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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 S-AAL-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; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; k, nodal degree; A, anterior; P, posterior; L, left; R, right.
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pone-0021976-g009: Nodal TRT reliability of degree and its relationship with nodal degree centrality for S-AAL-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; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; k, nodal degree; A, anterior; P, posterior; L, left; R, right.

Mentions: Finally, nodal reliability distributed non-uniformly over the brain, an observation irrespective of nodal metrics and factors of TI, NM and NT. To highlight those reliable regions, we selectively mapped nodal reliability of degree of all regions after averaging over factors of TI, NM and NT (Fig. 9a). This was because nodal degree showed higher reliability and less variance as compared to other nodal metrics and was robust to TI and NM as well as NT. As shown in Figure 9a, some association and limbic/paralimbic cortex regions [53] exhibited fair reliability that were predominately located in bilateral parietal and occipital lobes, such as association cortex regions of the left angular gyrus (ANG), right paracentral lobule (PCL), right precuneus (PCUN), bilateral supramarginal gyrus (SMG), bilateral dorsolateral superior frontal gyrus (SFGdor), right medial superior frontal gyrus (SFGmed) and left superior occipital gyrus (SOG), and limbic/paralimbic regions of the bilateral hippocampus (HIP) and the left posterior cingulate gyrus. In addition, one primary cortex region of the left calcarine fissure (CAL) was also found to be fairly reliable. To test whether or not nodal reliability was related with nodal centrality, we also mapped the mean nodal degree over TI, NM and NT (Fig. 9b) and found visually different patterns between nodal degree and nodal reliability. The most reliable regions located on the posterior while the most connected regions on the anterior portions of the brain. Further quantitative correlation analysis revealed that only tiny variance (R2<7%) in nodal reliability could be explained by nodal degree centrality in both cases of with (r = 0.255, p = 0.015, Fig. 9c) and without (r = 0.263, p = 0.012, Fig. 9d) correction for regional nodal size. To test whether there exist a relationship between spatial location and nodal reliability, we compared nodal degree reliability between anterior (y>0) and posterior (y<0) regions. The results revealed that posterior regions were more reliable than anterior regions even if nodal mean functional connectivity differences were corrected (t(87) = 2.801, p = 0.006). In addition, we also found dramatically different patterns across nodal metrics even for those most reliable regions except for the right PCL (Fig. S5a).


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 S-AAL-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; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; 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-g009: Nodal TRT reliability of degree and its relationship with nodal degree centrality for S-AAL-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; S-AAL, structural ROIs from Anatomical Automatic Labeling atlas; k, nodal degree; A, anterior; P, posterior; L, left; R, right.
Mentions: Finally, nodal reliability distributed non-uniformly over the brain, an observation irrespective of nodal metrics and factors of TI, NM and NT. To highlight those reliable regions, we selectively mapped nodal reliability of degree of all regions after averaging over factors of TI, NM and NT (Fig. 9a). This was because nodal degree showed higher reliability and less variance as compared to other nodal metrics and was robust to TI and NM as well as NT. As shown in Figure 9a, some association and limbic/paralimbic cortex regions [53] exhibited fair reliability that were predominately located in bilateral parietal and occipital lobes, such as association cortex regions of the left angular gyrus (ANG), right paracentral lobule (PCL), right precuneus (PCUN), bilateral supramarginal gyrus (SMG), bilateral dorsolateral superior frontal gyrus (SFGdor), right medial superior frontal gyrus (SFGmed) and left superior occipital gyrus (SOG), and limbic/paralimbic regions of the bilateral hippocampus (HIP) and the left posterior cingulate gyrus. In addition, one primary cortex region of the left calcarine fissure (CAL) was also found to be fairly reliable. To test whether or not nodal reliability was related with nodal centrality, we also mapped the mean nodal degree over TI, NM and NT (Fig. 9b) and found visually different patterns between nodal degree and nodal reliability. The most reliable regions located on the posterior while the most connected regions on the anterior portions of the brain. Further quantitative correlation analysis revealed that only tiny variance (R2<7%) in nodal reliability could be explained by nodal degree centrality in both cases of with (r = 0.255, p = 0.015, Fig. 9c) and without (r = 0.263, p = 0.012, Fig. 9d) correction for regional nodal size. To test whether there exist a relationship between spatial location and nodal reliability, we compared nodal degree reliability between anterior (y>0) and posterior (y<0) regions. The results revealed that posterior regions were more reliable than anterior regions even if nodal mean functional connectivity differences were corrected (t(87) = 2.801, p = 0.006). In addition, we also found dramatically different patterns across nodal metrics even for those most reliable regions except for the right PCL (Fig. S5a).

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