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WSPM: Wavelet Processing and the Analysis of fMRI Using Statistical Parametric Maps

D. Van De Ville, T. Blu, M. Unser

Invited talk, Second International Conference on Computational Harmonic Analysis, Nineteenth Annual Shanks Lecture (CHA'04), Nashville TN, USA, May 24-30, 2004.



Wavelet-based methods for the statistical analysis of functional magnetic resonance images (fMRI) are able to detect brain activity without smoothing the data (3D space + time). Up to now, the statistical inference was typically performed in the wavelet domain by testing the t-values of each wavelet coefficient; the activity map was reconstructed from the significant coefficients. The limitation of this approach is that there is no direct statistical interpretation of the reconstructed map. Here, we describe a new methodology that takes advantage of wavelet processing but keeps the statistical meaning in the spatial domain. We derive a spatial threshold with a proper non-stationary component and determine optimal threshold values by minimizing an approximation error. This framework was implemented as a toolbox (WSPM) for the widely-used SPM2 software, taking advantage of the multiple options and functionality of SPM (Statistical Parametric Mapping) such as the specification of a linear model that may account for the hemodymanic response of the system. The sensitivity of our method is comparable to that of conventional SPM, which applies a spatial Gaussian prefilter to the data, even though our statistical assumptions are more conservative.


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