Population Functional Data Analysis of Group ICA-Based Connectivity Measures from fMRI

• Activity Number: 452
• Type: Topic Contributed
• Date/Time: Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Shanshan Li*+ and Brian Caffo and Suresh Joel and Stewart Mostofsky and Jim Pekar and Susan Bassett
• Companies: The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University
• Keywords: Two-Stage Decomposition ; ICA ; PCA ; fMRI

Abstract

In this manuscript, we use a two-stage decomposition for the analysis of functional magnetic resonance imaging (fMRI). In the first stage, spatial independent component analysis is applied to the group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using conditional logistic regression for matched pairs data. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and the major direction variation of the mixing matrices. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity in clinically asymptomatic at risk subjects when compared to controls.