TY - JOUR
T1 - WSPM
T2 - Wavelet-based statistical parametric mapping
AU - Van De Ville, Dimitri
AU - Seghier, Mohamed L.
AU - Lazeyras, François
AU - Blu, Thierry
AU - Unser, Michael
PY - 2007/10/1
Y1 - 2007/10/1
N2 - Recently, we have introduced an integrated framework that combines wavelet-based processing with statistical testing in the spatial domain. In this paper, we propose two important enhancements of the framework. First, we revisit the underlying paradigm; i.e., that the effect of the wavelet processing can be considered as an adaptive denoising step to "improve" the parameter map, followed by a statistical detection procedure that takes into account the non-linear processing of the data. With an appropriate modification of the framework, we show that it is possible to reduce the spatial bias of the method with respect to the best linear estimate, providing conservative results that are closer to the original data. Second, we propose an extension of our earlier technique that compensates for the lack of shift-invariance of the wavelet transform. We demonstrate experimentally that both enhancements have a positive effect on performance. In particular, we present a reproducibility study for multi-session data that compares WSPM against SPM with different amounts of smoothing. The full approach is available as a toolbox, named WSPM, for the SPM2 software; it takes advantage of multiple options and features of SPM such as the general linear model.
AB - Recently, we have introduced an integrated framework that combines wavelet-based processing with statistical testing in the spatial domain. In this paper, we propose two important enhancements of the framework. First, we revisit the underlying paradigm; i.e., that the effect of the wavelet processing can be considered as an adaptive denoising step to "improve" the parameter map, followed by a statistical detection procedure that takes into account the non-linear processing of the data. With an appropriate modification of the framework, we show that it is possible to reduce the spatial bias of the method with respect to the best linear estimate, providing conservative results that are closer to the original data. Second, we propose an extension of our earlier technique that compensates for the lack of shift-invariance of the wavelet transform. We demonstrate experimentally that both enhancements have a positive effect on performance. In particular, we present a reproducibility study for multi-session data that compares WSPM against SPM with different amounts of smoothing. The full approach is available as a toolbox, named WSPM, for the SPM2 software; it takes advantage of multiple options and features of SPM such as the general linear model.
KW - Bias reduction
KW - Reproducibility study
KW - Shift-invariant transform
KW - Statistical testing
KW - Wavelet thresholding
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=34548546672&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2007.06.011
DO - 10.1016/j.neuroimage.2007.06.011
M3 - Article
C2 - 17689101
AN - SCOPUS:34548546672
SN - 1053-8119
VL - 37
SP - 1205
EP - 1217
JO - NeuroImage
JF - NeuroImage
IS - 4
ER -