Group analyses of fMRI data Methods & models for fMRI data analysis 26 November 2008 Klaas Enno...
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![Page 1: Group analyses of fMRI data Methods & models for fMRI data analysis 26 November 2008 Klaas Enno Stephan Laboratory for Social and Neural Systems Research.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d565503460f94a33ab2/html5/thumbnails/1.jpg)
Group analyses of fMRI data
Methods & models for fMRI data analysis26 November 2008
Klaas Enno Stephan
Laboratory for Social and Neural Systems ResearchInstitute for Empirical Research in EconomicsUniversity of Zurich
Functional Imaging Laboratory (FIL)Wellcome Trust Centre for NeuroimagingUniversity College London
With many thanks for slides & images to:
FIL Methods group, particularly Will Penny
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Overview of SPM
RealignmentRealignment SmoothingSmoothing
NormalisationNormalisation
General linear modelGeneral linear model
Statistical parametric map (SPM)Statistical parametric map (SPM)Image time-seriesImage time-series
Parameter estimatesParameter estimates
Design matrixDesign matrix
TemplateTemplate
KernelKernel
Gaussian Gaussian field theoryfield theory
p <0.05p <0.05
StatisticalStatisticalinferenceinference
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Why hierachical models?
fMRI, single subjectfMRI, single subject
fMRI, multi-subjectfMRI, multi-subject ERP/ERF, multi-subjectERP/ERF, multi-subject
EEG/MEG, single subjectEEG/MEG, single subject
Hierarchical models for all imaging data!
Hierarchical models for all imaging data!
time
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Time
BOLD signalTim
esingle voxel
time series
single voxel
time series
Reminder: voxel-wise time series analysis!
modelspecificati
on
modelspecificati
onparameterestimationparameterestimation
hypothesishypothesis
statisticstatistic
SPMSPM
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The model: voxel-wise GLM
=
e+yy XX
N
1
N N
1 1p
p
Model is specified by1. Design matrix X2. Assumptions about
e
Model is specified by1. Design matrix X2. Assumptions about
e
N: number of scansp: number of regressors
N: number of scansp: number of regressors
eXy eXy
The design matrix embodies all available knowledge about experimentally controlled factors and potential confounds.
),0(~ 2INe ),0(~ 2INe
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GLM assumes Gaussian “spherical” (i.i.d.) errors
sphericity = iid:error covariance is scalar multiple of identity matrix:Cov(e) = 2I
sphericity = iid:error covariance is scalar multiple of identity matrix:Cov(e) = 2I
10
01)(eCov
10
04)(eCov
21
12)(eCov
Examples for non-sphericity:
non-identity
non-independence
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Multiple covariance components at 1st level
),0(~ 2VNe ),0(~ 2VNe
iiQV
eCovV
)(
iiQV
eCovV
)(
= 1 + 2
Q1 Q2
Estimation of hyperparameters with ReML (restricted maximum likelihood).
V
enhanced noise model error covariance components Qand hyperparameters
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WeWXWy
c = 1 0 0 0 0 0 0 0 0 0 0c = 1 0 0 0 0 0 0 0 0 0 0
)ˆ(ˆ
ˆ
T
T
cdts
ct
cWXWXc
cdtsTT
T
)()(ˆ
)ˆ(ˆ
2
)(
ˆˆ
2
2
Rtr
WXWy
ReML-estimates
ReML-estimates
WyWX )(̂
)(2
2/1
eCovV
VW
)(WXWXIRX
t-statistic based on ML estimates
iiQ
V
TT XWXXWX 1)()( TT XWXXWX 1)()( For brevity:
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Group level inference: fixed effects (FFX)
• assumes that parameters are “fixed properties of the population”
• all variability is only intra-subject variability, e.g. due to measurement errors
• Laird & Ware (1982): the probability distribution of the data has the same form for each individual and the same parameters
• In SPM: simply concatenate the data and the design matrices lots of power (proportional to number of scans),
but results are only valid for the group studied, can’t be generalized to the population
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Group level inference: random effects (RFX)
• assumes that model parameters are probabilistically distributed in the population
• variance is due to inter-subject variability
• Laird & Ware (1982): the probability distribution of the data has the same form for each individual, but the parameters vary across individuals
• In SPM: hierarchical model much less power (proportional to number of subjects), but results can (in principle) be generalized to the population
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Recommended reading
Linear hierarchical models
Mixed effect models
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Linear hierarchical model
)()()()1(
)2()2()2()1(
)1()1()1(
nnnn X
X
Xy
)()()( i
k
i
kk
i QC
Hierarchical modelHierarchical model Multiple variance components at each level
Multiple variance components at each level
At each level, distribution of parameters is given by level above.
At each level, distribution of parameters is given by level above.
What we don’t know: distribution of parameters and variance parameters.
What we don’t know: distribution of parameters and variance parameters.
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Example: Two-level model
=
2221
111
X
Xy
1
1+ 1 = 2X
2
+ 2y
)1(1X
)1(2X
)1(3X
Second levelSecond level
First levelFirst level
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Two-level model
(1) (1) (1)
(1) (2) (2) (2)
y X
X
(1) (2) (2) (2) (1)
(1) (2) (2) (1) (2) (1)
y X X
X X X
Friston et al. 2002, NeuroImage
random effects random effects
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Mixed effects analysis
(1) (2) (2) (1) (2) (1)y X X X
(1) (1)
(2) (2) (2) (1) (1)
(2) (2) (2)
ˆ X y
X X
X
(2) (2) (1) (1) (1) T
Cov C X C X
Non-hierarchical modelNon-hierarchical model
Variance components at 2nd level
Variance components at 2nd level
Estimating 2nd level effectsEstimating 2nd level effects
between-level non-sphericity
( ) ( )( ) i iik k
kQC Additionally: within-level non-
sphericity at both levels!
Additionally: within-level non-sphericity at both levels!
Friston et al. 2005, NeuroImage
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Estimation
EM-algorithmEM-algorithm
gJ
d
LdJ
d
dLg
1
2
2
E-stepE-step
M-stepM-step
kk
kQC
Assume, at voxel j:
Assume, at voxel j: kjjk
maximise L ln ( )p y | λ
111
NppNN
Xy
yCXC
XCXCT
yy
Ty
1||
11| )(
Friston et al. 2002, NeuroImage
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Algorithmic equivalence
)()()()1(
)2()2()2()1(
)1()1()1(
nnnn X
X
Xy
Hierarchicalmodel
Hierarchicalmodel
ParametricEmpirical
Bayes (PEB)
ParametricEmpirical
Bayes (PEB)
EM = PEB = ReMLEM = PEB = ReML
RestrictedMaximumLikelihood
(ReML)
RestrictedMaximumLikelihood
(ReML)
Single-levelmodel
Single-levelmodel
)()()1(
)()1()1(
)2()1()1(
...
nn
nn
XXXX
Xy
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Mixed effects analysis
Summarystatistics
Summarystatistics
EMapproach
EMapproach
(2)̂
jjj
i
Tii QXQXV
XXY
)2()2()1()1()1()1(
)2(
)1(ˆ
yVXXVX TT 111)2( )(ˆ yVXXVX TT 111)2( )(ˆ
yVXXVX TT 111)1( )(ˆ yVXXVX TT 111)1( )(ˆ
},,{ QXnyyREML T },,{ QXnyyREML T
Step 1
Step 2
},,,{
][)1()2(
1)1()1(
1
)2()1()0(
TXQXQQ
XXXX
IV
XXX
][ )1()0(
datay
Friston et al. 2005, NeuroImage
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Practical problems
Most 2-level models are just too big to compute.
Most 2-level models are just too big to compute.
And even if, it takes a long time! And even if, it takes a long time!
Moreover, sometimes we are only interested in one specific effect and do not want to model all the data.
Moreover, sometimes we are only interested in one specific effect and do not want to model all the data.
Is there a fast approximation?Is there a fast approximation?
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Summary statistics approach
Data Design Matrix Contrast Images )ˆ(ˆ
ˆ
T
T
craV
ct
SPM(t)1̂
2̂
11̂
12̂
21̂
22̂
211̂
212̂
Second levelSecond levelFirst levelFirst level
One-samplet-test @ 2nd level
One-samplet-test @ 2nd level
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Validity of the summary statistics approach
The summary stats approach is exact if for each session/subject:
The summary stats approach is exact if for each session/subject:
All other cases: Summary stats approach seems to be fairly robust against typical violations.
All other cases: Summary stats approach seems to be fairly robust against typical violations.
Within-session covariance the sameWithin-session covariance the same
First-level design the sameFirst-level design the same
One contrast per sessionOne contrast per session
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Reminder: sphericity
„sphericity“ means:„sphericity“ means:
ICov 2)(
Xy )()( TECovC
Scans
Sca
ns
i.e.2)( iVar
12
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2nd level: non-sphericity
Errors are independent but not identical:
e.g. different groups (patients, controls)
Errors are independent but not identical:
e.g. different groups (patients, controls)
Errors are not independent and not identical:
e.g. repeated measures for each subject (like multiple basis
functions)
Errors are not independent and not identical:
e.g. repeated measures for each subject (like multiple basis
functions)
Errorcovariance
Errorcovariance
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Example 1: non-indentical & independent errors
Stimuli:Stimuli: Auditory Presentation (SOA = 4 secs) of(i) words and (ii) words spoken backwards
Auditory Presentation (SOA = 4 secs) of(i) words and (ii) words spoken backwards
Subjects:Subjects:
e.g. “Book”
and “Koob”
e.g. “Book”
and “Koob”
fMRI, 250 scans per subject, block design
fMRI, 250 scans per subject, block design
Scanning:Scanning:
(i) 12 control subjects(ii) 11 blind subjects
(i) 12 control subjects(ii) 11 blind subjects
Noppeney et al.
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1st level:1st level:
2nd level:2nd level:
ControlsControls BlindsBlinds
X
]11[ TcV
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Stimuli:Stimuli: Auditory Presentation (SOA = 4 secs) of words Auditory Presentation (SOA = 4 secs) of words
Subjects:Subjects:
fMRI, 250 scans persubject, block design
fMRI, 250 scans persubject, block designScanning:Scanning:
(i) 12 control subjects(i) 12 control subjects
1. Motion 2. Sound 3. Visual 4. Action
“jump” “click” “pink” “turn”
Question:Question:What regions are affectedby the semantic content ofthe words?
What regions are affectedby the semantic content ofthe words?
Example 2: non-indentical & non-independent errors
Noppeney et al.
1. Words referred to body motion. Subjects decided if the body movement was slow.
2. Words referred to auditory features. Subjects decided if the sound was usually loud
3. Words referred to visual features. Subjects decided if the visual form was curved.
4. Words referred to hand actions. Subjects decided if the hand action involved a tool.
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Repeated measures ANOVA
1st level:1st level:
2nd level:2nd level:
3.Visual3.Visual 4.Action4.Action
X
?=
?=
?=
1.Motion1.Motion 2.Sound2.Sound
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Repeated measures ANOVA
1st level:1st level:
2nd level:2nd level:
3.Visual3.Visual 4.Action4.Action
X
?=
?=
?=
1.Motion1.Motion 2.Sound2.Sound
1100
0110
0011Tc
V
X
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Practical conclusions
• Linear hierarchical models are general enough for typical multi-subject imaging data (PET, fMRI, EEG/MEG).
• Summary statistics are robust approximation to mixed-effects analysis.
– Use mixed-effects model only, if seriously in doubt about validity of summary statistics approach.
• RFX: If not using multi-dimensional contrasts at 2nd level (F-tests), use a series of 1-sample t-tests at the 2nd level.
– To minimize number of variance components to be estimated at 2nd level, compute relevant contrasts at 1st level and use simple test at 2nd level.
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Thank you