Matrix Decomposition Methods for Functional MRI Data

• Activity Number: 583
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Ani Eloyan*+ and Brian Caffo and Ciprian M. Crainiceanu
• Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University and The Johns Hopkins University

Abstract

The field of functional neuroimaging is growing very rapidly resulting in a vast amount of data for analysis. Recently, several large resting state functional magnetic resonance images from different laboratories have been collected in freely available datasets for analysis including the 1000 Functional Connectomes Project Dataset, ADHD 200 among others. Statistical dimension reduction techniques such as singular value decomposition (SVD), independent component analysis (ICA), etc. are routinely used by the practitioners in the field of neuroimaging to analyze complex fMRI data. In this talk, the main dimension reduction approaches for fMRI data are discussed stressing the major issues in the applications and the advantages of the methods depending on the biological question at hand. Extensions of the methods to high dimensional data are presented where possible.