Statistical Nonparametric Mapping - SnPM Thresholding without (m)any assumptions

1. Statistical Nonparametric Mapping - SnPMThresholding without (m)any assumptions Thomas Nichols, Ph.D. Director, Modelling & GeneticsGlaxoSmithKline Clinical Imaging Centre http://www.fmrib.ox.ac.uk/~nichols FIL SPM Course May 15, 2008

2. Overview • Multiple Comparisons Problem • Which of my 100,000 voxels are “active”? • SnPM • Permutation test to find threshold • Control chance of any false positives (FWER)

3. 5% Parametric Null Distribution 5% Nonparametric Null Distribution Nonparametric Inference • Parametric methods • Assume distribution ofstatistic under nullhypothesis • Needed to find P-values, u • Nonparametric methods • Use data to find distribution of statisticunder null hypothesis • Any statistic!

4. Permutation TestToy Example • Data from V1 voxel in visual stim. experiment A: Active, flashing checkerboard B: Baseline, fixation 6 blocks, ABABAB Just consider block averages... • Null hypothesis Ho • No experimental effect, A & B labels arbitrary • Statistic • Mean difference

5. Permutation TestToy Example • Under Ho • Consider all equivalent relabelings

6. Permutation TestToy Example • Under Ho • Consider all equivalent relabelings • Compute all possible statistic values

7. Permutation TestToy Example • Under Ho • Consider all equivalent relabelings • Compute all possible statistic values • Find 95%ile of permutation distribution

8. Permutation TestToy Example • Under Ho • Consider all equivalent relabelings • Compute all possible statistic values • Find 95%ile of permutation distribution

9. Permutation TestToy Example • Under Ho • Consider all equivalent relabelings • Compute all possible statistic values • Find 95%ile of permutation distribution -8 -4 0 4 8

10. Permutation TestStrengths • Requires only assumption of exchangeability • Under Ho, distribution unperturbed by permutation • Allows us to build permutation distribution • Subjects are exchangeable • Under Ho, each subject’s A/B labels can be flipped • fMRI scans not exchangeable under Ho • Due to temporal autocorrelation

11. Permutation TestLimitations • Computational Intensity • Analysis repeated for each relabeling • Not so bad on modern hardware • No analysis discussed below took more than 3 hours • Implementation Generality • Each experimental design type needs unique code to generate permutations • Not so bad for population inference with t-tests

12. MCP Solutions:Measuring False Positives • Familywise Error Rate (FWER) • Familywise Error • Existence of one or more false positives • FWER is probability of familywise error • False Discovery Rate (FDR) • R voxels declared active, V falsely so • Observed false discovery rate: V/R • FDR = E(V/R)

13. FWER MCP Solutions • Bonferroni • Maximum Distribution Methods • Random Field Theory • Permutation

14.  FWER MCP Solutions: Controlling FWER w/ Max • FWER & distribution of maximum FWER = P(FWE) = P(One or more voxels u | Ho) = P(Max voxel u | Ho) • 100(1-)%ile of max distn controls FWER FWER = P(Max voxel u | Ho)   u

15. FWER MCP Solutions • Bonferroni • Maximum Distribution Methods • Random Field Theory • Permutation

16. 5% Parametric Null Max Distribution 5% Nonparametric Null Max Distribution Controlling FWER: Permutation Test • Parametric methods • Assume distribution ofmax statistic under nullhypothesis • Nonparametric methods • Use data to find distribution of max statisticunder null hypothesis • Again, any max statistic!

17. Permutation TestOther Statistics • Collect max distribution • To find threshold that controls FWER • Consider smoothed variance t statistic • To regularize low-df variance estimate

18. mean difference Permutation TestSmoothed Variance t • Collect max distribution • To find threshold that controls FWER • Consider smoothed variance t statistic t-statistic variance

19. Permutation TestSmoothed Variance t • Collect max distribution • To find threshold that controls FWER • Consider smoothed variance t statistic SmoothedVariancet-statistic mean difference smoothedvariance

20. Active ... ... yes Baseline ... ... D UBKDA N XXXXX no Permutation TestExample • fMRI Study of Working Memory • 12 subjects, block design Marshuetz et al (2000) • Item Recognition • Active:View five letters, 2s pause, view probe letter, respond • Baseline: View XXXXX, 2s pause, view Y or N, respond • Second Level RFX • Difference image, A-B constructedfor each subject • One sample, smoothed variance t test

21. Maximum Intensity Projection Thresholded t Permutation DistributionMaximum t Permutation TestExample • Permute! • 212 = 4,096 ways to flip 12 A/B labels • For each, note maximum of t image .

22. Permutation TestExample • Compare with Bonferroni •  = 0.05/110,776 • Compare with parametric RFT • 110,776 222mm voxels • 5.15.86.9mm FWHM smoothness • 462.9 RESELs

23. 378 sig. vox. Smoothed Variance t Statistic,Nonparametric Threshold Test Level vs. t11 Threshold uRF = 9.87uBonf = 9.805 sig. vox. uPerm = 7.67 58 sig. vox. t11Statistic, Nonparametric Threshold t11Statistic, RF & Bonf. Threshold

24. Does this Generalize?RFT vs Bonf. vs Perm.

25. RFT vs Bonf. vs Perm.

26. Reliability with Small Groups • Consider n=50 group study • Event-related Odd-Ball paradigm, Kiehl, et al. • Analyze all 50 • Analyze with SPM and SnPM, find FWE thresh. • Randomly partition into 5 groups 10 • Analyze each with SPM & SnPM, find FWE thresh • Compare reliability of small groups with full • With and without variance smoothing .

27. SPM t11: 5 groups of 10 vs all 505% FWE Threshold T>10.93 T>11.04 T>11.01 10 subj 10 subj 10 subj 2 8 11 15 18 35 41 43 44 50 1 3 20 23 24 27 28 32 34 40 9 13 14 16 19 21 25 29 30 45 T>10.69 T>10.10 T>4.66 10 subj 10 subj all 50 4 5 10 22 31 33 36 39 42 47 6 7 12 17 26 37 38 46 48 49

28. SnPM t: 5 groups of 10 vs. all 505% FWE Threshold T>9.00 Arbitrary thresh of 9.0 T>7.06 T>8.28 T>6.3 10 subj 10 subj 10 subj 2 8 11 15 18 35 41 43 44 50 1 3 20 23 24 27 28 32 34 40 9 13 14 16 19 21 25 29 30 45 T>6.49 T>6.19 T>4.09 10 subj 10 subj all 50 4 5 10 22 31 33 36 39 42 47 6 7 12 17 26 37 38 46 48 49

29. SnPM SmVar t: 5 groups of 10 vs. all 505% FWE Threshold T>9.00 all 50 Arbitrary thresh of 9.0 T>4.69 T>5.04 T>4.57 10 subj 10 subj 10 subj 2 8 11 15 18 35 41 43 44 50 1 3 20 23 24 27 28 32 34 40 9 13 14 16 19 21 25 29 30 45 T>4.84 T>4.64 10 subj 10 subj 4 5 10 22 31 33 36 39 42 47 6 7 12 17 26 37 38 46 48 49

30. Conclusions • t random field results conservative for • Low df & smoothness • 9 df & 12 voxel FWHM; 19 df & < 10 voxel FWHM(based on Monte Carlo simulations, not shown) • Bonferroni not so bad for low smoothness • Nonparametric methods perform well overall

31. Monte Carlo Evaluations • What’s going wrong? • Normality assumptions? • Smoothness assumptions? • Use Monte Carlo Simulations • Normality strictly true • Compare over range of smoothness, df • Previous work • Gaussian (Z) image results well-validated • t image results hardly validated at all!

32. Monte Carlo EvaluationsChallenges • Accurately simulating t images • Cannot directly simulate smooth t images • Need to simulate  smooth Gaussian images ( = degrees of freedom) • Accounting for all sources of variability • Most M.C. evaluations use known smoothness • Smoothness not known • We estimated it residual images

33. Autocorrelation Function Monte Carlo Evaluations • Simulated One Sample T test • 32x32x32 Images (32767 voxels) • Smoothness: 0, 1.5, 3, 6,12 FWHM • Degrees of Freedom: 9, 19, 29 • Realizations: 3000 • Permutation • 100 relabelings • Threshold: 95%ile of permutation distn of maximum • Random Field • Threshold: { u : E(u| Ho) = 0.05 } • Also Gaussian FWHM

34. Inf. df FamilywiseErrorThresholds • RFT valid but conservative • Gaussian not so bad (FWHM >3) • t29 somewhat worse 29df more

35. Inf df FamilywiseRejectionRates • Need > 6 voxel FWHM 29 df more

36. 19 df FamilywiseErrorThresholds • RF & Perm adapt to smoothness • Perm & Truth close • Bonferroni close to truth for low smoothness 9 df more

37. 19 df FamilywiseRejectionRates • Bonf good on low df, smoothness • Bonf bad for high smoothness • RF only good for high df, high smoothness • Perm exact 9 df more

38. 19 df FamilywiseRejectionRates • Smoothness estimation is not (sole) problem 9 df cont

39. Performance Summary • Bonferroni • Not adaptive to smoothness • Not so conservative for low smoothness • Random Field • Adaptive • Conservative for low smoothness & df • Permutation • Adaptive (Exact)

40. Understanding Performance Differences • RFT Troubles • Multivariate Normality assumption • True by simulation • Smoothness estimation • Not much impact • Smoothness • You need lots, more at low df • High threshold assumption • Doesn’t improve for 0 less than 0.05 (not shown) HighThr

41. Conclusions • t random field results conservative for • Low df & smoothness • 9 df & 12 voxel FWHM; 19 df & < 10 voxel FWHM • Bonferroni surprisingly satisfactory for low smoothness • Nonparametric methods perform well overall • More data and simulations needed • Need guidelines as to when RF is useful • Better understand what assumption/approximation fails

42. References • TE Nichols and AP Holmes.Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. Human Brain Mapping, 15:1-25, 2002. • http://www.sph.umich.edu/~nichols Data ThrRslt MC ThrRslt MC P Rslt EstSmCf

44. Permutation TestExample • Permute! • 212 = 4,096 ways to flip A/B labels • For each, note max of smoothed variance t image . Permutation DistributionMax Smoothed Variance t Maximum Intensity Projection Threshold Sm. Var. t

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