Abstract
Functional magnetic resonance imaging (fMRI) is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer's disease. In the first stage, spatial independent component analysis is applied to 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 permutation-based exact logistic regression for matched pairs data. 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 when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.
Highlights
Functional magnetic resonance imaging is a driving force in the field of brain mapping and cognitive neuroscience
Our objective is to examine whether independent component analysis (ICA)-based analysis of task-related Functional magnetic resonance imaging (fMRI) presents evidence of differences in connectivity between subjects with mildly cognitive impairment and carefully matched controls
We propose and implement a use of Functional principal component analysis (FPCA) on temporal mixing matrices within the context of exact permutation-based conditional logistic regression to analyze risk status for mild cognitive impairment (MCI) in a matched-pairs study
Summary
Functional magnetic resonance imaging (fMRI) is a driving force in the field of brain mapping and cognitive neuroscience. Our study is motivated by the fact that differences in functional connectivity have been proposed to be associated with Alzheimer’s disease [2,3,4]. In this manuscript, our objective is to examine whether independent component analysis (ICA)-based analysis of task-related fMRI presents evidence of differences in connectivity between subjects with mildly cognitive impairment and carefully matched controls. We approach our study of fMRI by simultaneously analyzing all voxels This is in contrast to regional or seed-based approaches [3,5,6] that restrict attention to carefully chosen locations. Given the volume of voxels under study, flexible yet parsimonious models are required
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