group modeling for fmri
DESCRIPTION
Group Modeling for fMRI. Thomas Nichols, Ph.D. Assistant Professor Department of Biostatistics http://www.sph.umich.edu/~nichols IPAM MBI July 21, 2004. Overview. Mixed effects motivation Evaluating mixed effects methods Case Studies Summary statistic approach (HF) SPM2 FSL3 - PowerPoint PPT PresentationTRANSCRIPT
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Group Modelingfor fMRI
Thomas Nichols, Ph.D.Assistant Professor
Department of Biostatistics
http://www.sph.umich.edu/~nichols
IPAM MBIJuly 21, 2004
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SPM2– FSL3
• Conclusions
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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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)
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Subject1 Subject2 Subject3
Random Effects Illustration
• Standard linear model
assumes only one source of iid random variation
• Consider this RT data• Here, two sources
– Within subject var.
– Between subject var.
– Causes dependence in
Subject1 Subject2 Subject3
3 Ss, 5 replicated RT’s
Residuals
XY
x
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Subj. 1
Subj. 2
Subj. 3
Subj. 4
Subj. 5
Subj. 6
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Fixed vs.RandomEffects in fMRI
• Fixed Effects– Intra-subject
variation suggests all these subjects different from zero
• Random Effects– Intersubject
variation suggests population not very different from zero
Distribution of each subject’s estimated effect
Distribution of population effect
2FFX
2RFX
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Fixed Effects
• Only variation (over subjects/sessions) is measurement error
• True Response magnitude is fixed
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Random/Mixed Effects
• Two sources of variation– Measurement error– Response magnitude
• Response magnitude is random– Each subject/session has random magnitude–
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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
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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”
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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Assessing RFX ModelsIssues to Consider
• Assumptions & Limitations– What must I assume?– 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?
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Issues: Assumptions
• Distributional Assumptions– Gaussian? Nonparametric?
• Homogeneous Variance– Over subjects?
– Over conditions?
• Independence– Across subjects?
– Across conditions/repeated measures
– Note:• Nonsphericity = (Heterogeneous Var) or (Dependence)
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Issues: Soft AssumptionsRegularization
• Regularization– Weakened homogeneity assumption
– Usually variance/autocorrelation regularized over space
• Examples– fmristat - local pooling (smoothing) of (2
RFX)/(2FFX)
– SnPM - local pooling (smoothing) of 2RFX
– FSL3 - Bayesian (noninformative) prior on 2RFX
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Issues: Efficiency & Power
• Efficiency: 1/(Estmator Variance)– Goes up with n
• Power: Chance of detecting effect– Goes up with n– Also goes up with degrees of freedom (DF)
• DF accounts for uncertainty in estimate of 2RFX
• Usually DF and n yoked, e.g. DF = n-p
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Issues: Validity
• Are P-values accurate?– I reject my null when P < 0.05
Is my risk of false positives controlled at 5%?– “Exact” control
• FPR = – Valid control (possibly conservative)
• FPR
• Problems when– Standard Errors inaccurate– Degrees of freedom inaccurate
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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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. (SPM2)– Empirical Bayesian approach– Friston et al. Classical and Bayesian inference in neuroimagining: 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)
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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Case Studies: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
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Case Studies: HFAssumptions
• Distribution– Normality– Independent subjects
• Homogeneous Variance– Intrasubject variance homogeneous
2FFX same for all subjects
– Balanced designs
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Case Studies: HFLimitations• 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 conditions– Though nonsphericity
itself may be of interest... From HBMPosterWE 253
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Case Studies: HFEfficiency
• If assumptions true– Optimal, fully efficient
• If 2FFX differs between
subjects– Reduced efficiency– Here, optimal requires
down-weighting the 3 highly variable subjects
0
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Case Studies: HFValidity
• If assumptions true– Exact P-values
• If 2FFX differs btw subj.
– Standard errors OK• Est. of 2
RFX unbiased
– DF not OK• Here, 3 Ss dominate
• DF < 5 = 6-1
0
2RFX
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Case Studies: HFRobustness
• Heterogeneity of 2FFX across subjects...
– How bad is bad?
• Dramatic imbalance (rough rules of thumb only!)
– Some subjects missing 1/2 or more sessions
– Measured covariates of interest having dramatically different efficiency
• E.g. Split event related predictor by correct/incorrect
• One subj 5% trials correct, other subj 80% trials correct
• Dramatic heteroscedasticity– A “bad” subject, e.g. head movement, spike artifacts
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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Case Studies: SnPM
• No Gaussian assumption
• Instead, uses data to find empirical distribution
5%
Parametric Null Distribution
5%
Nonparametric Null Distribution
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Permutation TestToy Example
• Data from V1 voxel in visual stim. experimentA: Active, flashing checkerboard B: Baseline, fixation6 blocks, ABABAB Just consider block averages...
• Null hypothesis Ho – No experimental effect, A & B labels arbitrary
• Statistic– Mean difference
A B A B A B
103.00 90.48 99.93 87.83 99.76 96.06
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Permutation TestToy Example
• Under Ho
– Consider all equivalent relabelings
AAABBB ABABAB BAAABB BABBAA
AABABB ABABBA BAABAB BBAAAB
AABBAB ABBAAB BAABBA BBAABA
AABBBA ABBABA BABAAB BBABAA
ABAABB ABBBAA BABABA BBBAAA
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Permutation TestToy Example
• Under Ho
– Consider all equivalent relabelings– Compute all possible statistic values
AAABBB 4.82 ABABAB 9.45 BAAABB -1.48 BABBAA -6.86
AABABB -3.25 ABABBA 6.97 BAABAB 1.10 BBAAAB 3.15
AABBAB -0.67 ABBAAB 1.38 BAABBA -1.38 BBAABA 0.67
AABBBA -3.15 ABBABA -1.10 BABAAB -6.97 BBABAA 3.25
ABAABB 6.86 ABBBAA 1.48 BABABA -9.45 BBBAAA -4.82
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Permutation TestToy Example
• Under Ho
– Consider all equivalent relabelings– Compute all possible statistic values– Find 95%ile of permutation distribution
AAABBB 4.82 ABABAB 9.45 BAAABB -1.48 BABBAA -6.86
AABABB -3.25 ABABBA 6.97 BAABAB 1.10 BBAAAB 3.15
AABBAB -0.67 ABBAAB 1.38 BAABBA -1.38 BBAABA 0.67
AABBBA -3.15 ABBABA -1.10 BABAAB -6.97 BBABAA 3.25
ABAABB 6.86 ABBBAA 1.48 BABABA -9.45 BBBAAA -4.82
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Permutation TestToy Example
• Under Ho
– Consider all equivalent relabelings– Compute all possible statistic values– Find 95%ile of permutation distribution
AAABBB 4.82 ABABAB 9.45 BAAABB -1.48 BABBAA -6.86
AABABB -3.25 ABABBA 6.97 BAABAB 1.10 BBAAAB 3.15
AABBAB -0.67 ABBAAB 1.38 BAABBA -1.38 BBAABA 0.67
AABBBA -3.15 ABBABA -1.10 BABAAB -6.97 BBABAA 3.25
ABAABB 6.86 ABBBAA 1.48 BABABA -9.45 BBBAAA -4.82
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Permutation TestToy Example
• Under Ho
– Consider all equivalent relabelings– Compute all possible statistic values– Find 95%ile of permutation distribution
0 4 8-4-8
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Case Studies: SnPMAssumptions
• 1-Sample t on difference data– Under null, differences distn symmetric about 0
• OK if 2FFX differs btw subjects!
– Subjects independent
• 2-Sample t– Under null, distn of all data the same
• Implies 2FFX must be the same across subjects
– Subjects independent
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Case Studies: SnPMEfficiency
• Just as with HF... – Lower efficiency if heterogeneous variance– Efficiency increases with n– Efficiency increases with DF
• How to increase DF w/out increasing n?
• Variance smoothing!
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Case Studies: SnPMSmoothed Variance t
• For low df, variance estimates poor– Variance image “spikey”
• Consider smoothed variance t statistic
t-statisticvariance
mean difference
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Permutation TestSmoothed Variance t
• Smoothed variance estimate better– Variance “regularized”– df effectively increased, but no RFT result
SmoothedVariancet-statistic
mean difference
smoothedvariance
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Smoothed Variance t Statistic,Nonparametric Threshold
378 sig. vox.
t11 Statistic, RF & Bonf. Threshold
uRF = 9.87uBonf = 9.805 sig. vox.
t11 Statistic, Nonparametric Threshold
uPerm = 7.67
58 sig. vox.
Permutation Distn Maximum t11
Small GroupfMRI Example
• 12 subject study– Item recognition task
– 1-sample t test on diff. imgs
– FWE threshold
Working memory data: Marshuetz et al (2000)
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Case Studies: SnPMValidity
• If assumptions true– P-values’s exact
• Note on “Scope of inference”– SnPM can make inference on population– Simply need to assume random sampling of
population• Just as with parametric methods!
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Case Studies: SnPMRobustness
• If data not exchangeable under null– Can be invalid, P-values too liberal– More typically, valid but conservative
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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Case Study: SPM2
• 1 effect per subject– Uses Holmes & Friston approach
• >1 effect per subject– Variance basis function approach used...
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y = X + N 1 N p p 1 N 1
I )(Cor
N
N
Error covariance
SPM2 Notation: iid case
• 12 subjects,4 conditions – Use F-test to find
differences btw conditions
• Standard Assumptions– Identical distn– Independence– “Sphericity”... but here
not realistic!
X
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y = X + N 1 N p p 1 N 1
N
N
Error covariance
QCor kk
k )(
Errors can now have different variances and there can be correlations
Allows for ‘nonsphericity’
Multiple Variance Components
• 12 subjects, 4 conditions
• Measurements btw subjects uncorrelated
• Measurements w/in subjects correlated
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Non-Sphericity Modeling
– Errors are independent but not identical
– Errors are not independent and not identical
Error Covariance
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Case Study: SPM2
• Assumptions & Limitations– assumed to globally
homogeneous
– k’s only estimated from voxels with large F
– Most realistically, Cor() spatially heterogeneous
– Intrasubject variance assumed homogeneous
QCor kk
k )(
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Case Study: SPM2
• Efficiency & Power– If assumptions true, fully efficient
• Validity & Robustness– P-values could be wrong (over or under) if
local Cor() very different from globally assumed
– Stronger assumptions than Holmes & Friston
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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FSL3: Full Mixed Effects Model
KKKXY First-level, combines sessions
GGGK X Second-level, combines subjects
HHHG X Third-level, combines/compares groups
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FSL3: Summary Statistics
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Summary Stats Equivalence
0Htc
0Htc
)ˆcov( dof
Crucially, summary stats here are not just estimated effects.
Summary Stats needed for equivalence:
Beckman et al., 2003
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Case Study: FSL3’s FLAME
• Uses summary-stats model equivalent to full Mixed Effects model
• Doesn’t assume intrasubject variance is homogeneous– Designs can be unbalanced– Subjects measurement error can vary
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Case Study: FSL3’s FLAME
• Bayesian Estimation– Priors, priors, priors– Use reference prior
• Final inference on posterior of – | y has Multivariate T distn (MVT)
but with unknown dof
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Approximating MVTs
Samples)|( Yp
)|( Yp MVTdof?
SLOW
Estimate
Model
FAST
BIDETMCMC
)ˆ,|( Yp
Gaussian
BIDET = Bayesian Inference with Distribution Estimation using T
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Overview
• Mixed effects motivation
• Evaluating mixed effects methods
• Case Studies– Summary statistic approach (HF)– SnPM– SPM2– FSL3
• Conclusions
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Conclusions
• Random Effects crucial for pop. inference
• Don’t fall in love with your model!
• Evaluate...– Assumptions– Efficiency/Power– Validity & Robustness