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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 region-size impact and its correction strategy are shown. Circles represent nodes, where the network has two connected large functional regions that each consists of two voxels (blue and purple circles) and two connected small functional regions that each consists of one voxel (red and orange circles). Lines represent connections, where gray lines represent connections from other functional regions, red lines represent self-connections, and dashed lines represent connections divided by the size of the functional regions. The number in a circle represents the degree of centrality. (A,B) show how the voxel’s degree scales with functional region size, and (C) shows the effective strategy to correct this impact.
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Figure 1: The region-size impact and its correction strategy are shown. Circles represent nodes, where the network has two connected large functional regions that each consists of two voxels (blue and purple circles) and two connected small functional regions that each consists of one voxel (red and orange circles). Lines represent connections, where gray lines represent connections from other functional regions, red lines represent self-connections, and dashed lines represent connections divided by the size of the functional regions. The number in a circle represents the degree of centrality. (A,B) show how the voxel’s degree scales with functional region size, and (C) shows the effective strategy to correct this impact.

Mentions: In the light of the vacancy, we propose a modified version of degree-based method that can reduce the impact of region size but retaining its data-driven advantage. Briefly, this method is achieved by (1) disregarding self-connections among voxels within the same region and (2) regulating connections from voxels of other regions based on the sizes of those regions (Figure 1). The prerequisite of this strategy is to define the sizes of functional regions. In general, one way to approach this problem is to refer to anatomical brain atlases or predefined brain parcellations, but these are either inaccurate or inconsistent (23–26). Moreover, there is no consensus on how many functional regions the brain contains. Instead, we use a modified region-growing method to estimate the size of the functional region to which each voxel may belong, and then perform the above strategy. Our modified method inherits the data-driven advantage of traditional degree-based method and allows direct evaluation of this impact of region size. In addition, we also evaluate the test–retest reliability and other factors that may influence degree metrics, including global signal regression (GSR), head motions, network types (unweighted and weighted), and connectivity thresholds, as well as testing the sensitivity in group-statistic comparisons. The comprehensive assessment will prove the reliability of our method as a new tool for the degree-based analysis.


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 region-size impact and its correction strategy are shown. Circles represent nodes, where the network has two connected large functional regions that each consists of two voxels (blue and purple circles) and two connected small functional regions that each consists of one voxel (red and orange circles). Lines represent connections, where gray lines represent connections from other functional regions, red lines represent self-connections, and dashed lines represent connections divided by the size of the functional regions. The number in a circle represents the degree of centrality. (A,B) show how the voxel’s degree scales with functional region size, and (C) shows the effective strategy to correct this impact.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The region-size impact and its correction strategy are shown. Circles represent nodes, where the network has two connected large functional regions that each consists of two voxels (blue and purple circles) and two connected small functional regions that each consists of one voxel (red and orange circles). Lines represent connections, where gray lines represent connections from other functional regions, red lines represent self-connections, and dashed lines represent connections divided by the size of the functional regions. The number in a circle represents the degree of centrality. (A,B) show how the voxel’s degree scales with functional region size, and (C) shows the effective strategy to correct this impact.
Mentions: In the light of the vacancy, we propose a modified version of degree-based method that can reduce the impact of region size but retaining its data-driven advantage. Briefly, this method is achieved by (1) disregarding self-connections among voxels within the same region and (2) regulating connections from voxels of other regions based on the sizes of those regions (Figure 1). The prerequisite of this strategy is to define the sizes of functional regions. In general, one way to approach this problem is to refer to anatomical brain atlases or predefined brain parcellations, but these are either inaccurate or inconsistent (23–26). Moreover, there is no consensus on how many functional regions the brain contains. Instead, we use a modified region-growing method to estimate the size of the functional region to which each voxel may belong, and then perform the above strategy. Our modified method inherits the data-driven advantage of traditional degree-based method and allows direct evaluation of this impact of region size. In addition, we also evaluate the test–retest reliability and other factors that may influence degree metrics, including global signal regression (GSR), head motions, network types (unweighted and weighted), and connectivity thresholds, as well as testing the sensitivity in group-statistic comparisons. The comprehensive assessment will prove the reliability of our method as a new tool for the degree-based analysis.

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.