = .85, p < .001). The connections that showed low similarity between rest and FIR also showed low similarity between rest and LPF, suggesting that some connections showed relatively low stability of individual differences between rest and task that was not driven by a particular method for removing task‐related activity. Dashed line indicates where x = y.

FIGURE 7

Stability of individual differences in network‐to‐network connections. The 100 × 100 ROI‐to‐ROI connections were collapsed into 7 × 7 network‐to‐network connections. For each network‐to‐network connection, individual differences in connectivity were correlated between two methods (rest × LPF, rest × FIR, LPF × FIR).