Name
Abstract | ||
|---|---|---|
We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a FPCA model is fit to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neuroimage.2011.05.085 | NeuroImage |
Keywords | Field | DocType |
Voxel-based morphometry (VBM),MRI,FPCA,SVD,Brain imaging data | Functional principal component analysis,Brain mapping,Singular value decomposition,Artificial intelligence,Neuroimaging,Principal component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
58 | 3 | 1053-8119 |
Citations | PageRank | References |
3 | 0.40 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vadim Zipunnikov | 1 | 7 | 1.84 |
| Brian Caffo | 2 | 92 | 9.44 |
| David M Yousem | 3 | 7 | 2.36 |
| Christos Davatzikos | 4 | 3865 | 335.91 |
| Brian S Schwartz | 5 | 6 | 1.77 |
| Ciprian M Crainiceanu | 6 | 61 | 10.28 |