first-level fmri modelingmumford.fmripower.org/2010_summercourse/lev1_2010.pdf · 2010-07-19 ·...
TRANSCRIPT
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First-level fMRI modeling
UCLA Advanced NeuroImagingSummer School, 2010
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Goal in fMRI analysisTask on
Find voxels with BOLD time series that look like this
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Voxelwithsignal
Voxelnosignal
Voxelsignaland drift
Delay of BOLD response
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Voxelwithsignal
Voxelnosignal
Voxelsignaland drift
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Voxelwithsignal
Voxelnosignal
Voxelsignaland drift
Starts off high
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BOLD issues
• BOLD response is delayed– Convolution– FIR modeling
• BOLD time series suffer from low frequency noise– Highpass filtering– Prewhitening– Precoloring
• Scaling the data– Grand mean scaling– Intensity normalization
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Recall the GLM
Single voxel timeseries
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How to make a good model
• Explain as much variability in the dataas possible– If you miss something it will go into the
residual error, e
Big residuals ⇒Big variance⇒Small t stat
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Understanding the data
• Time series drifts down in beginning• BOLD response is delayed
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Simplest Model
=
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Simplest Model
=
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Simplest Model
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Simplest Model
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Modeling the delay
• Hemodynamic response function– Real data was used to find good models for
the hemodynamic response
Stimulus
HRF
(double gamma)
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Convolution
• Combine HRF and expected neuralresponse
Typically model derivative of convolved HRF to adjust for smalldifferences in onset (<1s)
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Different HRF’s
Too symmetric
Basic shape okay,but no post stimulusundershoot
Includes poststimulusundershoot
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Different HRF’s
Too symmetric
Basic shape okay,but no post stimulusundershoot
Includes poststimulusundershoot
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Different HRF’s
Too symmetric
Basic shape okay,but no post stimulusundershoot
Includes poststimulusundershoot
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Assumptions of canonicalHRF
• BOLD increases linearly
Dale & Buckner, 1997
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Assumptions of canonicalHRF
• The width, heightand delay arecorrect
• Lindquist & Wager(2007)– From what I’ve
seen only lookslike it would workwith 1 task
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Finite impulse response model• FIR
– Make no assumption about the shape ofthe HRF
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Constrained basis set
• Lower the number of regressors in themodel by using a basis set
• Constrained to shapes that arereasonable for HRF shapes
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Constrained basis set
Basis set HRF possibilities
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FLOBS
• fMRIB Linear OptimalBasis Sets– Generates a set of
basis sets to modelsignal
– Specify ranges fordifferent portions of thehrf
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Comparison
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More thoughts aboutcanonical HRF
• Advantages:– Simpler analysis– Easily interpretable outcome– Simplifies group analysis
• Disadvantages– Biased if canonical HRF is incorrect
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Unbiased basis sets
• Advantages– Not biased towards a particular shape– Allows testing of hypotheses about specific
HRF parameters• Disadvantages
– Less powerful– Makes group analysis more difficult– Tend to overfit the data (i.e., fit noise)
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We can make a design matrix!
• Start with task blocks or delta functions• Convolve• Estimate the GLM and carry out
hypothesis!
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Convolved Boxcar
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Convolved Boxcar
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The Noise
• White noise– All frequencies have similar power– Not a problem for OLS
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More Noise• Colored noise
– Has structure– OLS needs help!
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What about the drift?
• Sources– Head motion– Cardiac noise– Respiratory noise– Scanner noise
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What the noise looks like
Power spectra of noise data(Zarahn, Aguirre, D’Esposito, NI, 1997)
1/fstructure
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More noise
Average spectrum of principal components(Mitra & Pesaran, Biophysical Journal, 1999)
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More noise
Average spectrum of principal components(Mitra & Pesaran, Biophysical Journal, 1999)
Low frequencynoise
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More noise
Average spectrum of principal components(Mitra & Pesaran, Biophysical Journal, 1999)
Breathing
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More noise
Average spectrum of principal components(Mitra & Pesaran, Biophysical Journal, 1999)
Cardiac
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The 1-2 punch
• Punch 1: Highpass filtering– FSL uses gaussian weighted running line smoother– SPM fits a DCT basis set
• Punch 2: Prewhitening– We’ll get to that later
• There’s also a thing called lowpass filtering(precoloring), but generally it isn’t so great andnobody uses it
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Highpass filtering
• Simply hack off the low frequency noise– SPM: Adds a discrete cosine transform
basis set to design matrix
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Highpass filtering• FSL: Gaussian-weighted running line
smoother– Step 1: Fit a Gaussian weighted running
line
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Highpass filtering• FSL: Gaussian-weighted running line
smoother– Step 1: Fit a Gaussian weighted running
line
Fit at time t is a weightedaverage of data around t
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Highpass filtering
– Step 2: Subtract Gaussian weightedrunning line fit
– IMPORTANT: Must apply filter to both thedata and the design.
• FSL has ‘apply temporal filter’ box in designsetup. Leave it checked!
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Highpass filtered design (FSL)
If it wasn’tfiltered, thistrend wouldn’t behere.
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Highpass filtering
Filter below .01 Hz
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Filter cutoff
• High, but not higher than paradigmfrequency– Look at power spectrum of your design and
base cutoff on that– Block design: Longer than 1 task
cycle…usually twice the task cycle– Event related design: Larger than 66 s
(based on the power spectrum of acanonical HRF of a single response)
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High-pass Filtering• Removes the worst of the low frequency trends
High-pass
From S. Smith
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Highpass filtering
• What does it do to the signal??
Signal Powerspectrum
Lowpassfilter
Highpassfilter
Woolrich et al, NI 2001
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Highpass filtering
• What does it do to the signal??
Signal Powerspectrum
Lowpassfilter
Highpassfilter
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Highpass filtering
• What does it do to the signal??
Signal Powerspectrum
Lowpassfilter
Highpassfilter
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Filtering conclusions
• Lowpass filtering– Idea is to swamp out high frequency noise– Easily removes important signal in ER designs– Choose cutoff to remove noise, but avoid your signal
• Highpass filtering– Removes low frequency drift– We typically avoid designs with low frequencies, so highpass
filtering is always used
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Bandpass filtering
• High and lowpass filtering– Common in functional connectivity analysis
since it allows you to focus on a specificfrequency
– Not typically used in standard fMRIanalyses
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Model with HP filter
=
Parameter ofinterest
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Model with HP filter
=
Parameter ofinterest
Use contrast
c=(1 0 0 0 0 0 0 0 )
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Convolution & HP filter
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Punch 2: Prewhitening
• Highpass filtering– Analogous to using a roller to paint a
wall…you can’t get the edges very neatly• Prewhitening
– More precise estimate of correlation…likeusing a brush for the edges
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Prewhitening
• Remember Gauss Markov?– If our errors are distributed with mean 0,
constant variance and not temporallyautocorrelated then our estimates areunbiased and have the smallest variance ofall unbiased estimators.
– Uh oh,
(even after highpass filtering)
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Prewhitening
• Find K such that
• Premultiply GLM by K
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Prewhitening
• Find K such that
• Premultiply GLM by KAwesome! G-Mholds for our*new* model
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Whitening
• OLS can be used on whitened model–
–
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Prewhitening
• Step 1: Fit the linear regressionignoring temporal autocorrelation
• Step 2: Use residuals from firstregression to estimate temporalautocorrelation to obtain K
• Step 3: Create prewhitened model andestimate in usual way
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Estimating V
• We don’t know V, so we estimate it
• There’s a bias problem….– SPM uses a global covariance estimate to
help with this– FSL uses a local estimate, but smoothes it
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fMRI noise
• Tends to follow 1/ftrend
• Autoregressive (AR)models fit it well–
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Whitening FSL
• Estimates V locally• Step 1: Estimate raw autocorrelations
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Whitening FSL
• Estimates V locally• Step 1: Estimate raw autocorrelations
Lag 1
[e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12]
[e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12]
Take products and average
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Whitening FSL
• Estimates V locally• Step 1: Estimate raw autocorrelations
Lag 2
[e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12]
[e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12]
Take products and average
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Whitening FSL
• Estimates V locally• Step 1: Estimate raw autocorrelations
Lag 7
[e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12][e1 e2 e3 e4 e5 e6 e7…]
Take products and average
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Whitening FSL
• Estimates V locally• Step 1: Estimate raw autocorrelations• Step 2: Smooth spatially• Step 3: Within voxel, smooth
correlation estimates (Tukey taper)– Correlation estimates at high lags aren’t
estimated well, so they are down-weighted
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Whitening FSL
Woolrich et al., 2001, NI
Time Domain Spectral Domain
Raw estimate
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Whitening FSL
Woolrich et al., 2001, NI
Time Domain Spectral Domain
Tukey Taper
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Whitening SPM
• Globally estimates correlation– Correlation of time series averaged over
voxels• Structured correlation estimate
– Scaled AR(1) with correlation 0.2 pluswhite noise
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Whitening SPM
Only 2 parameters are estimated
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Comparisons
• Bandpass (solid), Prewhitening withoutbias correction (dot-dash), highpass(dashed)
PW
HP
BP
Friston, NI 2000
PW
HP
BP
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Summary of filters
• Although bandpass (and lowpass) hasthe best looking bias, it is less efficient
• Highpass filtering doesn’t remove all ofthe structured noise
• Prewhitening with bias correction withhighpass filtering is the traditionalcombination
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Convolution, HP filter, Whitening
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Scaling• Grand Mean Scaling
– Removes intersession variance in globalsignal due to changes in gain of scanneramplification
– Allows us to combine data across subjects– Whole 4D data set is scaled by a single
number– Automatically done in software packages– Doesn’t change variability between time
points
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Scaling
• Proportional scaling– Forces each volume of 4D dataset to have
the same mean– Also done by modeling the global signal– Idea is to remove background activity– Problems can occur if true activation is
wide spread– Negative activations may result
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Intensity Normalizaton
without
with
Signal is lost and negative activation artifacts
Junghofer et al, 2004, NI
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Other modeling considerations
• Adding the derivative of the HRF
• Adding motion parameters to the model
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Model HRF & Derivative
0 5 10 15-0.2
0
0.2
0.4
0.6
0.8
1
1.2
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“Shifted” HRF
0 5 10 15-0.2
0
0.2
0.4
0.6
0.8
1
1.2 Blue line is sum ofHRF and its derivative.
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Temporal derivative
• We model the derivative, but don’t studyinferences of it– Linquist, et al (NI, 2008) suggest this is a
bad idea…may lead to bias• Generally I’d say we don’t worry about
the canonical HRF too much
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Collinearity
• When designing your study, you wantyour tasks to be uncorrelated
• Correlation between regressors lowersthe efficiency of the parameterestimation
• Parameter estimates are highly variable– Can even flip signs
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Why is it a problem?
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Why is it a problem?
• There are an infinite # of solutions forand
…etc
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Collinearity illustration
X1
X2
Cor=0.96
X1
X2
Cor= -0.2
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Correlated RegressorsIntercept X1 X2
Highly variableoverexperiments
Inflated forcorrelatedcase (green)
T statistic
Bias can go ineither direction
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Correlated RegressorsIntercept X1 X2
Highly variableoverexperiments
Inflated forcorrelatedcase (green)
T statistic
Bias can go ineither direction
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Correlated RegressorsIntercept X1 X2
Highly variableoverexperiments
Inflated forcorrelatedcase (green)
T statistic
Bias can go ineither direction
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Residuals don’t change
• The designs explain the same amountof variability
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Collinearity• You can’t fix it after the data have been
collected• You can’t tell from the t statistic if you
had collinearity– FSL has some diagnostics
Absolutevalue ofcorrelation
Bad=whiteoff diagonal
Eigenvaluesfrom SVD
Bad=near 0
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You are now first levelmodeling experts!
• You know why we convolve, highpassfilter and prewhiten
• If you don’t trust the canonical HRFthere are other options using basisfunctions
• Precoloring (lowpass filtering) typicallyisn’t used because it removes signal
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Other things you learned
• Prewhitening was orginally viewed as toobiased, but methods have been developed toalleviate this problem
• Grand mean scaling is necessary in groupanalyses
• Proportional scaling is generally frownedupon (although popular in resting statefunctional connectivity studies)
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Last things you learned
• Add derivative as well as motionparameters (unconvolved) to soak upextra variance
• Check for collinearity!– You should think about it before you collect
your data.