Limits...
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.

Show MeSH

Related in: MedlinePlus

TRT reliability of global topological properties for Pearson's-correlation and partial-correlation-based networks with and without global signal regression.The reliability was estimated using areas under curves (AUC) of each metric. Statistical analysis revealed significant differences in (A) short-term and/or (B) long-term TRT reliability driven by correlation metrics and/or global signal regression.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3295769&req=5

pone-0032766-g005: TRT reliability of global topological properties for Pearson's-correlation and partial-correlation-based networks with and without global signal regression.The reliability was estimated using areas under curves (AUC) of each metric. Statistical analysis revealed significant differences in (A) short-term and/or (B) long-term TRT reliability driven by correlation metrics and/or global signal regression.

Mentions: Given that particular choices of processing options (i.e., correlation metrics and global signal regression) can make significant differences in network topological parameters, we next asked which analytical scheme would perform the best at modeling brain networks from the perspective of TRT reliability. Figure 5 shows the TRT reliability of 9 global network metrics under four different processing choices. Generally, most global network metrics exhibited poor to low reliability irrespective of the correlation metric or global signal regression. To test whether there is a difference in the TRT reliability associated with different processing options, we further performed a nonparametric paired-sample Wilcoxon signed rank test on the ICCs of global graph metrics. Our results showed that global graph metrics derived from Pearson's-correlation-based networks are more reliable than those derived from partial-correlation-based networks for both short-term scans (WOGR-PEAR>WOGR-PAR: p = 0.04) and long-term scans (WGR-PEAR>WGR-PAR: p = 0.003; WOGR-PEAR>WOGR-PAR: p = 0.016). Global signal regression produced less reliable results for short-term scans (WOGR-PEAR>WGR-PEAR: p = 0.019) but no significant differences for long-term scans (p = 0.46).


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)

TRT reliability of global topological properties for Pearson's-correlation and partial-correlation-based networks with and without global signal regression.The reliability was estimated using areas under curves (AUC) of each metric. Statistical analysis revealed significant differences in (A) short-term and/or (B) long-term TRT reliability driven by correlation metrics and/or global signal regression.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0032766-g005: TRT reliability of global topological properties for Pearson's-correlation and partial-correlation-based networks with and without global signal regression.The reliability was estimated using areas under curves (AUC) of each metric. Statistical analysis revealed significant differences in (A) short-term and/or (B) long-term TRT reliability driven by correlation metrics and/or global signal regression.
Mentions: Given that particular choices of processing options (i.e., correlation metrics and global signal regression) can make significant differences in network topological parameters, we next asked which analytical scheme would perform the best at modeling brain networks from the perspective of TRT reliability. Figure 5 shows the TRT reliability of 9 global network metrics under four different processing choices. Generally, most global network metrics exhibited poor to low reliability irrespective of the correlation metric or global signal regression. To test whether there is a difference in the TRT reliability associated with different processing options, we further performed a nonparametric paired-sample Wilcoxon signed rank test on the ICCs of global graph metrics. Our results showed that global graph metrics derived from Pearson's-correlation-based networks are more reliable than those derived from partial-correlation-based networks for both short-term scans (WOGR-PEAR>WOGR-PAR: p = 0.04) and long-term scans (WGR-PEAR>WGR-PAR: p = 0.003; WOGR-PEAR>WOGR-PAR: p = 0.016). Global signal regression produced less reliable results for short-term scans (WOGR-PEAR>WGR-PEAR: p = 0.019) but no significant differences for long-term scans (p = 0.46).

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
Related in: MedlinePlus