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A data-driven method to reduce the impact of region size on degree metrics in voxel-wise functional brain networks.

Liu C, Tian X - Front Neurol (2014)

Bottom Line: Despite its extensive applications, defining nodes as voxels without considering the different sizes of brain regions may result in a network where the degree cannot accurately represent the importance of nodes.However, the locations of prominent hubs were stable even after correcting the impact.These findings were robust under different connectivity thresholds, degree metrics, data-preprocessing procedures, and datasets.

View Article: PubMed Central - PubMed

Affiliation: Queensland Brain Institute, The University of Queensland , St Lucia, QLD , Australia.

ABSTRACT
Degree, which is the number of connections incident upon a node, measures the relative importance of the node within a network. By computing degree metrics in voxel-wise functional brain networks, many studies performed high-resolution mapping of brain network hubs using resting-state functional magnetic resonance imaging. Despite its extensive applications, defining nodes as voxels without considering the different sizes of brain regions may result in a network where the degree cannot accurately represent the importance of nodes. In this study, we designed a data-driven method to reduce this impact of the region size in degree metrics by (1) disregarding all self-connections among voxels within the same region and (2) regulating connections from voxels of other regions by the sizes of those regions. The modified method that we proposed allowed direct evaluation of the impact of the region size, showing that traditional degree metrics overestimated the degree of previous identified hubs in humans, including the visual cortex, precuneus/posterior cingulate cortex, and posterior parietal cortex, and underestimated the degree of regions including the insular cortex, anterior cingulate cortex, parahippocampus, sensory and motor cortex, and supplementary motor area. However, the locations of prominent hubs were stable even after correcting the impact. These findings were robust under different connectivity thresholds, degree metrics, data-preprocessing procedures, and datasets. In addition, our modified method improved test-retest reliability of degree metrics as well as the sensitivity in group-statistic comparisons. As a promising new tool, our method may reveal network properties that better represent true brain architecture without compromising its data-driven advantage.

No MeSH data available.


Statistical T-values comparison between the traditional degree metrics and the modified degree metrics is shown. Cluster 1 is the left lateral prefrontal cortex (true-positive), cluster 2 is the left posterior parietal cortex (true-positive), and cluster 3 is the visual cortex (false-positive). Bars represent mean ± SEM, ***p < 0.001.
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Figure 11: Statistical T-values comparison between the traditional degree metrics and the modified degree metrics is shown. Cluster 1 is the left lateral prefrontal cortex (true-positive), cluster 2 is the left posterior parietal cortex (true-positive), and cluster 3 is the visual cortex (false-positive). Bars represent mean ± SEM, ***p < 0.001.

Mentions: In addition, we compared the performance of the traditional degree metrics and the modified degree metrics in the statistical comparison between the cocaine-dependent and control group. Figure 10 shows results of two-sample t-tests between the degree metrics of the cocaine-dependent group and those of the control group, under connectivity thresholds 0.2. Results under the other thresholds are similar as that under 0.2. When the traditional degree metrics are used, the cocaine-dependent group has a significantly higher unweighted degree (U) in the left lateral prefrontal cortex and a lower weighted degree (W) in the visual cortex than the control group (p < 0.05, corrected). When the modified degree metrics are used, the cocaine-dependent group has a significantly higher unweighted (URSE) and weighted degree (WRSE) in the left lateral prefrontal cortex and left posterior parietal cortex than the control group. Thus, the left lateral prefrontal cortex (cluster 1), left posterior parietal cortex (cluster 2), and visual cortex (cluster 3) show significant differences in degree between the two groups. Statistical T-values from voxels in these clusters were extracted, and Wilcoxon matched pairs tests were carried out between the traditional degree metrics and the modified degree metrics for each cluster. As shown in Figure 11, the modified degree metrics produce significantly higher T-values in the left lateral prefrontal cortex and the left posterior parietal cortex (true-positive), but lower T-values in the visual cortex (false-positive, see Discussion) than the traditional degree metrics.


A data-driven method to reduce the impact of region size on degree metrics in voxel-wise functional brain networks.

Liu C, Tian X - Front Neurol (2014)

Statistical T-values comparison between the traditional degree metrics and the modified degree metrics is shown. Cluster 1 is the left lateral prefrontal cortex (true-positive), cluster 2 is the left posterior parietal cortex (true-positive), and cluster 3 is the visual cortex (false-positive). Bars represent mean ± SEM, ***p < 0.001.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4195315&req=5

Figure 11: Statistical T-values comparison between the traditional degree metrics and the modified degree metrics is shown. Cluster 1 is the left lateral prefrontal cortex (true-positive), cluster 2 is the left posterior parietal cortex (true-positive), and cluster 3 is the visual cortex (false-positive). Bars represent mean ± SEM, ***p < 0.001.
Mentions: In addition, we compared the performance of the traditional degree metrics and the modified degree metrics in the statistical comparison between the cocaine-dependent and control group. Figure 10 shows results of two-sample t-tests between the degree metrics of the cocaine-dependent group and those of the control group, under connectivity thresholds 0.2. Results under the other thresholds are similar as that under 0.2. When the traditional degree metrics are used, the cocaine-dependent group has a significantly higher unweighted degree (U) in the left lateral prefrontal cortex and a lower weighted degree (W) in the visual cortex than the control group (p < 0.05, corrected). When the modified degree metrics are used, the cocaine-dependent group has a significantly higher unweighted (URSE) and weighted degree (WRSE) in the left lateral prefrontal cortex and left posterior parietal cortex than the control group. Thus, the left lateral prefrontal cortex (cluster 1), left posterior parietal cortex (cluster 2), and visual cortex (cluster 3) show significant differences in degree between the two groups. Statistical T-values from voxels in these clusters were extracted, and Wilcoxon matched pairs tests were carried out between the traditional degree metrics and the modified degree metrics for each cluster. As shown in Figure 11, the modified degree metrics produce significantly higher T-values in the left lateral prefrontal cortex and the left posterior parietal cortex (true-positive), but lower T-values in the visual cortex (false-positive, see Discussion) than the traditional degree metrics.

Bottom Line: Despite its extensive applications, defining nodes as voxels without considering the different sizes of brain regions may result in a network where the degree cannot accurately represent the importance of nodes.However, the locations of prominent hubs were stable even after correcting the impact.These findings were robust under different connectivity thresholds, degree metrics, data-preprocessing procedures, and datasets.

View Article: PubMed Central - PubMed

Affiliation: Queensland Brain Institute, The University of Queensland , St Lucia, QLD , Australia.

ABSTRACT
Degree, which is the number of connections incident upon a node, measures the relative importance of the node within a network. By computing degree metrics in voxel-wise functional brain networks, many studies performed high-resolution mapping of brain network hubs using resting-state functional magnetic resonance imaging. Despite its extensive applications, defining nodes as voxels without considering the different sizes of brain regions may result in a network where the degree cannot accurately represent the importance of nodes. In this study, we designed a data-driven method to reduce this impact of the region size in degree metrics by (1) disregarding all self-connections among voxels within the same region and (2) regulating connections from voxels of other regions by the sizes of those regions. The modified method that we proposed allowed direct evaluation of the impact of the region size, showing that traditional degree metrics overestimated the degree of previous identified hubs in humans, including the visual cortex, precuneus/posterior cingulate cortex, and posterior parietal cortex, and underestimated the degree of regions including the insular cortex, anterior cingulate cortex, parahippocampus, sensory and motor cortex, and supplementary motor area. However, the locations of prominent hubs were stable even after correcting the impact. These findings were robust under different connectivity thresholds, degree metrics, data-preprocessing procedures, and datasets. In addition, our modified method improved test-retest reliability of degree metrics as well as the sensitivity in group-statistic comparisons. As a promising new tool, our method may reveal network properties that better represent true brain architecture without compromising its data-driven advantage.

No MeSH data available.