WSPM: Wavelet-based statistical parametric mapping

Dimitri Van De Ville, Mohamed L. Seghier, François Lazeyras, Thierry Blu, Michael Unser

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)1205-1217
Number of pages13
JournalNeuroImage
Volume37
Issue number4
DOIs
StatePublished - 1 Oct 2007

Keywords

  • Bias reduction
  • Reproducibility study
  • Shift-invariant transform
  • Statistical testing
  • Wavelet thresholding
  • Wavelets

Fingerprint

Dive into the research topics of 'WSPM: Wavelet-based statistical parametric mapping'. Together they form a unique fingerprint.

Cite this