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Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

Liang X, Wang J, Yan C, Shu N, Xu K, Gong G, He Y - PLoS ONE (2012)

Bottom Line: Our results show significant differences in global network metrics associated with both correlation metrics and global signals.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

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.

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Functional hubs derived from networks in different frequency bands.Regions with degree>the mean+standard deviation are considered to be hubs. Node colors were coded according to their membership of classical cortex classifications: association cortex (red), limbic cortex (purple), paralimbic cortex (green), subcortical regions (light blue) and primary cortex regions (dark blue). (A) slow-5 (0.01–0.027 Hz). (B) slow-4 (0.027–0.073 Hz).
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pone-0032766-g008: Functional hubs derived from networks in different frequency bands.Regions with degree>the mean+standard deviation are considered to be hubs. Node colors were coded according to their membership of classical cortex classifications: association cortex (red), limbic cortex (purple), paralimbic cortex (green), subcortical regions (light blue) and primary cortex regions (dark blue). (A) slow-5 (0.01–0.027 Hz). (B) slow-4 (0.027–0.073 Hz).

Mentions: There was no significant difference in the clustering coefficients or local efficiency of brain graphs driven by the different frequency bands. However, brain networks constructed in the 0.027–0.073 Hz frequency band were found to be more globally efficient than those in 0.01–0.027 Hz as indicated by shorter Lp (p = 0.0007) and greater global efficiency (p = 0.001) (Fig. 7). Additionally, our results revealed that brain networks in slow-4 are less assortative (p = 0.002) but more hierarchical (p = 3.79e−9) than those in slow-5 (Fig. 7). These results indicate that the selection of different frequency bands can have a significant influence on the global topological properties of functional brain networks. Fig. 8 shows the hubs in WOGR-PEAR networks in the two different frequency bands. Similar spatial patterns of network hubs were observed in the two frequency intervals mainly in association cortex regions (e.g., ITG, MTG, fusiform gyrus [FFG] and SFGdor) and paralimbic/limbic cortex regions (e.g., MCG, TPOsup and TPOmid) with a high correlation coefficient (r = 0.85, p = 0.00).


Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

Liang X, Wang J, Yan C, Shu N, Xu K, Gong G, He Y - PLoS ONE (2012)

Functional hubs derived from networks in different frequency bands.Regions with degree>the mean+standard deviation are considered to be hubs. Node colors were coded according to their membership of classical cortex classifications: association cortex (red), limbic cortex (purple), paralimbic cortex (green), subcortical regions (light blue) and primary cortex regions (dark blue). (A) slow-5 (0.01–0.027 Hz). (B) slow-4 (0.027–0.073 Hz).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3295769&req=5

pone-0032766-g008: Functional hubs derived from networks in different frequency bands.Regions with degree>the mean+standard deviation are considered to be hubs. Node colors were coded according to their membership of classical cortex classifications: association cortex (red), limbic cortex (purple), paralimbic cortex (green), subcortical regions (light blue) and primary cortex regions (dark blue). (A) slow-5 (0.01–0.027 Hz). (B) slow-4 (0.027–0.073 Hz).
Mentions: There was no significant difference in the clustering coefficients or local efficiency of brain graphs driven by the different frequency bands. However, brain networks constructed in the 0.027–0.073 Hz frequency band were found to be more globally efficient than those in 0.01–0.027 Hz as indicated by shorter Lp (p = 0.0007) and greater global efficiency (p = 0.001) (Fig. 7). Additionally, our results revealed that brain networks in slow-4 are less assortative (p = 0.002) but more hierarchical (p = 3.79e−9) than those in slow-5 (Fig. 7). These results indicate that the selection of different frequency bands can have a significant influence on the global topological properties of functional brain networks. Fig. 8 shows the hubs in WOGR-PEAR networks in the two different frequency bands. Similar spatial patterns of network hubs were observed in the two frequency intervals mainly in association cortex regions (e.g., ITG, MTG, fusiform gyrus [FFG] and SFGdor) and paralimbic/limbic cortex regions (e.g., MCG, TPOsup and TPOmid) with a high correlation coefficient (r = 0.85, p = 0.00).

Bottom Line: Our results show significant differences in global network metrics associated with both correlation metrics and global signals.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

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.

Show MeSH