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Literature DB >> 21798354

Functional principal component model for high-dimensional brain imaging.

Vadim Zipunnikov¹, Brian Caffo, David M Yousem, Christos Davatzikos, Brian S Schwartz, Ciprian Crainiceanu.

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.

Entities: Disease Species

Mesh：

Year: 2011 PMID： 21798354 PMCID： PMC3169674 DOI： 10.1016/j.neuroimage.2011.05.085

Source DB: PubMed Journal: Neuroimage ISSN： 1053-8119 Impact factor: 6.556

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8. Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk.

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13 in total

1. Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis.

Authors: Vadim Zipunnikov; Sonja Greven; Haochang Shou; Brian Caffo; Daniel S Reich; Ciprian Crainiceanu
Journal: Ann Appl Stat Date: 2014 Impact factor: 2.083

Functional principal component model for high-dimensional brain imaging.

1. Functional mixed effects models.

2. Tensorial extensions of independent component analysis for multisubject FMRI analysis.

3. CLASSIC: consistent longitudinal alignment and segmentation for serial image computing.

4. Functional generalized linear models with images as predictors.

5. Fast methods for spatially correlated multilevel functional data.

6. An image-processing system for qualitative and quantitative volumetric analysis of brain images.

7. Population Value Decomposition, a Framework for the Analysis of Image Populations.

8. Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk.

9. Postmenopausal hormone therapy and regional brain volumes: the WHIMS-MRI Study.

10. Associations of tibial lead levels with BsmI polymorphisms in the vitamin D receptor in former organolead manufacturing workers.

1. Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis.

2. Statistical modeling of time-dependent fMRI activation effects.

3. FGWAS: Functional genome wide association analysis.

4. Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities.

5. Multilevel Functional Principal Component Analysis for High-Dimensional Data.

6. Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.

7. Fast, Exact Bootstrap Principal Component Analysis for p > 1 million.

8. Structured functional additive regression in reproducing kernel Hilbert spaces.

9. Addressing Confounding in Predictive Models with an Application to Neuroimaging.

10. Principal component analysis of hybrid functional and vector data.