An exploration of graph metric reproducibility in complex brain networks
The application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain’s complex organization. More importantly, it has become a useful tool for studying the brain under vario...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-05-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00067/full |
| _version_ | 1817995371612209152 |
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| author | Qawi K Telesford Jonathan H Burdette Paul J Laurienti |
| author_facet | Qawi K Telesford Jonathan H Burdette Paul J Laurienti |
| author_sort | Qawi K Telesford |
| collection | DOAJ |
| description | The application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain’s complex organization. More importantly, it has become a useful tool for studying the brain under various states and conditions. With the ever expanding popularity of network science in the neuroimaging community, there is increasing interest to validate the measurements and calculations derived from brain networks. Underpinning these studies is the desire to use brain networks in longitudinal studies or as clinical biomarkers to understand changes in the brain. A highly reproducible tool for brain imaging could potentially prove useful as a clinical tool. In this review, we examine recent studies in network reproducibility and their implications for analysis of brain networks. |
| first_indexed | 2024-04-14T02:05:08Z |
| format | Article |
| id | doaj.art-bd89c3df7f5c4f3f964f71a4e744f176 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-14T02:05:08Z |
| publishDate | 2013-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-bd89c3df7f5c4f3f964f71a4e744f1762022-12-22T02:18:43ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2013-05-01710.3389/fnins.2013.0006732965An exploration of graph metric reproducibility in complex brain networksQawi K Telesford0Jonathan H Burdette1Paul J Laurienti2Wake Forest University School of MedicineWake Forest University School of MedicineWake Forest University School of MedicineThe application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain’s complex organization. More importantly, it has become a useful tool for studying the brain under various states and conditions. With the ever expanding popularity of network science in the neuroimaging community, there is increasing interest to validate the measurements and calculations derived from brain networks. Underpinning these studies is the desire to use brain networks in longitudinal studies or as clinical biomarkers to understand changes in the brain. A highly reproducible tool for brain imaging could potentially prove useful as a clinical tool. In this review, we examine recent studies in network reproducibility and their implications for analysis of brain networks.http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00067/fullbrain networksgraph theoryNetwork Scienceintraclass correlation coefficientreproducibility |
| spellingShingle | Qawi K Telesford Jonathan H Burdette Paul J Laurienti An exploration of graph metric reproducibility in complex brain networks Frontiers in Neuroscience brain networks graph theory Network Science intraclass correlation coefficient reproducibility |
| title | An exploration of graph metric reproducibility in complex brain networks |
| title_full | An exploration of graph metric reproducibility in complex brain networks |
| title_fullStr | An exploration of graph metric reproducibility in complex brain networks |
| title_full_unstemmed | An exploration of graph metric reproducibility in complex brain networks |
| title_short | An exploration of graph metric reproducibility in complex brain networks |
| title_sort | exploration of graph metric reproducibility in complex brain networks |
| topic | brain networks graph theory Network Science intraclass correlation coefficient reproducibility |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00067/full |
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