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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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TRT reliability of global topological properties for networks in different frequency bands.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 different frequency bands.
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pone-0032766-g009: TRT reliability of global topological properties for networks in different frequency bands.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 different frequency bands.

Mentions: The brain networks in both frequency bands showed overall low reliability (Fig. 9). The TRT reliability of global brain network metrics appeared to be greater in slow-4 (0.027–0.073 Hz) than in slow-5 (0.01–0.027 Hz) by visual inspection. Subsequent statistical comparisons revealed that the TRT reliability of global network metrics was modulated by the frequency band with higher reliability observed for networks in slow-4 for long-term scans (p = 0.004) but not for short-term scans (p = 0.65). Nodal degree in the slow-5 and slow-4 brain networks exhibited similar short-term reliability with fair reliability regions mostly located in association and limbic/paralimbic cortex regions such as the right medial superior frontal gyrus (SFGmed), right medial orbitofrontal cortex (ORBmed), left ITG and bilateral MCG (Fig. 10A). Regions with fair long-term nodal degree reliability were also located in association and limbic/paralimbic cortex regions. However, networks in the slow-4 band displayed more fair reliability regions than slow-5 band networks including the left FFG, left ITG, bilateral ORBmed, right PCL, right PCG and right HIP (Fig. 10B). Statistical comparisons revealed that the nodal degree of brain networks in slow-4 showed greater reliability than those in slow-5 for long-term scans (p = 0.0018) but not for short-term scans (p = 0.26) (Fig. 10C, D). Note that for short-term scans, even not significant, slow-4 demonstrates greater ICC values than slow-5 on visual inspection, indicating a trend of frequency-dependent differences in reliability for short-term scans. The strengthened differences in reliability between slow-4 and slow-5 for long-term scans could be a result of decreased inter-scan reliability in slow-5 band. We performed statistical tests between inter-scan and intra-scan ICCs in the two slow bands, and confirmed that reliability is significant lower for long-term scans than short-term scans in slow-5 band (p = 0.004) but not in slow-4 band (p = 0.18). These results suggest that brain networks constructed in the slow-4 band are more reliable than those in slow-5, which may reflect the fact that different frequency bands could be associated with different physiological functions (Buzsaki and Draguhn, 2004). Nevertheless, further work is needed to verify this finding and investigate the specific brain function in different frequency bands.


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 networks in different frequency bands.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 different frequency bands.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0032766-g009: TRT reliability of global topological properties for networks in different frequency bands.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 different frequency bands.
Mentions: The brain networks in both frequency bands showed overall low reliability (Fig. 9). The TRT reliability of global brain network metrics appeared to be greater in slow-4 (0.027–0.073 Hz) than in slow-5 (0.01–0.027 Hz) by visual inspection. Subsequent statistical comparisons revealed that the TRT reliability of global network metrics was modulated by the frequency band with higher reliability observed for networks in slow-4 for long-term scans (p = 0.004) but not for short-term scans (p = 0.65). Nodal degree in the slow-5 and slow-4 brain networks exhibited similar short-term reliability with fair reliability regions mostly located in association and limbic/paralimbic cortex regions such as the right medial superior frontal gyrus (SFGmed), right medial orbitofrontal cortex (ORBmed), left ITG and bilateral MCG (Fig. 10A). Regions with fair long-term nodal degree reliability were also located in association and limbic/paralimbic cortex regions. However, networks in the slow-4 band displayed more fair reliability regions than slow-5 band networks including the left FFG, left ITG, bilateral ORBmed, right PCL, right PCG and right HIP (Fig. 10B). Statistical comparisons revealed that the nodal degree of brain networks in slow-4 showed greater reliability than those in slow-5 for long-term scans (p = 0.0018) but not for short-term scans (p = 0.26) (Fig. 10C, D). Note that for short-term scans, even not significant, slow-4 demonstrates greater ICC values than slow-5 on visual inspection, indicating a trend of frequency-dependent differences in reliability for short-term scans. The strengthened differences in reliability between slow-4 and slow-5 for long-term scans could be a result of decreased inter-scan reliability in slow-5 band. We performed statistical tests between inter-scan and intra-scan ICCs in the two slow bands, and confirmed that reliability is significant lower for long-term scans than short-term scans in slow-5 band (p = 0.004) but not in slow-4 band (p = 0.18). These results suggest that brain networks constructed in the slow-4 band are more reliable than those in slow-5, which may reflect the fact that different frequency bands could be associated with different physiological functions (Buzsaki and Draguhn, 2004). Nevertheless, further work is needed to verify this finding and investigate the specific brain function in different frequency bands.

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