WSPM or How to Obtain Statistical Parametric Maps Using Shift-Invariant Wavelet Processing
D. Van De Ville, T. Blu, M. Unser
Proceedings of the IEEE Thirty-First International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), Toulouse, French Republic, May 14-19, 2006, pp. V-1101-V-1104.
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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AUTHOR="Van De Ville, D. and Blu, T. and Unser, M.",
TITLE="{WSPM} or How to Obtain Statistical Parametric Maps Using
Shift-Invariant Wavelet Processing",
BOOKTITLE="Proceedings of the {IEEE} Thirty-First International
Conference on Acoustics, Speech, and Signal Processing
({ICASSP'06})",
YEAR="2006",
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pages="{V}-1101--{V}-1104",
address="Toulouse, French Republic",
month="May 14-19,",
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