1. Preprocessing of FMRI Data fMRI Graduate Course October 22, 2003.
Preprocessing in fMRI
Transcript of Preprocessing in fMRI
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Preprocessing in fMRI
Chun-Chia Kung
Oct 5, 2013
NCKU MRI center
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The Black Box
• The danger of automated processing and fancy images is that you can get blobs without every really looking at the real data
• The more steps done at once, the greater the chance of problems
Raw
Data
Big Black Box
of automated
software
Pretty pictures
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Know Thy Data
• Look at raw functional images
– Where are the artifacts and distortions?
– How well do the functionals and anatomicals correspond
• Look at the movies
– Is there any evidence of head motion?
– Is there any evidence of scanner artifacts (e.g., spikes)
• Look at the time courses
– Is there anything unexpected (e.g., abrupt signal changes at the start of the run)?
– What do the time courses look like in the unactivatable areas (ventricles, white matter, outside head)?
• Look at individual subjects
• Double check effects of various transformations
– Make sure left and right didn’t get reversed
– Make sure functionals line up well with anatomicals following all transformations
• Think as you go. Investigate suspicious patterns.
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Data Preprocessing Options
1. artifact screening• ensure the data is free from scanner and subject artifacts
• done by eyeballing and manual correction
2. slice scan time correction• correct for sampling of different slices at different times
3. motion correction• correct for sampling of different slices at different times
4. spatial filtering• smooth the spatial data
5. temporal filtering• remove low frequency drifts (e.g., linear trends)
• remove high frequency noise (not recommended because it increases temporal
autocorrelation and artificially inflates statistics)
6. spatial normalization• put data in standard space (Talairach or MNI Space)
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Sample ArtifactsGhosts
Spikes
Metallic Objects (e.g., hair tie)Hardware Malfunctions
Longitudinal saturation effect
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Freq distribution of physiological noise
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A Map of Noise
• voxels with high variability shown in white
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Linear Drift (or scanner drift)
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Distribution of physiological noise
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2. slice-scan time correction
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3. Motion-induced intensity Changes
Slide modified from Duke course
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Motion Spurious Activation at Edges
time1 time2
lateral
motion in
x direction
motion in
z direction
(e.g., padding sinks)
time 1 > time 2
time 1 < time 2
brain
position
stat
map
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Spurious Activation at Edges
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Motion Correction Algorithms
• Most algorithms assume a rigid body (i.e., that brain doesn’t deform with movement)
• Align each volume of the brain to a target volume using six parameters: three translations and three rotations
• Target volume: the functional volume that is closest in time to the anatomical image
x translation
z t
ransla
tion
y t
ransla
tion
pitch roll yaw
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Head Motion: relatively good
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… and catastrophically bad
Slide from Duke course
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Problems with Motion Correction
• lose information from top and bottom of image
– possible solution: prospective motion correction• calculate motion prior to volume collection and change slice plan
accordingly
we’re missing data here
we have extra data here
Time 1 Time 2
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Different motions; different effectsDrift within run Movement
between runs
Uncorrelated
abrupt movement
within run
Correlated abrupt
movement within a
run
Motion correction
Spurious activations okay, corrected
by LTR
okay minor problem huge problem can reduce
problems
Increased residuals okay, corrected
by LTR
okay problem problem can reduce
problems; may be
improved by
including motion
parameters as
predictors of no
interest
Regions move problem minor-major
problem depending
on size of
movement
problem problem can reduce
problems; if
algorithm is fooled
by physics
artifacts, problem
can be made
worse by MC
Physics artifacts not such a
problem
because effects
are gradual
okay problem huge problem can’t fix problem;
may be misled by
artifacts
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The Fridge Rule
• When it doubt, throw it out!
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Head Restraint
Head Vise(more comfortable than it
sounds!)
Bite Bar
Often a whack of foam padding works as well as anything
Vacuum Pack
Thermoplastic mask
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Even the mock scanner…
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Prevention is the Best Remedy
• Tell your subjects how to be good subjects– “Don’t move” is too vague
• Make sure the subject is comfy going in– avoid “princess and the pea” phenomenon
• Emphasize importance of not moving at all during beeping– do not change posture
– if possible, do not swallow
– do not change posture
– do not change mouth position
– do not tense up at start of scan
• Discourage any movements that would displace the head between scans
• Do not use compressible head support
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4. Spatial Smoothing
• Application of Gaussian kernel
– Usually expressed in #mm FWHM
– “Full Width – Half Maximum”
– Typically ~2 times voxel size
Slide from Duke course
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Reduction of false-positive rate by spatial smoothing
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Effects of Spatial Smoothing on Activity
Unsmoothed Data
Smoothed Data (kernel width 5 voxels)
Slide from Duke course
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Should you spatially smooth?
• Advantages– Increases Signal to Noise Ratio (SNR)
• Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal
– Reduces number of comparisons• Allows application of Gaussian Field Theory
– May improve comparisons across subjects• Signal may be spread widely across cortex, due to intersubject variability
• Disadvantages– Reduces spatial resolution – Challenging to smooth accurately if size/shape of signal is not
known
Slide from Duke course
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5. Time Course Filtering
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Low and High Frequency Noise
Source: Smith chapter in Functional MRI: An Introduction to Methods
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Preprocessing Options
Before LTR:
After LTR:
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Preprocessing Options
High pass filter•pass the high frequencies, block the low frequencies•a linear trend is really just a very very low frequency so LTR may not be strictly necessary if HP filtering is performed (though it doesn’t hurt)
Before High-pass
linear drift
~1/2 cycle/time course
~2 cycles/time course
After High-pass
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Preprocessing Options
• Gaussian filtering
– each time point gets averaged with adjacent time points
– has the effect of being a low pass filter
• passes the low frequencies, blocks the high frequencies
– You better know it clearly what you are doing
After Gaussian (Low Pass) filteringBefore Gaussian (Low Pass) filtering
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Take home Messages
• Look at your data
• Work with your physicist to minimize physical noise
• Design your experiments to minimize physiological noise
• Motion is the worst problem: When in doubt, triple-check
• Preprocessing is not always a “one size fits all” exercise