Stat4Ci A sensitive statistical test for smooth classification images
Z=1.64, p=.05 Z=2.35, p=.01 Test Z
Pour des images ?
Gaussian Random field
Seuil non corrigé
Bonferroni Correction
Exemples Bonf t = Bonf t = RFT t = 4.06
Pixel test
Résumé Seulement 2 paramètres –FWHM = taille du filtre de lissage –p = seuil de confiance Comment choisir FWHM ? –Pour détecter un signal donné, le meilleur filtre est un filtre de taille comparable Problèmes –Si le signal est diffus, le pic est faible Solution –Prendre en compte la taille et le Z score. Cluster test
t z = 2.5 k = 350 pixels
Pixel test t z = 3.30
La toolbox p=.05; tC=2.7;% threshold for 2D image (other test) FWHM=HalfMax(sigma_b); [Sci,h] = SmoothCi(Ci, sigma_b); ZSCi = ZTransCi(SCi, mean(vecCi(:)), std(vecCi(:))); [volumes,N]=CiVol(sum(mask(:)),D) [tP,k]=stat_threshold(volumes,N,FWHM,Inf,p,tC,p); tCi = DisplayCi(ZSCi,tC,k,tP,FWHM,p,RFTtest,background);
ZTransCi ZSCi = ZTransCi(SCi, mean(vecCi(:)), std(vecCi(:))); In ZtransCi(Ci1, n1, Ci2, n2, sigmaNoise, smoothFilter), –Ci1 = sum of white noise fields that led to a type 1 response (e.g., correct) –n1 = number of type 1 response –Ci2 = sum of white noise fields that led to a type 2 response (e.g., incorrect) –n2 = number of type 2 response –sigmaNoise = standard deviation of white noise –smoothFilter = Gaussian filter used to smooth the classification image
stat_threshold [tP,k]=stat_threshold(volumes,N,FWHM,Inf,p,tC,p); –t pixel –k taille minimun –volumes, num_voxels, FWHM –df : Inf –p_val_peak,... –cluster_threshold, –p_val_extent
DisplayCi tCi = DisplayCi(ZSCi,tC,k,tP,FWHM,p,RFTtest,background); tsizereselsZmaxxy C[2.70] [2.70] P3.30- p-value = 0.05 FWHM = 47.1 Minimum cluster size = 861.7
tsize resels Zmax x y C[2.70] [2.70] P3.30- p-value = [0.05] FWHM = [47.1] Minimum cluster size = t size resels Zmax x y C[2.70] P3.30- p-value = [0.05] FWHM = [47.1] Minimum cluster size = 861.7
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