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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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Related in: MedlinePlus

The similarity between inter-scan ICC-based reliability and inter-scan Pearson correlation coefficients for S-AAL-based networks.The reliability and correlation analyses revealed highly consistent results (r>0.9 under most conditions), ruling out the possibility of linear scaling biases of network metrics across test and retest scans that will lead to low TRT reliability.
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pone-0021976-g010: The similarity between inter-scan ICC-based reliability and inter-scan Pearson correlation coefficients for S-AAL-based networks.The reliability and correlation analyses revealed highly consistent results (r>0.9 under most conditions), ruling out the possibility of linear scaling biases of network metrics across test and retest scans that will lead to low TRT reliability.

Mentions: To test the possibility of linear scaling biases across test and retest scans which may result in low TRT reliability, we calculated the inter-scan Pearson correlation coefficient for each global network metric (AUC) across subjects for both short-term and long-term scans. Further scatter plots between ICC values and Pearson correlation coefficients revealed highly correlated patterns (r>0.9 under most conditions) (Fig. 10), suggesting consistent results revealed by the two measures.


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)

The similarity between inter-scan ICC-based reliability and inter-scan Pearson correlation coefficients for S-AAL-based networks.The reliability and correlation analyses revealed highly consistent results (r>0.9 under most conditions), ruling out the possibility of linear scaling biases of network metrics across test and retest scans that will lead to low TRT reliability.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g010: The similarity between inter-scan ICC-based reliability and inter-scan Pearson correlation coefficients for S-AAL-based networks.The reliability and correlation analyses revealed highly consistent results (r>0.9 under most conditions), ruling out the possibility of linear scaling biases of network metrics across test and retest scans that will lead to low TRT reliability.
Mentions: To test the possibility of linear scaling biases across test and retest scans which may result in low TRT reliability, we calculated the inter-scan Pearson correlation coefficient for each global network metric (AUC) across subjects for both short-term and long-term scans. Further scatter plots between ICC values and Pearson correlation coefficients revealed highly correlated patterns (r>0.9 under most conditions) (Fig. 10), suggesting consistent results revealed by the two measures.

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