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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 F-DOS-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 many connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, a significant (p<0.05) correlation was found in the ICC matrices between short-term and long-term scans (c). No inter-hemisphere homotopic functional connections were highlighted because of the absence of direct inter-hemisphere correspondence for these ROIs. TRT, test-retest; RSFC, resting-state functional connectivity; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010). Of note, the functional ROIs were listed as in Table S3.
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pone-0021976-g004: Spatial similarity and TRT reliability patterns of F-DOS-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 many connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, a significant (p<0.05) correlation was found in the ICC matrices between short-term and long-term scans (c). No inter-hemisphere homotopic functional connections were highlighted because of the absence of direct inter-hemisphere correspondence for these ROIs. TRT, test-retest; RSFC, resting-state functional connectivity; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010). Of note, the functional ROIs were listed as in Table S3.

Mentions: Relative to structural ROIs-based RSFC matrices (both S-AAL and S-HOA), the similarity in the spatial patterns across scans decreased for the mean RSFC matrices derived on the basis of 160 functional ROIs but still remained high (Scan1 vs. Scan2: r = 0.896, p<10−300; Scan1 vs. Scan3: r = 0.915, p<10−300; Scan2 vs. Scan3: r = 0.902, p<10−300) (Fig. 4a and b).


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 F-DOS-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 many connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, a significant (p<0.05) correlation was found in the ICC matrices between short-term and long-term scans (c). No inter-hemisphere homotopic functional connections were highlighted because of the absence of direct inter-hemisphere correspondence for these ROIs. TRT, test-retest; RSFC, resting-state functional connectivity; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010). Of note, the functional ROIs were listed as in Table S3.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g004: Spatial similarity and TRT reliability patterns of F-DOS-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 many connections exhibiting fair to excellent reliability (c, also see Fig. 2). Moreover, a significant (p<0.05) correlation was found in the ICC matrices between short-term and long-term scans (c). No inter-hemisphere homotopic functional connections were highlighted because of the absence of direct inter-hemisphere correspondence for these ROIs. TRT, test-retest; RSFC, resting-state functional connectivity; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010). Of note, the functional ROIs were listed as in Table S3.
Mentions: Relative to structural ROIs-based RSFC matrices (both S-AAL and S-HOA), the similarity in the spatial patterns across scans decreased for the mean RSFC matrices derived on the basis of 160 functional ROIs but still remained high (Scan1 vs. Scan2: r = 0.896, p<10−300; Scan1 vs. Scan3: r = 0.915, p<10−300; Scan2 vs. Scan3: r = 0.902, p<10−300) (Fig. 4a and b).

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