000063121 001__ 63121
000063121 005__ 20190213064558.0
000063121 0247_ $$2doi$$a10.1016/j.neuroimage.2004.07.056
000063121 02470 $$2DAR$$a6304
000063121 02470 $$2ISI$$a000226041800023
000063121 037__ $$aARTICLE
000063121 245__ $$aIntegrated Wavelet Processing and Spatial Statistical Testing of fMRI Data
000063121 269__ $$a2004
000063121 260__ $$bElsevier$$c2004
000063121 336__ $$aJournal Articles
000063121 520__ $$9eng$$a 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.
000063121 6531_ $$afMRI Activity
000063121 700__ $$0240173$$aVan De Ville, D.$$g152027
000063121 700__ $$0240171$$aBlu, T.$$g115589
000063121 700__ $$0240182$$aUnser, M.$$g115227
000063121 773__ $$j23$$k4$$q1472–1485$$tNeuroImage
000063121 8564_ $$uhttp://bigwww.epfl.ch/publications/vandeville0406.ps$$zURL
000063121 8564_ $$uhttp://bigwww.epfl.ch/publications/vandeville0406.html$$zURL
000063121 8564_ $$s790423$$uhttps://infoscience.epfl.ch/record/63121/files/vandeville0406.pdf$$zn/a
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000063121 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000063121 980__ $$aARTICLE