Multivariate Classification of Complex and Multi-echo fMRI Data

Abstract

Multivariate pattern classification and prediction offers an alternative to standard univariate analysis techniques, and has recently been applied in MR imaging using support vector machines (SVM), and used to attain real-time feedback. The standard approach has been to use reconstructed image magnitude data. However, information is also present in the image phase data, and in the k-space data itself. Further, multi-echo imaging offers possibilities of increased functional sensitivity and quantitative imaging. In this study, we explore applying SVM techniques to complex and multi-echo fMRI data, using both phase information and earlier echo-times for prediction.