A robust independent component analysis (ICA) model for functional magnetic resonance imaging (fMRI) data

Abstract

The coupling of carefully designed experiments with proper analysis of functional magnetic resonance imaging (fMRI) data provides us with a powerful as well as noninvasive tool to help us understand cognitive processes associated with specific brain regions and hence could be used to detect abnormalities induced by a diseased state. The hypothesisdriven General Linear Model (GLM) and the data-driven Independent Component Analysis (ICA) model are the two most commonly used models for fMRI data analysis. A hybrid ICA-GLM model combines the two models to take advantages of benefits from both models to achieve more accurate mapping of the stimulus-induced activated brain regions. We propose a modified hybrid ICA-GLM model with probabilistic ICA that includes a noise model. In this modified hybrid model, a probabilistic principle component analysis (PPCA)-based component number estimation is used in the ICA stage to extract the intrinsic number of original time courses. In addition, frequency matching is introduced into the time course selection stage, along with temporal correlation, F-test based model fitting estimation, and time course combination, to produce a more accurate design matrix for GLM. A standard fMRI dataset is used to compare the results of applying GLM and the proposed hybrid ICA-GLM in generating activation maps.