WSPM: A new approach for wavelet-based statistical analysis of fMRI data
Dimitri Van De Ville, BIG, EPFL

Test Run • 09 June 2005 • BM 4.235

Abstract
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 approach 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 “integrated” approach: the data is processed in the wavelet domain (e.g., by thresholding wavelet coefficients), and a suitable statistical testing procedure is applied afterwards in the spatial domain. 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 focus on the central paradigm of our framework, which separates approximation (obtained by processing the wavelet coefficients) and statistical testing. Interestingly, such a decoupling offers high flexibility on the type of processing that can be done in the wavelet domain. For example, we discuss the use of a redundant 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 will be available as a toolbox (WSPM) for the SPM2 software