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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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Relationship between RSFC and TRT reliability.Scatter plots of mean connectivity strength against corresponding ICC values are depicted to show the relationship for both S-AAL (a) and F-DOS (b) based correlation matrices. The trend lines were obtained by linear least-square fit. Significant (p<0.05) positive correlations were found between positive RSFC and their corresponding ICC values for both ROIs sets and for both short-term and long-term scanning. In addition, significant negative correlations were also found for negative RSFC with their corresponding ICC values but only for F-DOS-based correlation matrices. These findings suggest higher reliability for stronger RSFC. Functional connections linking inter-hemisphere homotopic regions are highlighted by plus signs (+) for S-AAL but not for F-DOS because of the absence of direct correspondence. RSFC, resting-state functional connectivity; TRT, test-retest; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).
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pone-0021976-g003: Relationship between RSFC and TRT reliability.Scatter plots of mean connectivity strength against corresponding ICC values are depicted to show the relationship for both S-AAL (a) and F-DOS (b) based correlation matrices. The trend lines were obtained by linear least-square fit. Significant (p<0.05) positive correlations were found between positive RSFC and their corresponding ICC values for both ROIs sets and for both short-term and long-term scanning. In addition, significant negative correlations were also found for negative RSFC with their corresponding ICC values but only for F-DOS-based correlation matrices. These findings suggest higher reliability for stronger RSFC. Functional connections linking inter-hemisphere homotopic regions are highlighted by plus signs (+) for S-AAL but not for F-DOS because of the absence of direct correspondence. RSFC, resting-state functional connectivity; TRT, test-retest; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).

Mentions: To explore the relationship between connectivity strength and reliability, linearly fitted lines were obtained separately for positive connections and negative connections with their corresponding ICC values. We found significantly positive correlations (Pearson correlation) between positive connections and their ICC values for both short-term (r = 0.135, p<10−7) and long-term (r = 0.145, p<10−8) scans (Fig. 3a). No significant correlations were found between negative correlations and their ICC values (p>0.3 for both short-term and long-term scans) (Fig. 3a). These findings indicate that reliability of functional connectivity was partly determined by their strength, whereas functional connectivity strength had limited predictive ability to their reliability since the small amount of variance in the functional connectivity reliability explained by their strength (R2<3%).


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)

Relationship between RSFC and TRT reliability.Scatter plots of mean connectivity strength against corresponding ICC values are depicted to show the relationship for both S-AAL (a) and F-DOS (b) based correlation matrices. The trend lines were obtained by linear least-square fit. Significant (p<0.05) positive correlations were found between positive RSFC and their corresponding ICC values for both ROIs sets and for both short-term and long-term scanning. In addition, significant negative correlations were also found for negative RSFC with their corresponding ICC values but only for F-DOS-based correlation matrices. These findings suggest higher reliability for stronger RSFC. Functional connections linking inter-hemisphere homotopic regions are highlighted by plus signs (+) for S-AAL but not for F-DOS because of the absence of direct correspondence. RSFC, resting-state functional connectivity; TRT, test-retest; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g003: Relationship between RSFC and TRT reliability.Scatter plots of mean connectivity strength against corresponding ICC values are depicted to show the relationship for both S-AAL (a) and F-DOS (b) based correlation matrices. The trend lines were obtained by linear least-square fit. Significant (p<0.05) positive correlations were found between positive RSFC and their corresponding ICC values for both ROIs sets and for both short-term and long-term scanning. In addition, significant negative correlations were also found for negative RSFC with their corresponding ICC values but only for F-DOS-based correlation matrices. These findings suggest higher reliability for stronger RSFC. Functional connections linking inter-hemisphere homotopic regions are highlighted by plus signs (+) for S-AAL but not for F-DOS because of the absence of direct correspondence. RSFC, resting-state functional connectivity; TRT, test-retest; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).
Mentions: To explore the relationship between connectivity strength and reliability, linearly fitted lines were obtained separately for positive connections and negative connections with their corresponding ICC values. We found significantly positive correlations (Pearson correlation) between positive connections and their ICC values for both short-term (r = 0.135, p<10−7) and long-term (r = 0.145, p<10−8) scans (Fig. 3a). No significant correlations were found between negative correlations and their ICC values (p>0.3 for both short-term and long-term scans) (Fig. 3a). These findings indicate that reliability of functional connectivity was partly determined by their strength, whereas functional connectivity strength had limited predictive ability to their reliability since the small amount of variance in the functional connectivity reliability explained by their strength (R2<3%).

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