Abstract
Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.
Highlights
Resting-state functional MRI (R-fMRI) has recently emerged as a powerful tool for exploring spontaneous brain function [1]
Robust small-world functional brain networks Graph theoretical analysis revealed that functional brain networks derived from R-fMRI data show prominent smallworld architecture across a wide sparsity range, Figure 2 showed the global network parameters within a sparsity range from 0.1 to 0.4, where the networks are sparse and their small-world attributes are estimable [4]
Our observations indicate that human functional brain networks have efficient small-world properties regardless of the correlation metric selected or the application of global signal regression
Summary
Resting-state functional MRI (R-fMRI) has recently emerged as a powerful tool for exploring spontaneous brain function [1]. Graph-based analysis has been used to investigate the topological changes of functional brain networks under pathological conditions [3,11,12,13]. Different functional connectivity metrics have been used to define network edges in fMRI data analysis, including Pearson’s correlation [8,10,17,19] and partial correlation [20,21,22]. The former measures the general dependence between variables, whereas the latter estimates the direct interdependence after ruling out third-party effects [23,24]. Most fMRI-based brain network studies have focused on only one of the two correlation metrics, and it remains unclear how the use of these different correlation metrics influences the topological properties of brain functional networks
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