WSPM or How to Obtain Statistical Parametric Maps Using Shift-Invariant Wavelet 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 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 (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 discuss the extension of our algorithm to the 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 is available as a toolbox (WSPM) for the SPM2 software.

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Proceedings of the IEEE Thirty-First International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), Toulouse, French Republic, V-1101–V-1104

 Record created 2015-09-18, last modified 2018-10-07

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