Limits...
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.

Show MeSH

Related in: MedlinePlus

TRT reliability of global network metrics as a function of sparsity threshold for F-DOS-based networks.ICC values less than 0.25 were mapped to a single color of dark blue as well dark red color for ICC values greater than 0.75, respectively. Multiple network metrics showed modest reliability in certain threshold range. Network (+/-), networks constructed using absolute both positive and negative correlations; Network (+), networks constructed using only positive correlations; Binarized, binarized network anlysis; Weighted, weighted network analysis; TRT: test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3139595&req=5

pone-0021976-g013: TRT reliability of global network metrics as a function of sparsity threshold for F-DOS-based networks.ICC values less than 0.25 were mapped to a single color of dark blue as well dark red color for ICC values greater than 0.75, respectively. Multiple network metrics showed modest reliability in certain threshold range. Network (+/-), networks constructed using absolute both positive and negative correlations; Network (+), networks constructed using only positive correlations; Binarized, binarized network anlysis; Weighted, weighted network analysis; TRT: test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).

Mentions: In compared with structural ROIs-based networks, functional ROIs-based networks showed fair reliability in more global metrics over wider threshold range, especially for networks of positive correlations (Fig. 13). For example, small-world parameters (clustering coefficient , characteristic path length , normalized clustering coefficient , normalized characteristic path length and small-worldness ) were fairly reliable (predominantly for long-term reliability) for positive networks. The threshold-independent reliability was presented in the right panel of Figure 6a. Subsequent statistical analyses revealed that, in contrast with the measure-related differences in global network reliability observed for structural ROIs based-networks (Fig. 6b, left and Fig. S6b), there was no significant differences (F(11,77) = 1.298, p = 0.242) among global metrics (Fig. 6b, right) for functional ROIs-based networks. Furthermore, unlike the sensitivity of global network reliability to experimental factor of TI and graph-based analytical strategies of NM and NT for structural ROIs-based networks, reliability of functional ROIs-based networks was robust against these factors (p>0.05) (Table 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)

TRT reliability of global network metrics as a function of sparsity threshold for F-DOS-based networks.ICC values less than 0.25 were mapped to a single color of dark blue as well dark red color for ICC values greater than 0.75, respectively. Multiple network metrics showed modest reliability in certain threshold range. Network (+/-), networks constructed using absolute both positive and negative correlations; Network (+), networks constructed using only positive correlations; Binarized, binarized network anlysis; Weighted, weighted network analysis; TRT: test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0021976-g013: TRT reliability of global network metrics as a function of sparsity threshold for F-DOS-based networks.ICC values less than 0.25 were mapped to a single color of dark blue as well dark red color for ICC values greater than 0.75, respectively. Multiple network metrics showed modest reliability in certain threshold range. Network (+/-), networks constructed using absolute both positive and negative correlations; Network (+), networks constructed using only positive correlations; Binarized, binarized network anlysis; Weighted, weighted network analysis; TRT: test-retest; F-DOS, functional ROIs from Dosenbach et al. (2006, 2010).
Mentions: In compared with structural ROIs-based networks, functional ROIs-based networks showed fair reliability in more global metrics over wider threshold range, especially for networks of positive correlations (Fig. 13). For example, small-world parameters (clustering coefficient , characteristic path length , normalized clustering coefficient , normalized characteristic path length and small-worldness ) were fairly reliable (predominantly for long-term reliability) for positive networks. The threshold-independent reliability was presented in the right panel of Figure 6a. Subsequent statistical analyses revealed that, in contrast with the measure-related differences in global network reliability observed for structural ROIs based-networks (Fig. 6b, left and Fig. S6b), there was no significant differences (F(11,77) = 1.298, p = 0.242) among global metrics (Fig. 6b, right) for functional ROIs-based networks. Furthermore, unlike the sensitivity of global network reliability to experimental factor of TI and graph-based analytical strategies of NM and NT for structural ROIs-based networks, reliability of functional ROIs-based networks was robust against these factors (p>0.05) (Table 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