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Stat4Ci

Stat4Ci. A sensitive statistical test for smooth classification images. Test Z. Z=2.35, p=.01. Z=1.64, p =.05. Pour des images ?. Gaussian Random field. Seuil non corrigé. Bonferroni Correction. Exemples. Bonf t = 3.5463. Bonf t = 3.5463 RFT t = 4.06. Bonf t = 4.5228. Pixel test.

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Stat4Ci

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  1. Stat4Ci A sensitive statistical test for smooth classification images

  2. Test Z Z=2.35, p=.01 Z=1.64, p=.05

  3. Pour des images ?

  4. Gaussian Random field

  5. Seuil non corrigé

  6. Bonferroni Correction

  7. Exemples Bonf t = 3.5463 Bonf t = 3.5463 RFT t = 4.06 Bonf t = 4.5228

  8. Pixel test

  9. 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 Résumé

  10. Cluster test tz = 2.5 k = 350 pixels

  11. Pixel test tz = 3.30

  12. 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);

  13. 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

  14. 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

  15. DisplayCi • tCi = DisplayCi(ZSCi,tC,k,tP,FWHM,p,RFTtest,background); t size resels Zmax x y ----------------------------------------- C [2.70] 970 0.44 4.17 122 129 [2.70] 917 0.41 3.95 162 129 ----------------------------------------- P 3.30 - p-value = 0.05 FWHM = 47.1 Minimum cluster size = 861.7

  16. t size resels Zmax x y ----------------------------------------- C [2.70] 970 0.44 4.17 122 129 [2.70] 917 0.41 3.95 162 129 ----------------------------------------- P 3.30 - p-value = [0.05] FWHM = [47.1] Minimum cluster size = 861.7 t size resels Zmax x y ----------------------------------------- C [2.70] 1787 0.81 5.2133 208 ----------------------------------------- P 3.30 - p-value = [0.05] FWHM = [47.1] Minimum cluster size = 861.7

  17. Visage à l’endroit Visage à l’envers

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