A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks
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 mag...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-10-01
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| Series: | Frontiers in Neurology |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fneur.2014.00199/full |
| _version_ | 1818922411080286208 |
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| author | Cirong eLiu Xiaoguang eTian |
| author_facet | Cirong eLiu Xiaoguang eTian |
| author_sort | Cirong eLiu |
| collection | DOAJ |
| description | 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 a network. 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 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. |
| first_indexed | 2024-12-20T01:53:06Z |
| format | Article |
| id | doaj.art-fe1366c2ac77414aac01b563d69a1ef3 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-20T01:53:06Z |
| publishDate | 2014-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-fe1366c2ac77414aac01b563d69a1ef32022-12-21T19:57:35ZengFrontiers Media S.A.Frontiers in Neurology1664-22952014-10-01510.3389/fneur.2014.00199109331A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networksCirong eLiu0Xiaoguang eTian1The University of QueenslandWerner Reichardt Centre for Integrative NeuroscienceDegree, 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 a network. 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 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.http://journal.frontiersin.org/Journal/10.3389/fneur.2014.00199/fulldegreebrain networksresting state fMRIFunctional hubsRegion growing |
| spellingShingle | Cirong eLiu Xiaoguang eTian A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks Frontiers in Neurology degree brain networks resting state fMRI Functional hubs Region growing |
| title | A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks |
| title_full | A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks |
| title_fullStr | A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks |
| title_full_unstemmed | A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks |
| title_short | A data-driven method to reduce the impact of region-size on degree metrics in voxel-wise functional brain networks |
| title_sort | data driven method to reduce the impact of region size on degree metrics in voxel wise functional brain networks |
| topic | degree brain networks resting state fMRI Functional hubs Region growing |
| url | http://journal.frontiersin.org/Journal/10.3389/fneur.2014.00199/full |
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