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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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Nodal TRT reliability of degree and its relationship with nodal degree centrality for F-DOS-based networks.(a) Nodal TRT reliability was mapped in anatomical space after average across scanning time interval, network type and network membership because of no effects of these factors on nodal reliability. (b) Nodal degree centrality (AUCs) was also mapped in anatomical space which was averaged across subjects and factors of scanning time interval, network type and network membership. Trend lines were further obtained by linear least-square fit to reveal the relationship between nodal degree centrality and their corresponding reliability after with (d) and without (c) correcting for the effects of regional size. Of note, the full names of region's abbreviations were listed as in Table S1. TRT, test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010); k, nodal degree; A, anterior; P, posterior; L, left; R, right.
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pone-0021976-g015: Nodal TRT reliability of degree and its relationship with nodal degree centrality for F-DOS-based networks.(a) Nodal TRT reliability was mapped in anatomical space after average across scanning time interval, network type and network membership because of no effects of these factors on nodal reliability. (b) Nodal degree centrality (AUCs) was also mapped in anatomical space which was averaged across subjects and factors of scanning time interval, network type and network membership. Trend lines were further obtained by linear least-square fit to reveal the relationship between nodal degree centrality and their corresponding reliability after with (d) and without (c) correcting for the effects of regional size. Of note, the full names of region's abbreviations were listed as in Table S1. TRT, test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010); k, nodal degree; A, anterior; P, posterior; L, left; R, right.

Mentions: Figure 14 delineated the nodal reliability for functional ROIs-based networks. No significant (p>0.05) effects were observed for TI, NM and NT on mean nodal reliability (Table 3), consistent with findings from structural ROIs-based networks (both S-AAL and S-HOA). Also analogous to findings of structural ROIs-based networks, nodal degree was found to show the highest reliability and least variance in compared with others (F(5,35) = 3.041, p = 0.022) (Fig. 8b). After averaged over factors of TI, NM and NT, mean nodal degree reliability showed that there were quite a few reliable regions distributed in bilateral temporal, parietal and the right frontal lobes (Fig. 15a). The nodal centrality pattern (Fig. 15b) can only explain a small fraction (R2<6%) of nodal reliability pattern (Fig. 15c and d). We also noted that the most reliable regions were predominantly located in the right hemisphere (Fig. 15a) and varied across nodal metrics (Fig. S5c).


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)

Nodal TRT reliability of degree and its relationship with nodal degree centrality for F-DOS-based networks.(a) Nodal TRT reliability was mapped in anatomical space after average across scanning time interval, network type and network membership because of no effects of these factors on nodal reliability. (b) Nodal degree centrality (AUCs) was also mapped in anatomical space which was averaged across subjects and factors of scanning time interval, network type and network membership. Trend lines were further obtained by linear least-square fit to reveal the relationship between nodal degree centrality and their corresponding reliability after with (d) and without (c) correcting for the effects of regional size. Of note, the full names of region's abbreviations were listed as in Table S1. TRT, test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010); k, nodal degree; A, anterior; P, posterior; L, left; R, right.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g015: Nodal TRT reliability of degree and its relationship with nodal degree centrality for F-DOS-based networks.(a) Nodal TRT reliability was mapped in anatomical space after average across scanning time interval, network type and network membership because of no effects of these factors on nodal reliability. (b) Nodal degree centrality (AUCs) was also mapped in anatomical space which was averaged across subjects and factors of scanning time interval, network type and network membership. Trend lines were further obtained by linear least-square fit to reveal the relationship between nodal degree centrality and their corresponding reliability after with (d) and without (c) correcting for the effects of regional size. Of note, the full names of region's abbreviations were listed as in Table S1. TRT, test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010); k, nodal degree; A, anterior; P, posterior; L, left; R, right.
Mentions: Figure 14 delineated the nodal reliability for functional ROIs-based networks. No significant (p>0.05) effects were observed for TI, NM and NT on mean nodal reliability (Table 3), consistent with findings from structural ROIs-based networks (both S-AAL and S-HOA). Also analogous to findings of structural ROIs-based networks, nodal degree was found to show the highest reliability and least variance in compared with others (F(5,35) = 3.041, p = 0.022) (Fig. 8b). After averaged over factors of TI, NM and NT, mean nodal degree reliability showed that there were quite a few reliable regions distributed in bilateral temporal, parietal and the right frontal lobes (Fig. 15a). The nodal centrality pattern (Fig. 15b) can only explain a small fraction (R2<6%) of nodal reliability pattern (Fig. 15c and d). We also noted that the most reliable regions were predominantly located in the right hemisphere (Fig. 15a) and varied across nodal metrics (Fig. S5c).

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