Consistent Estimation of Dimensionality for Data-Driven Methods in fMRI Analysis

Abstract

Data-driven methods, such as principal component analysis and independentcomponent analysis, have been successfully applied to functionalmagnetic resonance imaging (fMRI) data in particular and neuro-imaging data in general. A central issue of thesemethods is the importance of correctly selecting the number of components to be used in the factor model. This issue is often addressed using a model selection criterion, where the goodness-of-fit term is obtained from the log-likelihood function. In this paper, an alternative criterion is proposed for selecting the number of components. Unlike existingmodel selection criteria that use the log-likelihood function, the proposed goodness-of-fit term..