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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.


The overall degree distribution is similar across different types of degree metrics. The unweighted and weighted degree metrics (U, URSE, W, and WRSE) are displayed; other degree metrics (WS, WSRSE, WF, WFRSE) are presented in the Figure S2 in Supplementary Material. Brain regions with high degree are consistently observed in the visual cortex, precuneus/posterior cingulate cortex, thalamus, posterior parietal cortex, anterior cingulate cortex, anterior insular cortex, and medial temporal cortex.
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Figure 9: The overall degree distribution is similar across different types of degree metrics. The unweighted and weighted degree metrics (U, URSE, W, and WRSE) are displayed; other degree metrics (WS, WSRSE, WF, WFRSE) are presented in the Figure S2 in Supplementary Material. Brain regions with high degree are consistently observed in the visual cortex, precuneus/posterior cingulate cortex, thalamus, posterior parietal cortex, anterior cingulate cortex, anterior insular cortex, and medial temporal cortex.

Mentions: Although the region-size variance has a significant effect on degree metrics, the effect does not affect the distribution of high-degree regions (Figure 9), even under very high connectivity thresholds that may induce low test–retest reliability (Figure S2 in Supplementary Material). All degree metrics produce similar patterns of degree distribution. Brain regions with a high degree are consistently observed in the visual cortex, precuneus/posterior cingulate cortex, thalamus, posterior parietal cortex, anterior cingulate cortex, anterior insular cortex, and medial temporal cortex.


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)

The overall degree distribution is similar across different types of degree metrics. The unweighted and weighted degree metrics (U, URSE, W, and WRSE) are displayed; other degree metrics (WS, WSRSE, WF, WFRSE) are presented in the Figure S2 in Supplementary Material. Brain regions with high degree are consistently observed in the visual cortex, precuneus/posterior cingulate cortex, thalamus, posterior parietal cortex, anterior cingulate cortex, anterior insular cortex, and medial temporal cortex.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: The overall degree distribution is similar across different types of degree metrics. The unweighted and weighted degree metrics (U, URSE, W, and WRSE) are displayed; other degree metrics (WS, WSRSE, WF, WFRSE) are presented in the Figure S2 in Supplementary Material. Brain regions with high degree are consistently observed in the visual cortex, precuneus/posterior cingulate cortex, thalamus, posterior parietal cortex, anterior cingulate cortex, anterior insular cortex, and medial temporal cortex.
Mentions: Although the region-size variance has a significant effect on degree metrics, the effect does not affect the distribution of high-degree regions (Figure 9), even under very high connectivity thresholds that may induce low test–retest reliability (Figure S2 in Supplementary Material). All degree metrics produce similar patterns of degree distribution. Brain regions with a high degree are consistently observed in the visual cortex, precuneus/posterior cingulate cortex, thalamus, posterior parietal cortex, anterior cingulate cortex, anterior insular cortex, and medial temporal cortex.

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.