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Cited by 46Functional Brain Connectivity StudiesImage and Signal Denoising MethodsAdvanced Neuroimaging Techniques and Applications

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WSPM: Wavelet-based statistical parametric mapping

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Van De Ville, Dimitri, Seghier, Mohamed L., Lazeyras, François, Blu, Thierry, Unser, Michael·2007·NeuroImage
DOI 10.1016/j.neuroimage.2007.06.011
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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.

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Metadata

Authors
Van De Ville, D., Seghier, M. L., Lazeyras, F., Blu, T., & Unser, M.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • University Hospital of Geneva (CH)
  • École Polytechnique Fédérale de Lausanne (CH)
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2007-10
Volume
37
Issue
4
Pages
1205-1217
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2007.06.011
OpenAlex
W2168015895
Cited by
46
References
2
Citations by year
2023: 1 · 2022: 1 · 2020: 2 · 2018: 3 · 2016: 4 · 2015: 4 · 2014: 4 · 2013: 5 · 2012: 2
FWCI
2.33
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • UCL Discovery (University College London) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
Funders
  • National Science Foundation (200020)
  • École Polytechnique Fédérale de Lausanne
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (101821, 200020)
  • Centre d'Imagerie BioMédicale
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
wavelet denoising, translation invariance, statistical detection
MeSH terms
Acoustic Stimulation, Algorithms, Brain Mapping, Computer Simulation, Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, ROC Curve, Reproducibility of Results, Linear Models, Nonlinear Dynamics

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