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Activity Number: 226
Type: Topic Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #312520
Title: Spatial-Temporal Functional Principal Component Analysis and Its Application on fMRI
Author(s): Lei Huang*+ and Philip T. Reiss and Luo Xiao and Martin Lindquist and Ciprian Crainiceanu
Companies: Johns Hopkins University and New York University School of Medicine and Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University
Keywords: FPCA ; spatial-temporal structure ; fMRI
Abstract:

Functional principal component analysis (FPCA) is a basic tool for dimension reduction in functional data analysis. Many examples of massive data, however, have additional structure that is ignored by standard approaches to FPCA, such as the spatio-temporal structure of functional MRI (fMRI) data. We propose generalized models of FPCA that are appropriate for massive data sets with known two-way dependencies. We discuss identifiability conditions and develop estimation procedures for each model. The methodology is motivated by, and is applied to, an fMRI study designed to analyze the relationship between pain and brain activity.


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