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Integrated wavelet processing and spatial statistical testing of fMRI data

Authors
Journal
NeuroImage
1053-8119
Publisher
Elsevier
Publication Date
Volume
23
Issue
4
Identifiers
DOI: 10.1016/j.neuroimage.2004.07.056
Keywords
  • Discrete Wavelet Transform
  • Wavelet Thresholding
  • Statistical Testing
  • Threshold Selection
  • Approximation Error

Abstract

We introduce an integrated framework for detecting brain activity from fMRI data, which is based on a spatial discrete wavelet transform. Unlike the standard wavelet-based approach for fMRI analysis, we apply the suitable statistical test procedure in the spatial domain. For a desired significance level, this scheme has one remaining degree of freedom, characterizing the wavelet processing, which is optimized according to the principle of minimal approximation error. This allows us to determine the threshold values in a way that does not depend on data. While developing our framework, we make only conservative assumptions. Consequently, the detection of activation is based on strong evidence. We have implemented this framework as a toolbox (WSPM) for the SPM2 software, taking advantage of multiple options and functions of SPM such as the setup of the linear model and the use of the hemodynamic response function. We show by experimental results that our method is able to detect activation patterns; the results are comparable to those obtained by SPM even though statistical assumptions are more conservative.

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