Some Issues in the Analysis of Time Course Data in Neuroimaging

Abstract

Neuroimaging is a rapidly growing area of research. The ability to measure the function of the brain “in-vivo” provides both neuroscience and psychology with tools for investigating hypotheses in a quantifiable manner that has not previously been available. However, these methods have also raised new questions for the analysis of the data. The data is rich in four dimensions and, therefore, spatio-temporal statistical techniques are required. In this talk, we will explore methods for examining time courses in brain image analysis that borrow spatial information to improve estimation. Particular emphasis will be placed on non-parametric techniques such as wavelets and functional principal component analysis. It will be shown that by using these methods, estimation of temporal parameters from traditional parametric models can be dramatically improved in terms of mean squared error, allowing more sensitive analyses to be undertaken.