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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: 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.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.

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.


Comparison between traditional and modified degree metrics is shown. Results of the unweighted (U − URSE) and weighted degree metrics (W  − WRSE) are shown (p < 0.05, FDR corrected). Results of derivative metrics (WS − WSRSE and WF − WFRSE) are similar as the displayed results. The displayed results are based on data without global signal regression; the results with other data-preprocessing procedures are presented in the Figure S1 in Supplementary Material.
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Figure 8: Comparison between traditional and modified degree metrics is shown. Results of the unweighted (U − URSE) and weighted degree metrics (W  − WRSE) are shown (p < 0.05, FDR corrected). Results of derivative metrics (WS − WSRSE and WF − WFRSE) are similar as the displayed results. The displayed results are based on data without global signal regression; the results with other data-preprocessing procedures are presented in the Figure S1 in Supplementary Material.

Mentions: To further examine the impact of region sizes, we compared traditional degree metrics (U, W, WS, and WF) with modified degree metrics (URSE, WRSE, WSRSE, and WFRSE) under different connectivity thresholds by paired two-sample t-test. We conducted the comparison at a range of connectivity thresholds with Td ≤ 0.45, because very high Ta results in low test–retest reliability of degree metrics and may comprise the network architecture (Figure 7). All types of degree metrics produce similar results (Figure 8), even under different data pre-processing procedures (Figure S1 in Supplementary Material). Compared with the modified degree metrics, the traditional degree metrics have a significantly higher degree in the visual cortex, precuneus/posterior cingulate cortex, posterior parietal cortex, and a significantly lower degree in the insular cortex, sensory and motor cortex, supplementary motor area, anterior cingulate cortex, parahippocampus, and areas around temporal pole (p < 0.05, FDR corrected). This result is consistent with region-growing results where larger brain regions tend to have higher degree (Figures 6B and 8).


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)

Comparison between traditional and modified degree metrics is shown. Results of the unweighted (U − URSE) and weighted degree metrics (W  − WRSE) are shown (p < 0.05, FDR corrected). Results of derivative metrics (WS − WSRSE and WF − WFRSE) are similar as the displayed results. The displayed results are based on data without global signal regression; the results with other data-preprocessing procedures are presented in the Figure S1 in Supplementary Material.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Comparison between traditional and modified degree metrics is shown. Results of the unweighted (U − URSE) and weighted degree metrics (W  − WRSE) are shown (p < 0.05, FDR corrected). Results of derivative metrics (WS − WSRSE and WF − WFRSE) are similar as the displayed results. The displayed results are based on data without global signal regression; the results with other data-preprocessing procedures are presented in the Figure S1 in Supplementary Material.
Mentions: To further examine the impact of region sizes, we compared traditional degree metrics (U, W, WS, and WF) with modified degree metrics (URSE, WRSE, WSRSE, and WFRSE) under different connectivity thresholds by paired two-sample t-test. We conducted the comparison at a range of connectivity thresholds with Td ≤ 0.45, because very high Ta results in low test–retest reliability of degree metrics and may comprise the network architecture (Figure 7). All types of degree metrics produce similar results (Figure 8), even under different data pre-processing procedures (Figure S1 in Supplementary Material). Compared with the modified degree metrics, the traditional degree metrics have a significantly higher degree in the visual cortex, precuneus/posterior cingulate cortex, posterior parietal cortex, and a significantly lower degree in the insular cortex, sensory and motor cortex, supplementary motor area, anterior cingulate cortex, parahippocampus, and areas around temporal pole (p < 0.05, FDR corrected). This result is consistent with region-growing results where larger brain regions tend to have higher degree (Figures 6B and 8).

Bottom Line: 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.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.

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.