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| Field |
Value |
| Title: | Reproducibility of structural brain connectivity and network metrics using probabilistic diffusion tractography |
| Authors: | 蔡尚岳
Tsai, Shang-Yueh |
| Contributors: | 應物所 |
| Date: | 2018 |
| Issue Date: | 2018-09-11 18:24:51 (UTC+8) |
| Abstract: | The structural connectivity network constructed using probabilistic diffusion tractography can be characterized by the network metrics. In this study, short-term test-retest reproducibility of structural networks and network metrics were evaluated on 30 subjects in terms of within- and between-subject coefficient of variance (CVws, CVbs), and intra class coefficient (ICC) using various connectivity thresholds. The short-term reproducibility under various connectivity thresholds were also investigated when subject groups have same or different sparsity. In summary, connectivity threshold of 0.01 can exclude around 80% of the edges with CVws = 73.2 ± 37.7%, CVbs = 119.3 ± 44.0% and ICC = 0.62 ± 0.19. The rest 20% edges have CVws < 45%, CVbs < 90%, ICC = 0.75 ± 0.12. The presence of 1% difference in the sparsity can cause additional within-subject variations on network metrics. In conclusion, applying connectivity thresholds on structural network to exclude spurious connections for the network analysis should be considered as necessities. Our findings suggest that a connectivity threshold over 0.01 can be applied without significant effect on the short-term when network metrics are evaluated at the same sparsity in subject group. When the sparsity is not the same, the procedure of integration over various connectivity thresholds can provide reliable estimation of network metrics. |
| Relation: | Scientific Reportsvolume 8, Article number: 11562 |
| Data Type: | article |
| DOI: | https://doi.org/10.1038/s41598-018-29943-0 |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 應物所 | |
| dc.creator (Authors) | 蔡尚岳 | zh_TW |
| dc.creator (Authors) | Tsai, Shang-Yueh | en_US |
| dc.date (Date) | 2018 | |
| dc.date.accessioned | 2018-09-11 18:24:51 (UTC+8) | - |
| dc.date.available | 2018-09-11 18:24:51 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2018-09-11 18:24:51 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/120067 | - |
| dc.description.abstract (Abstract) | The structural connectivity network constructed using probabilistic diffusion tractography can be characterized by the network metrics. In this study, short-term test-retest reproducibility of structural networks and network metrics were evaluated on 30 subjects in terms of within- and between-subject coefficient of variance (CVws, CVbs), and intra class coefficient (ICC) using various connectivity thresholds. The short-term reproducibility under various connectivity thresholds were also investigated when subject groups have same or different sparsity. In summary, connectivity threshold of 0.01 can exclude around 80% of the edges with CVws = 73.2 ± 37.7%, CVbs = 119.3 ± 44.0% and ICC = 0.62 ± 0.19. The rest 20% edges have CVws < 45%, CVbs < 90%, ICC = 0.75 ± 0.12. The presence of 1% difference in the sparsity can cause additional within-subject variations on network metrics. In conclusion, applying connectivity thresholds on structural network to exclude spurious connections for the network analysis should be considered as necessities. Our findings suggest that a connectivity threshold over 0.01 can be applied without significant effect on the short-term when network metrics are evaluated at the same sparsity in subject group. When the sparsity is not the same, the procedure of integration over various connectivity thresholds can provide reliable estimation of network metrics. | en_US |
| dc.format.extent | 4670702 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (Relation) | Scientific Reportsvolume 8, Article number: 11562 | |
| dc.title (Title) | Reproducibility of structural brain connectivity and network metrics using probabilistic diffusion tractography | en_US |
| dc.type (Data Type) | article | |
| dc.identifier.doi (DOI) | 10.1038/s41598-018-29943-0 | |
| dc.doi.uri | https://doi.org/10.1038/s41598-018-29943-0 | |
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