Mixed effects and Group Modeling for fMRI data Thomas Nichols, Ph.D. Department of Statistics Warwick Manufacturing Group University of Warwick Zurich SPM Course February 18, 2010

Outline Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2,5,8) SPM8 Nonsphericity Modelling Data exploration Conclusions

Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2) SPM8 Nonsphericity Modelling Data exploration Conclusions

Lexicon Hierarchical Models Mixed Effects Models Random Effects (RFX) Models Components of Variance ... all the same ... all alluding to multiple sources of variation (in contrast to fixed effects)

Fixed vs. Random Effects in fMRI Distribution of each subject’s estimated effect 2FFX Subj. 1 Subj. 2 Fixed Effects Intra-subject variation suggests all these subjects different from zero Random Effects Intersubject variation suggests population not very different from zero Subj. 3 Subj. 4 Subj. 5 Subj. 6 2RFX Distribution of population effect

Fixed Effects Only variation (over sessions) is measurement error True Response magnitude is fixed

Random/Mixed Effects Two sources of variation Measurement error Response magnitude Response magnitude is random Each subject/session has random magnitude

Random/Mixed Effects Two sources of variation Measurement error Response magnitude Response magnitude is random Each subject/session has random magnitude But note, population mean magnitude is fixed

Fixed vs. Random Fixed isn’t “wrong,” just usually isn’t of interest Fixed Effects Inference “I can see this effect in this cohort” Random Effects Inference “If I were to sample a new cohort from the population I would get the same result”

Two Different Fixed Effects Approaches Grand GLM approach Model all subjects at once Good: Mondo DF Good: Can simplify modeling Bad: Assumes common variance over subjects at each voxel Bad: Huge amount of data

Two Different Fixed Effects Approaches Meta Analysis approach Model each subject individually Combine set of T statistics mean(T)n ~ N(0,1) sum(-logP) ~ 2n Good: Doesn’t assume common variance Bad: Not implemented in software Hard to interrogate statistic maps

Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2) SPM8 Nonsphericity Modelling Data exploration Conclusions

Assessing RFX Models Issues to Consider Assumptions & Limitations What must I assume? Independence? “Nonsphericity”? (aka independence + homogeneous var.) When can I use it Efficiency & Power How sensitive is it? Validity & Robustness Can I trust the P-values? Are the standard errors correct? If assumptions off, things still OK?

Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2,5,8) SPM8 Nonsphericity Modelling Data exploration Conclusions

Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2,5,8) SPM8 Nonsphericity Modelling Data exploration Conclusions

Holmes & Friston Unweighted summary statistic approach 1- or 2-sample t test on contrast images Intrasubject variance images not used (c.f. FSL) Proceedure Fit GLM for each subject i Compute cbi, contrast estimate Analyze {cbi}i

Holmes & Friston motivation... estimated mean activation image Fixed effects... 1 ^  ^ 2 ^  ^ p < 0.001 (uncorrected) — • – c.f. 2 / nw ^ 3 ^ SPM{t}  ^ – c.f. 4 ^ n – subjects  ^ w – error DF 5 ^  ^ p < 0.05 (corrected) ...powerful but wrong inference SPM{t} 6 ^  ^

Holmes & Friston Random Effects level-one (within-subject) level-two (between-subject) 1 ^  an estimate of the mixed-effects model variance 2 + 2 / w  ^ 2 ^  ^ variance 2  ^ (no voxels significant at p < 0.05 (corrected)) 3 ^   ^ — ^ 4 ^ • – c.f. 2/n = 2 /n + 2 / nw   ^ – c.f. 5 ^   ^ p < 0.001 (uncorrected) 6 ^  SPM{t}  ^ timecourses at [ 03, -78, 00 ] contrast images

Holmes & Friston Assumptions Distribution Normality Independent subjects Homogeneous Variance Intrasubject variance homogeneous 2FFX same for all subjects Balanced designs

Holmes & Friston Limitations Only single image per subject If 2 or more conditions, Must run separate model for each contrast Limitation a strength! No sphericity assumption made on different conditions when each is fit with separate model

Holmes & Friston Efficiency If assumptions true Optimal, fully efficient If 2FFX differs between subjects Reduced efficiency Here, optimal requires down-weighting the 3 highly variable subjects

Holmes & Friston Validity If assumptions true Exact P-values If 2FFX differs btw subj. Standard errors not OK Est. of 2RFX may be biased DF not OK Here, 3 Ss dominate DF < 5 = 6-1 2RFX

Holmes & Friston Robustness In practice, Validity & Efficiency are excellent For one sample case, HF almost impossible to break 2-sample & correlation might give trouble Dramatic imbalance or heteroscedasticity (outlier severity) Mumford & Nichols. Simple group fMRI modeling and inference. Neuroimage, 47(4):1469--1475, 2009. False Positive Rate Power Relative to Optimal

Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2,5,8) SPM8 Nonsphericity Modelling Data exploration Conclusions

SPM8 Nonsphericity Modelling 1 effect per subject Uses Holmes & Friston approach >1 effect per subject Can’t use HF; must use SPM8 Nonsphericity Modelling Variance basis function approach used...

SPM8 Notation: iid case X y = X  + e Cor(ε) = λ I N  1 N  p p  1 N  1 Cor(ε) = λ I X Error covariance 12 subjects, 4 conditions Use F-test to find differences btw conditions Standard Assumptions Identical distn Independence “Sphericity”... but here not realistic! N N

Multiple Variance Components y = X  + e N  1 N  p p  1 N  1 Cor(ε) =Σk λkQk Error covariance 12 subjects, 4 conditions Measurements btw subjects uncorrelated Measurements w/in subjects correlated N N Errors can now have different variances and there can be correlations Allows for ‘nonsphericity’

Non-Sphericity Modeling Errors are independent but not identical Eg. Two Sample T Two basis elements Qk’s: Error Covariance

Non-Sphericity Modeling Error Covariance Errors are not independent and not identical Qk’s:

SPM8 Nonsphericity Modelling Assumptions & Limitations assumed to globally homogeneous lk’s only estimated from voxels with large F Most realistically, Cor(e) spatially heterogeneous Intrasubject variance assumed homogeneous Cor(ε) =Σk λkQk

SPM8 Nonsphericity Modelling Efficiency & Power If assumptions true, fully efficient Validity & Robustness P-values could be wrong (over or under) if local Cor(e) very different from globally assumed Stronger assumptions than Holmes & Friston

Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2,5,8) SPM8 Nonsphericity Modelling Data exploration Conclusions

Data: FIAC Data Acquisition Experiment (Block Design only) Analysis 3 TE Bruker Magnet For each subject: 2 (block design) sessions, 195 EPI images each TR=2.5s, TE=35ms, 646430 volumes, 334mm vx. Experiment (Block Design only) Passive sentence listening 22 Factorial Design Sentence Effect: Same sentence repeated vs different Speaker Effect: Same speaker vs. different Analysis Slice time correction, motion correction, sptl. norm. 555 mm FWHM Gaussian smoothing Box-car convolved w/ canonical HRF Drift fit with DCT, 1/128Hz

Look at the Data! With small n, really can do it! Start with anatomical Alignment OK? Yup Any horrible anatomical anomalies? Nope

Look at the Data! Mean & Standard Deviation also useful Variance lowest in white matter Highest around ventricles

Look at the Data! Then the functionals Set same intensity window for all [-10 10] Last 6 subjects good Some variability in occipital cortex

Feel the Void! Compare functional with anatomical to assess extent of signal voids

Conclusions Random Effects crucial for pop. inference When question reduces to one contrast HF summary statistic approach When question requires multiple contrasts Repeated measures modelling Look at the data!

References for four RFX Approaches in fMRI Holmes & Friston (HF) Summary Statistic approach (contrasts only) Holmes & Friston (HBM 1998). Generalisability, Random Effects & Population Inference. NI, 7(4 (2/3)):S754, 1999. Holmes et al. (SnPM) Permutation inference on summary statistics Nichols & Holmes (2001). Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. HBM, 15;1-25. Holmes, Blair, Watson & Ford (1996). Nonparametric Analysis of Statistic Images from Functional Mapping Experiments. JCBFM, 16:7-22. Friston et al. (SPM8 Nonsphericity Modelling) Empirical Bayesian approach Friston et al. Classical and Bayesian inference in neuroimaging: theory. NI 16(2):465-483, 2002 Friston et al. Classical and Bayesian inference in neuroimaging: variance component estimation in fMRI. NI: 16(2):484-512, 2002. Beckmann et al. & Woolrich et al. (FSL3) Summary Statistics (contrast estimates and variance) Beckmann, Jenkinson & Smith. General Multilevel linear modeling for group analysis in fMRI. NI 20(2):1052-1063 (2003) Woolrich, Behrens et al. Multilevel linear modeling for fMRI group analysis using Bayesian inference. NI 21:1732-1747 (2004)