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Inference by permutation of multi-subject neuroimaging studies

M Brammer E Bullmore J Fadili C Long. V Maxim C Ooi L Sendur D Welchew. Inference by permutation of multi-subject neuroimaging studies. John Suckling Brain Mapping Unit Department of Psychiatry University of Cambridge

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Inference by permutation of multi-subject neuroimaging studies

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  1. M Brammer E Bullmore J Fadili C Long V Maxim C Ooi L Sendur D Welchew Inference by permutation of multi-subject neuroimaging studies John Suckling Brain Mapping Unit Department of Psychiatry University of Cambridge Acknowledgements: Funded by the Human Brain Project/Neuroinformatics, National Institute of Biomedical Imaging and Bioengineering and the National Instititute of Mental Health. Experimental work supported by GlaxoSmithKline plc. Data collection at the MRI Unit, Maudsley Hospital, London (1.5T) and Wolfson Brain Imaging Centre, Cambridge (3T).

  2. Overview • Part I: Preliminaries • Software overview • fMRI processing pipeline • Part II: multi-subject experiments • Within-group activation mapping • Categorically designed experiments • Factorally designed experiments

  3. Statistical inference group mapping categorical design factorial design Brain Activation and Morphological Mapping (BAMM) fMRI analysis Standard space mapping Structural analysis Other analysis (SPM, DTI, MT…) http://www-bmu.psychiatry.cam.ac.uk/software/

  4. parenchymal masking geometric motion correction temporal motion correction global trend removal fMRI processing Threshold from histogram Rigid body mapping onto mean time-series image fMRI analysis Regress quadratic function of displacements and lag=1 onto time-series Mean zero and linear trend removal

  5. fMRI processing Estimation via the general linear model (with auto-regressive pre-whitening) response estimation Y=bX+e R Surrogate time-series with comparable auto-covariance permutation in wavelet domain Affine transformation of observed and surrogate responses standard space mapping between- and within- group inference

  6. fMRI processing Processing controlled by scripts and parameter files. Studywide are set via options in the control script (fbamm.csh): # User configurable optionssetenv DUMMIES 0 # No. of dummy scanssetenv SMTHKERNEL 0.0 # Amount of spatial pre-smoothingsetenv PHASE off # Phase: on/offsetenv UNSTD off # Test statistic standardised: on/off setenv NRANDOM 10 # Number of randomisations … and individual parameter files: /home/user/study/images/subject1/AB012345.12jun04 # subject ID /home/user/study/designmatrix # paradigm design 1 # cluster level E(FP)

  7. Individual & group mapping run from a command script: #!/bin/csh fbamm.csh < /home/user/study/subject1.param fbamm.csh < /home/user/study/subject2.param fbamm.csh < /home/user/study/subject3.param fbamm.csh < /home/user/study/subject7.param fbamm.csh < /home/user/study/subject10.param fbamm.csh < /home/user/study/subject23.param … gbamm.csh < gbamm.param Group map parameter files: /home/user/study/subject.list # list of subjects /app/BAMM/templates/MNI/EPI # template /home/user/study/groupmap # output directory 1 # cluster level E(FP) fMRI processing

  8. Inference by permutation • Parametric • Random sampling: from the population • Random assignment: to treatments • Homogeneity of variance (sphericity) • Permutation • Follows from random assignment (Fisher, 1929): Identify the independent (exchangable) quantity, such that its reordering has no effect on the distribution of test statistic under H0

  9. d1 d2 d3 d4 d5 d6 s6 decorrelated coefficients 1/f–like noise Within-group activation mapping original DWT permuted iDWT Colored Noise and Computational Inference in Neurophysiological (fMRI) Time Series Analysis: Resampling Methods in Time and Wavelet Domain. E Bullmore et al. 12: 61-78,

  10. observed X R CV- CV+ -b +b Bullmore E et al (2003) Practice and difficulty evoke anatomically and pharmacologically dissociable brain activation dynamics. Cerebral Cortex 13: 144-154. Within-group activation mapping median observed & permuted responses aggregate permuted responses Obtain cluster CVs controlling FWER

  11. Activation is both focal and diffuse p<0.005 b/SE(b) p<0.05 Cluster statistics • Procedure • Threshold voxel F (or t ) map @ p<0.05 • Aggregate contiguous voxels into 3D clusters • Calculate sum of supra-threshold F for each cluster • Repeat for permuted F maps • Obtain CV and threshold observed clusters

  12. Cluster statistics Null experiment: Estimated type I errors 1.5T 3T Obs FP Obs FP Exp FP Exp FP

  13. Yi = a0 + a1G + … +anXn Yi - observation at voxel i G- independent variable Xn - confounds a1/SE(a1) - test statistic Observed value cases controls Neural response to pleasant stimuli in anhedonia: Mitterschiffthaler et al Neuroreport 12: 177-182 Between-subject categorically designed experiments DATA Test statistic: slope of linear model Permute observations Obtain cluster CVs controlling FWER

  14. Observed value Continuous measure Between-subject categorically designed experiments Attenuation of the Neural Response to Sad Faces in Major Depression by Antidepressant Treatment Fu et al. Archives of General Psychiatry (in press)

  15. Between-subject categorically designed experiments Null experiment: Estimated type I errors voxel cluster area cluster mass Global, Voxel, and Cluster Tests, by Theory and Permutation, for a Difference Between Two Groups of Structural MR Images of the Brain. E T Bullmore et al IEEE Trans Med Imag 18: 32-42

  16. main effect A Factor A Level 1 Level 2 Level 3 Level 1 b*11n b*21n b*31n Factor B A1 A1 A1 A2 A2 A2 A3 A3 A3 exact tests: N =p.V main effect B b*12n b*22n b*32n Level 2 interaction B1 B1 B1 n=1…N for a balanced design B2 B2 B2 Rijn = bijn- bi.. - b.j. - b… approx test: NI p.V Attenuation of the Neural Response to Sad Faces in Major Depression by Antidepressant Treatment Fu et al. Archives of General Psychiatry (in press) Between-subject factorially designed experiments DATA Calculate F: main effects and interaction Permute observations Obtain cluster CVs controlling FWER

  17. parametric permutation cluster g 1.0 0.0 Between-subject factorially designed experiments Simulation effect (g.SNR2.5db ), smoothed Gaussian noise Independent or repeated measures J Suckling and E Bullmore. Permutation Tests for Factorially Designed Neuroimaging Experiments. HBM 22: 193-205

  18. /home/user/study/subject1/prefix 1 1 /home/user/study/subject2/prefix 1 1 /home/user/study/subject3/prefix 1 1 … /home/user/study/subject7/prefix 1 2 /home/user/study/subject10/prefix 1 2 /home/user/study/subject23/prefix 1 2 … /home/user/study/subject45/prefix 2 1 /home/user/study/subject46/prefix 2 2 /home/user/study/subject48/prefix 2 2 usage: exbamm [-i|r|m] -d FILE -t FILE -o DIR –p VALUE -i|r|m independent, mixed or repeated observations -p eppi/ecpi (default=1) -d design matrix filename -t template image filename -o output directory Balanced design Processing

  19. Future • GUI improvements for modular program linking • BLU estimation of response in wavelet domain • Permutation testing of spectral measures • Inference of spatial statistics in wavelet domain

  20. End

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