Name
Abstract | ||
|---|---|---|
Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at approximate to 1 mm(3) scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel nonparametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art. |
Year | Venue | Keywords |
|---|---|---|
2013 | IEEE Global Conference on Signal and Information Processing | connectomics,magnetic resonance imaging,network theory,pipeline |
DocType | Volume | ISSN |
Journal | abs/1312.4875 | 2376-4066 |
Citations | PageRank | References |
4 | 0.51 | 9 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Gray Roncal | 1 | 38 | 8.25 |
| Zachary H. Koterba | 2 | 4 | 0.51 |
| Disa Mhembere | 3 | 63 | 5.42 |
| Dean Kleissas | 4 | 14 | 2.71 |
| Joshua T. Vogelstein | 5 | 273 | 31.99 |
| Randal Burns | 6 | 1955 | 115.15 |
| Anita R. Bowles | 7 | 4 | 0.51 |
| Dimitrios K. Donavos | 8 | 4 | 0.51 |
| Sephira Ryman | 9 | 7 | 1.11 |
| Rex E. Jung | 10 | 102 | 10.63 |
| Lei Wu | 11 | 4 | 0.51 |
| Vince D Calhoun | 12 | 2769 | 268.91 |
| R. J. Vogelstein | 13 | 269 | 24.60 |