statistical analysis of pet data using fmristat (!)
DESCRIPTION
Statistical analysis of PET data using FMRISTAT (!). Keith Worsley Department of Mathematics and Statistics, McConnell Brain Imaging Centre, McGill University. c:/keith/fMRI/manou/cbf non kin/bach allan -h1 tal 200008161003.mnc, slice 21. 4. x 10. 4. 1. 6. 11. 16. 21. 25. 30. 35. 40. - PowerPoint PPT PresentationTRANSCRIPT
Statistical analysis of PET data using FMRISTAT (!)
Keith Worsley
Department of Mathematics and Statistics, McConnell Brain
Imaging Centre, McGill University
CBF non kinetic
Unnormalized data (z = -6 mm)
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CBF non-kinetic
Normalized: thresh at ½ max, average, divide
base
Correlation models
Independent scans
AutocorrelationAR(1)
Allcorrelations
AutocorrelationAR(2)
DF: (#subj-1) ×(#scans-1) = 51
Depends on correlations, contrast (#subj-1) = 13
Standard error of contrasts:
bias varianceSafest: DOT, FMRISTAT
boost df by pooling/smoothingSPM?
Is pooling sd valid?Is sd constant across the brain?Unsmoothed sd assuming independent scans, 51 df:
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dfeff = dfresidual(2 + 1)
FMRISTAT: smoothing instead of poolingEffective df depends on FWHMsd:
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e.g. FWHMdata = 8.3 mm:
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no smoothing, dfeff = 51dfeff = 13
pooled sd, dfeff = infinityTarget = 100 df
FWHM = 4.4 mm
FWHM = 10.0 mm
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FMRISTAT: smooth sd by FWHM = 4.4 mm, df = 100
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Effect is always same
T statisticSD: Voxel Smooth Pooled DF: 51 100 infinite
Stat_summarytask 4 – baseCLUSTERS:clus vol resel Pval (one) 1 4688 10.92 0 ( 0) 2 7067 10.58 0 ( 0) 3 947 1.6 0.033 (0.001) 4 543 0.88 0.22 ( 0.01) 5 415 0.75 0.323 (0.015) 6 169 0.39 0.788 ( 0.06)
PEAKS:clus peak Pval (one) Qval (i j k) ( x y z ) 2 8.03 0 ( 0) 0 ( 35 62 64) (-38.9 -19.4 58.5) 1 6.8 0 ( 0) 0 ( 81 40 13) ( 22.8 -57.3 -18) 1 6.58 0 ( 0) 0 ( 76 43 11) ( 16.1 -52.1 -21) 2 6.34 0 ( 0) 0 ( 39 59 67) (-33.5 -24.6 63) 2 6.33 0 ( 0) 0 ( 42 64 70) (-29.5 -16 67.5) 1 5.41 0.019 (0.001) 0 ( 70 42 12) ( 8 -53.8 -19.5) 1 5.4 0.02 (0.001) 0 ( 68 41 13) ( 5.4 -55.6 -18) 1 5.38 0.021 (0.001) 0 ( 69 42 13) ( 6.7 -53.8 -18) 2 5.25 0.037 (0.002) 0 ( 30 57 63) (-45.6 -28 57) 3 5.08 0.076 (0.004) 0.001 ( 17 76 42) ( -63 4.6 25.5) 2 5 0.104 (0.005) 0.001 ( 31 59 64) (-44.2 -24.6 58.5) 9 4.9 0.155 (0.007) 0.001 ( 14 67 12) ( -67 -10.8 -19.5) 3 4.85 0.191 (0.008) 0.001 ( 16 75 42) (-64.3 2.9 25.5) 5 4.81 0.223 ( 0.01) 0.001 ( 65 77 61) ( 1.3 6.4 54) 0
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Effective FWHM: 15
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CBF kinetic
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Is pooling sd valid?Is sd constant across the brain?Unsmoothed sd assuming independent scans, 51 df:
PooledSd =7.2
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CBF kinetic (z = 57 mm)
Effect is always same
T statisticSD: Voxel Smooth Pooled DF: 51 100 infinite
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T stat, smoothed sd, 100 dfCBF non kinetic vs. kinetic
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Stat_summarytask 4 – base
CLUSTERS:clus vol resel Pval (one) 1 1338 3.11 0 (0.002) 2 363 1 0.008 (0.043)
PEAKS:clus peak Pval (one) Qval (i j k) ( x y z ) 1 6.24 0 ( 0) 0 (33 63 61) (-41.5 -17.7 54) 1 5.32 0 ( 0) 0 (32 62 63) (-42.9 -19.4 57) 1 5.14 0 (0.001) 0 (35 63 60) (-38.9 -17.7 52.5) 2 4.82 0.001 (0.003) 0 (79 43 11) ( 20.1 -52.1 -21) 2 4.48 0.003 (0.009) 0 (78 44 12) ( 18.8 -50.4 -19.5) 2 4.32 0.005 (0.015) 0 (81 42 11) ( 22.8 -53.8 -21) 1 4.3 0.005 (0.015) 0 (41 62 70) (-30.8 -19.4 67.5) 1 4.19 0.007 (0.021) 0 (40 61 69) (-32.2 -21.2 66) 1 4.16 0.007 (0.023) 0 (32 60 66) (-42.9 -22.9 61.5) 6 3.91 0.016 (0.049) 0.001 (31 59 64) (-44.2 -24.6 58.5) 2 3.64 0.034 (0.099) 0.001 (81 41 10) ( 22.8 -55.6 -22.5) 2 3.52 0.047 (0.135) 0.002 (82 41 11) ( 24.1 -55.6 -21) 1 3.5 0.05 (0.143) 0.002 (39 58 69) (-33.5 -26.3 66) 3 3.42 0.062 (0.175) 0.002 (30 58 64) (-45.6 -26.3 58.5) 1 3.33 0.077 (0.214) 0.002 (38 65 68) (-34.8 -14.3 64.5) 2 3.16 0.116 (0.313) 0.003 (82 38 10) ( 24.1 -60.7 -22.5)
Effective FWHM:15
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(search region is whereCBF non kinetic T > 5)
CMRO kinetic
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CMRO kinetic
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Is pooling sd valid?Is sd constant across the brain?Unsmoothed sd assuming independent scans, 48 df:
PooledSd =12.7
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CMRO kinetic T statistic, z = 57 mm
SD: Voxel Smooth Pooled DF: 48 100 infinite
T statistic, z = -6 mmSD: Voxel Smooth Pooled DF: 48 100 infinite
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CMRO kinetic smoothed 16 mm,“safe” analysis of task 3 - base
Unsmoothedsd, 12 df
Sd smoothed26 mm, 100 df
Unsmoothedsd, 12 dfT = 6.79
Sd smoothed26 mm, 100 df
T = 4.14
Pooled sd,infinite dfT = 3.93
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Stat_summarytask 3 – base
CLUSTERS:clus vol resel Pval (one) 1 927 0.25 0.179 (0.121) 2 211 0.06 0.485 (0.409) 3 138 0.05 0.545 (0.485) 4 266 0.04 0.554 (0.498)
PEAKS:clus peak Pval (one) Qval (i j k) ( x y z ) 1 4.42 0.065 (0.034) 0.651 (37 107 40) (-36.2 58 22.5) 1 4.41 0.068 (0.036) 0.651 (35 106 39) (-38.9 56.2 21) 1 4.41 0.068 (0.036) 0.651 (36 106 40) (-37.5 56.2 22.5) 1 4.37 0.077 (0.041) 0.651 (37 106 42) (-36.2 56.2 25.5) 1 4.32 0.089 (0.047) 0.651 (36 107 38) (-37.5 58 19.5)... 4 3.44 1.072 ( 0.54) 1.003 (83 75 21) ( 25.5 2.9 -6) 4 3.44 1.091 (0.549) 1.003 (82 75 22) ( 24.1 2.9 -4.5)
Effective FWHM:40
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