overview of spm realignmentsmoothing normalisation general linear model statistical parametric map...
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Overview of SPM
Realignment Smoothing
Normalisation
General linear model
Statistical parametric map (SPM)Image time-series
Parameter estimates
Design matrix
Template
Kernel
Gaussian field theory
p <0.05
Statisticalinference
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The General Linear Model (GLM)
With many thanks for slides & images to:
FIL Methods group, Virginia Flanagin and Klaas Enno Stephan
Frederike Petzschner
Translational Neuromodeling Unit (TNU)Institute for Biomedical Engineering, University of Zurich & ETH Zurich
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Image a very simple experiment…
time
• One session• 7 cycles of rest and
listening• Blocks of 6 scans with 7 sec
TR
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time
Time
single voxel time series
Image a very simple experiment…
Question: Is there a change in the BOLD response between listening and rest?
What we know.
What we measure.
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time
Time
single voxel time series
Image a very simple experiment…
Question: Is there a change in the BOLD response between listening and rest?
What we know.
What we measure.
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linear model
effects estimate
error estimate
statistic
You need a model of your data…
Question: Is there a change in the BOLD response between listening and rest?
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BOLD signal
Tim
e =1 2+ +
err
or
x1 x2 e
Explain your data… as a combination of experimental manipulation,confounds and errors
Single voxel regression model:
regressor
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BOLD signal
Tim
e =1 2+ +
err
or
x1 x2 e
eXy Single voxel regression model:
Explain your data… as a combination of experimental manipulation,confounds and errors
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n
= + +
err
or
e
1
2
eXy
1
n
p
p
1
n
1
n: number of scansp: number of regressors
The black and white version in SPM
Desi
gn
matr
ix
err
or
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The design matrix embodies all available knowledge about experimentally controlled
factors and potential confounds. Talk: Experimental Design Wed 9:45 – 10:45
Model assumptions
Designmatrix
errorYou want to estimate your parameters such that you minimize:
This can be done using an Ordinary least squares estimation (OLS) assuming an i.i.d. error:
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errorGLM assumes identical and independently distributed errors
i.i.d. = error covariance is a scalar multiple of the identity matrix: Cov(e) = 2I
10
01)(eCov
10
04)(eCov
21
12)(eCov
non-identity non-independencet1 t2
t1
t2
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= +
e
2
1
y X
„Option 1“: Per hand
How to fit the model and estimate the parameters?
err
or
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= +
e
2
1
y X
How to fit the model and estimate the parameters?
err
or
Data predicted by our modelError between predicted and actual dataGoal is to determine the betas such that we minimize the quadratic error
OLS (Ordinary Least Squares)
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OLS (Ordinary Least Squares) The goal is to
minimize the quadratic error between data and model
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OLS (Ordinary Least Squares) The goal is to
minimize the quadratic error between data and model
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OLS (Ordinary Least Squares)
This is a scalar and the transpose of a scalar is a scalar
The goal is to minimize the quadratic error between data and model
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OLS (Ordinary Least Squares)
This is a scalar and the transpose of a scalar is a scalar
The goal is to minimize the quadratic error between data and model
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OLS (Ordinary Least Squares)
This is a scalar and the transpose of a scalar is a scalar
You find the extremum of a function by taking its derivative and setting it to zero
The goal is to minimize the quadratic error between data and model
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OLS (Ordinary Least Squares)
This is a scalar and the transpose of a scalar is a scalar
You find the extremum of a function by taking its derivative and setting it to zero
SOLUTION: OLS of the Parameters
The goal is to minimize the quadratic error between data and model
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y
e
Design space defined by X
x1
x2
A geometric perspective on the GLM
ˆ Xy
yXXX TT 1)(ˆ OLS estimates
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x1
x2x2*
y
Correlated and orthogonal regressors
When x2 is orthogonalized with regard to x1, only the parameter estimate for x1 changes, not that for x2!
Correlated regressors = explained variance is shared between regressors
121
2211
exxy
1;1 *21
*2
*211
exxy
Design space defined by X
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linear model
effects estimate
error estimate
statistic
We are nearly there…
= +
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What are the problems?
1. BOLD responses have a delayed and dispersed form.
2. The BOLD signal includes substantial amounts of low-frequency noise.
3. The data are serially correlated (temporally autocorrelated) this violates the assumptions of the noise model in the GLM
Design Error
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t
dtgftgf0
)()()(
The response of a linear time-invariant (LTI) system is the convolution of the input with the system's response to an impulse (delta function).
Problem 1: Shape of BOLD response
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Solution: Convolution model of the BOLD response
expected BOLD response
= input function impulse response
function (HRF)
HRF
t
dtgftgf0
)()()(
blue = datagreen = predicted response, taking convolved with HRFred = predicted response, NOT taking into account the HRF
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Problem 2: Low frequency noise
blue = datablack = mean + low-frequency driftgreen = predicted response, taking into account low-frequency driftred = predicted response, NOT taking into
account low-frequency drift
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Problem 2: Low frequency noise
blue = datablack = mean + low-frequency driftgreen = predicted response, taking into account low-frequency driftred = predicted response, NOT taking into
account low-frequency drift
Linear model
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discrete cosine transform (DCT)
set
discrete cosine transform (DCT)
set
Solution 2: High pass filtering
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Problem 3: Serial correlations
non-identity non-independencet1 t2
t1
t2
i.i.d
n: number of scans
n
n
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Problem 3: Serial correlations
n: number of scans
n
n
autocovariancefunction
withwithttt aee 1 ),0(~ 2 Nt
1st order autoregressive process: AR(1)
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Problem 3: Serial correlations
• Pre-whitening:
1. Use an enhanced noise model with multiple error covariance components, i.e. e ~ N(0,2V) instead of e ~ N(0,2I).
2. Use estimated serial correlation to specify filter matrix W for whitening the data.
WeWXWy
This is i.i.d
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How do we define W ?
• Enhanced noise model
• Remember linear transform for Gaussians
• Choose W such that error covariance becomes spherical
• Conclusion: W is a simple function of V so how do we estimate V ?
WeWXWy
),0(~ 2VNe
),(~
),,(~22
2
aaNy
axyNx
2/1
2
22 ),0(~
VW
IVW
VWNWe
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Find V: Multiple covariance components
),0(~ 2VNe
iiQV
eCovV
)(
= 1 + 2
Q1 Q2
Estimation of hyperparameters with EM (expectation maximisation) or ReML (restricted maximum likelihood).
V
enhanced noise model error covariance components Qand hyperparameters
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linear model
effects estimate
error estimate
statistic
We are there…
= +
• GLM includes all known experimental effects and confounds
• Convolution with a canonical HRF
• High-pass filtering to account for low-frequency drifts
• Estimation of multiple variance components (e.g. to account for serial correlations)
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linear model
effects estimate
error estimate
statistic
We are there…
= +
c = 1 0 0 0 0 0 0 0 0 0 0
Null hypothesis:Null hypothesis: 01
)ˆ(
ˆ
T
T
cStd
ct
Talk: Statistical Inference and design efficiency. Next Talk
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We are there…
Time
single voxel time series
• Mass-univariate approach: GLM applied to > 100,000 voxels
• Threshold of p<0.05 more than 5000 voxels significant by chance! • Massive problem with
multiple comparisons!
• Solution: Gaussian random field theory
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Outlook: further challenges
• correction for multiple comparisons Talk: Multiple Comparisons Wed 8:30 – 9:30
• variability in the HRF across voxels Talk: Experimental Design Wed 9:45 – 10:45
• limitations of frequentist statistics Talk: entire Friday
• GLM ignores interactions among voxels Talk: Multivariate Analysis Thu 12:30 – 13:30
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Thank you!
• Friston, Ashburner, Kiebel, Nichols, Penny (2007) Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier.
• Christensen R (1996) Plane Answers to Complex Questions: The Theory of Linear Models. Springer.
• Friston KJ et al. (1995) Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping 2: 189-210.
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