Multivariate approach to functional MRI analysis for brain functionstudy,

摘要

Abstract: Functional MRI (fMRI) is a means of analyzing localized brain activity. It is statistically modeled by the multivariate Gaussian probability distribution (in space) and the time series (in time). However, the currently used analysis method takes an univariate approach. That is, the spatial relationships among voxels are ignored. This paper presents a multivariate analysis method. It formulates fMRI activation foci detection as a sensor-array signal processing problem and converts hypotheses tests of the univariate approach to a computer vision approach. It first creates multiple independent, identical sub-images and then uses a covariance matrix to characterize the multivariate Gaussian environment. Not only it utilizes the voxel intensities but also their spatio-temporal relationships. It achieves computer speed superiority over the existing methods. Results obtained by using simulated images, phantom images, and real fMRI data are included. The theoretical and experimental results obtained by using this approach were in good agreement. !20