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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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TRT reliability of global network metrics as a function of noise in RSFC for S-AAL-based networks.Global network metrics were sensitive to disturbances of RSFC and weighted network analysis generated numerically more stable results in comparison with binarized network analysis. The highlighted black border marks are the average reliability across metrics for binarized (square) and weighted (circle) network analysis, respectively. Of note, the sensitivity varied dramatically among metrics. Small-world parameters and network efficiency were extremely sensitive to even little noise in functional connectivity while assortativity, hierarchy, synchronization and modularity were relatively resistant to noise. TRT, test-retest; RSFC, resting-state functional connectivity.
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pone-0021976-g011: TRT reliability of global network metrics as a function of noise in RSFC for S-AAL-based networks.Global network metrics were sensitive to disturbances of RSFC and weighted network analysis generated numerically more stable results in comparison with binarized network analysis. The highlighted black border marks are the average reliability across metrics for binarized (square) and weighted (circle) network analysis, respectively. Of note, the sensitivity varied dramatically among metrics. Small-world parameters and network efficiency were extremely sensitive to even little noise in functional connectivity while assortativity, hierarchy, synchronization and modularity were relatively resistant to noise. TRT, test-retest; RSFC, resting-state functional connectivity.

Mentions: By simulating functional connectivity matrices with different levels of noise, we found that: 1) for global network metrics, the TRT reliability was sensitive (F(5,55) = 23.303, p<10-11, repeated two-way ANOVA) to disturbances in functional connectivity values and weighted network analysis generated numerically more (F(1,11) = 5.183, p = 0.044, repeated two-way ANOVA) reliable results than binarized network analysis (Fig. 11); 2) for nodal network metrics, although sensitive to the levels of noise (F(5,25) = 7.762, p<10−3, repeated two-way ANOVA), they were highly resistant to numerical changes in functional connectivity and there were no differences (F(1,5) = 0.312, p = 0.601, repeated two-way ANOVA) in the resistance to noise between binarized and weighted network analyses (Fig. 12); 3) there were no differences in numerical stability against noise in functional connectivity (p>0.05 under each noise level) between the first-order and second-order network metrics (Table 2); 4) nodal network metrics were more numerically reliable than global network against noise in functional connectivity (p<10−3 under each noise level). Of note, although sensitive to functional connectivity noise, the degree varied dramatically among global metrics. For instance, small-world parameters and network efficiency were extremely sensitive to even little noise in functional connectivity while assortativity, hierarchy, synchronization and modularity were relatively resistant to noise (Fig. 11).


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)

TRT reliability of global network metrics as a function of noise in RSFC for S-AAL-based networks.Global network metrics were sensitive to disturbances of RSFC and weighted network analysis generated numerically more stable results in comparison with binarized network analysis. The highlighted black border marks are the average reliability across metrics for binarized (square) and weighted (circle) network analysis, respectively. Of note, the sensitivity varied dramatically among metrics. Small-world parameters and network efficiency were extremely sensitive to even little noise in functional connectivity while assortativity, hierarchy, synchronization and modularity were relatively resistant to noise. TRT, test-retest; RSFC, resting-state functional connectivity.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g011: TRT reliability of global network metrics as a function of noise in RSFC for S-AAL-based networks.Global network metrics were sensitive to disturbances of RSFC and weighted network analysis generated numerically more stable results in comparison with binarized network analysis. The highlighted black border marks are the average reliability across metrics for binarized (square) and weighted (circle) network analysis, respectively. Of note, the sensitivity varied dramatically among metrics. Small-world parameters and network efficiency were extremely sensitive to even little noise in functional connectivity while assortativity, hierarchy, synchronization and modularity were relatively resistant to noise. TRT, test-retest; RSFC, resting-state functional connectivity.
Mentions: By simulating functional connectivity matrices with different levels of noise, we found that: 1) for global network metrics, the TRT reliability was sensitive (F(5,55) = 23.303, p<10-11, repeated two-way ANOVA) to disturbances in functional connectivity values and weighted network analysis generated numerically more (F(1,11) = 5.183, p = 0.044, repeated two-way ANOVA) reliable results than binarized network analysis (Fig. 11); 2) for nodal network metrics, although sensitive to the levels of noise (F(5,25) = 7.762, p<10−3, repeated two-way ANOVA), they were highly resistant to numerical changes in functional connectivity and there were no differences (F(1,5) = 0.312, p = 0.601, repeated two-way ANOVA) in the resistance to noise between binarized and weighted network analyses (Fig. 12); 3) there were no differences in numerical stability against noise in functional connectivity (p>0.05 under each noise level) between the first-order and second-order network metrics (Table 2); 4) nodal network metrics were more numerically reliable than global network against noise in functional connectivity (p<10−3 under each noise level). Of note, although sensitive to functional connectivity noise, the degree varied dramatically among global metrics. For instance, small-world parameters and network efficiency were extremely sensitive to even little noise in functional connectivity while assortativity, hierarchy, synchronization and modularity were relatively resistant to noise (Fig. 11).

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