WSPM OR HOW TO OBTAIN STATISTICAL PARAMETRIC MAPS USING SHIFT-INVARIANTWAVELET PROCESSING

Recently, we have proposed a new framework for detecting brain activity from fMRI data, which is based on the spatial discrete wavelet transform. The standard wavelet-based ap-proach performs a statistical test in the wavelet domain, and therefore fails to provide a rigorous statistical interpretation in the spatial domain. The new framework provides an “inte-grated ” approach: the data is processed in the wavelet domain (by thresholding wavelet coefcients), and a suitable statisti-cal testing procedure is applied afterwards in the spatial do-main. This method is based on conservative assumptions only and has a strong type-I error control by construction. At the same time, it has a sensitivity comparable to that of SPM. Here, we discuss the extension of our algorithm to the re-dundant discrete wavelet transform, which provides a shift-invariant detection scheme. The key features of our technique are illustrated with experimental results. An implementation of our framework is available as a toolbox (WSPM) for the SPM2 software. 1