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Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data.

Wang JH, Zuo XN, Gohel S, Milham MP, Biswal BB, He Y - PLoS ONE (2011)

Bottom Line: We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks).Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks.For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance.

View Article: PubMed Central - PubMed

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

ABSTRACT
Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.

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Spatial similarity and TRT reliability patterns of S-AAL-based RSFC.Mean Pearson correlation matrices (a), consistency of overall patterns between mean matrices (b) and TRT reliability of individual connections as well as the relationship between short-term and long-term reliability (c) are illustrated. The mean correlation matrices exhibited high similarity from both visual inspection (a) and quantitative spatial correlation analyses (b). Further TRT reliability analyses revealed a portion of connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, short-term reliability was significantly (p<0.05) correlated with long-term reliability among connections (c). Functional connections linking inter-hemisphere homotopic regions, as highlighted by plus signs (+), showed high connectivity strength and many of them exhibited high reliability. TRT, test-retest; RSFC, resting-state functional connectivity; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas. Of note, the structural ROIs were listed as in Table S1.
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pone-0021976-g001: Spatial similarity and TRT reliability patterns of S-AAL-based RSFC.Mean Pearson correlation matrices (a), consistency of overall patterns between mean matrices (b) and TRT reliability of individual connections as well as the relationship between short-term and long-term reliability (c) are illustrated. The mean correlation matrices exhibited high similarity from both visual inspection (a) and quantitative spatial correlation analyses (b). Further TRT reliability analyses revealed a portion of connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, short-term reliability was significantly (p<0.05) correlated with long-term reliability among connections (c). Functional connections linking inter-hemisphere homotopic regions, as highlighted by plus signs (+), showed high connectivity strength and many of them exhibited high reliability. TRT, test-retest; RSFC, resting-state functional connectivity; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas. Of note, the structural ROIs were listed as in Table S1.

Mentions: The mean RSFC matrices across subjects were calculated (after Fisher's r-to-z transform) for each of the three TRT scans. Initial visual inspection suggested that mean RSFC matrices showed highly similar spatial patterns between different time points (Fig. 1a). Further quantitative spatial correlation analysis (Pearson correlation) confirmed the visual inspection, as revealed by high correlations in the mean correlation values between each pair among the three scans (Fig. 1b, scan1 vs. scan2: r = 0.961, p<10−300; scan1 vs. scan3: r = 0.962, p<10−300; scan2 vs. scan3: r = 0.966, p<10−300).


Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data.

Wang JH, Zuo XN, Gohel S, Milham MP, Biswal BB, He Y - PLoS ONE (2011)

Spatial similarity and TRT reliability patterns of S-AAL-based RSFC.Mean Pearson correlation matrices (a), consistency of overall patterns between mean matrices (b) and TRT reliability of individual connections as well as the relationship between short-term and long-term reliability (c) are illustrated. The mean correlation matrices exhibited high similarity from both visual inspection (a) and quantitative spatial correlation analyses (b). Further TRT reliability analyses revealed a portion of connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, short-term reliability was significantly (p<0.05) correlated with long-term reliability among connections (c). Functional connections linking inter-hemisphere homotopic regions, as highlighted by plus signs (+), showed high connectivity strength and many of them exhibited high reliability. TRT, test-retest; RSFC, resting-state functional connectivity; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas. Of note, the structural ROIs were listed as in Table S1.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g001: Spatial similarity and TRT reliability patterns of S-AAL-based RSFC.Mean Pearson correlation matrices (a), consistency of overall patterns between mean matrices (b) and TRT reliability of individual connections as well as the relationship between short-term and long-term reliability (c) are illustrated. The mean correlation matrices exhibited high similarity from both visual inspection (a) and quantitative spatial correlation analyses (b). Further TRT reliability analyses revealed a portion of connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, short-term reliability was significantly (p<0.05) correlated with long-term reliability among connections (c). Functional connections linking inter-hemisphere homotopic regions, as highlighted by plus signs (+), showed high connectivity strength and many of them exhibited high reliability. TRT, test-retest; RSFC, resting-state functional connectivity; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas. Of note, the structural ROIs were listed as in Table S1.
Mentions: The mean RSFC matrices across subjects were calculated (after Fisher's r-to-z transform) for each of the three TRT scans. Initial visual inspection suggested that mean RSFC matrices showed highly similar spatial patterns between different time points (Fig. 1a). Further quantitative spatial correlation analysis (Pearson correlation) confirmed the visual inspection, as revealed by high correlations in the mean correlation values between each pair among the three scans (Fig. 1b, scan1 vs. scan2: r = 0.961, p<10−300; scan1 vs. scan3: r = 0.962, p<10−300; scan2 vs. scan3: r = 0.966, p<10−300).

Bottom Line: We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks).Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks.For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance.

View Article: PubMed Central - PubMed

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

ABSTRACT
Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.

Show MeSH
Related in: MedlinePlus