1. Class 5: Signal, Noise, and the fMRI data preprocessing
fMRI: theory and practice
Spring 2010

2. The more steps done at once, the greater the chance of problems
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
Big Black Box
of automated software
Raw
Data
Pretty pictures

3. Know Thy Data Look at raw functional images Look at the movies
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.

4. Hardware Malfunctions
Sample Artifacts
Ghosts
Hardware Malfunctions
Metallic Objects (e.g., hair tie)
Spikes
Longitudinal saturation effect

5. Calculating Raw Signal:Noise Ratio
Pick a region of interest (ROI) outside the brain free from artifacts (no ghosts, susceptibility artifacts). Find mean () and standard deviation (SD).
Pick an ROI inside the brain in the area you care about. Find  and SD.
e.g., =4, SD=2.1
SNR = brain/ outside = 200/4 = 50
[Alternatively SNR = brain/ SDoutside = 200/2.1 = 95
(should be 1/1.91 of above because /SD ~ 1.91)]
When citing SNR, state which denominator you used.
e.g.,  = 200
Head coil should have SNR > 50:1
Surface coil should have SNR > 100:1
Source: Joe Gati, personal communication

6. Calculating Functional SNR

7. What affects SNR? Physical factors
SOLUTION & TRADEOFF
Thermal Noise (body & system)
Inherent – can’t change
Magnet Strength
e.g. 1.5T  4T gives 2-4X increase in SNR
Use higher field magnet
additional cost and maintenance
physiological noise may increase
Coil
e.g., head  surface coil gives ~2+X increase in SNR
Use surface coil
Lose other brain areas
Lose homogeneity
Voxel size
e.g., doubling slice thickness increases SNR by root-2
Use larger voxel size
Lose resolution
Sampling time
Longer scan sessions
additional time, money and subject discomfort
Source: Doug Noll’s online tutorial

8. Coils Head coil Surface coil homogenous signal moderate SNR
highest signal at hotspot
high SNR at hotspot
Source: Joe Gati

9. Bigger is better… to a point
Voxel size
Bigger is better… to a point
Increasing voxel size  signals summate, noise cancels out
“Partial voluming”: If tissue is of different types, then increasing voxel size waters down differences
e.g., gray and white matter in an anatomical
e.g., activated and unactivated tissue in a functional

10. Sampling Time
More samples  More confidence effects are real

11. What affects SNR? Physiological factors
SOLUTION & TRADEOFF
Head (and body) motion
Use experienced or well-warned subjects
limits useable subjects
Use head-restraint system
possible subject discomfort
Post-processing correction
often incompletely effective
2nd order effects
can introduce other artifacts
Single trials to avoid body motion
Cardiac and respiratory noise
Monitor and compensate
hassle
Low frequency noise
Use smart design
Perform post-processing filtering
BOLD noise (neural and vascular fluctuations)
Use many trials to average out variability
Behavioral variations
Use well-controlled paradigm
Source: Doug Noll’s online tutorial

12. Effects of field strength

13. Showing visual columns in 7T

14. But bigger magnet has its downside…

15. Sources of Noise
Physical noise
“Blame the magnet, the physicist, or the laws of physics”
Physiological noise
“Blame the subject”

16. Freq distribution of physiological noise

17. A Map of Noise
voxels with high variability shown in white

18. Linear Drift (or scanner drift)

19. Distribution of physiological noise

20. Field Strength
Although Raw SNR goes up with field strength, so does thermal and physiological noise
Thus there are diminishing returns for increases in field strength

21. 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
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)

22. 2. slice-scan time correction

23. 3. Motion-induced intensity Changes
Slide modified from Duke course

24. Motion  Spurious Activation at Edges
lateral
motion in
x direction
motion in
z direction
(e.g., padding sinks)
brain
position
time1
time2 
time 1 > time 2
time 1 < time 2
stat
map

25. Spurious Activation at Edges

26. Motion Correction Algorithms
pitch
roll
yaw
z translation
y translation
x translation
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

27. Head Motion: relatively good

28. … and catastrophically bad
Slide from Duke course

29. 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

30. Different motions; different effects
Drift 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
problem
can reduce problems; may be improved by including motion parameters as predictors of no interest
Regions move
minor-major problem depending on size of movement
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
can’t fix problem; may be misled by artifacts

31. The Fridge Rule
When it doubt, throw it out!

32. (more comfortable than it sounds!)
Head Restraint
Vacuum Pack
Head Vise
(more comfortable than it sounds!)
Bite Bar
Thermoplastic mask
Often a whack of foam padding works as well as anything

33. Even the mock scanner…

34. 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 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

35. 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

36. Reduction of false-positive rate by spatial smoothing

37. Effects of Spatial Smoothing on Activity
Unsmoothed Data
Smoothed Data (kernel width 5 voxels)
Slide from Duke course

38. 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

39. 5. Time Course Filtering

40. Low and High Frequency Noise
Source: Smith chapter in Functional MRI: An Introduction to Methods

41. Preprocessing Options
Before LTR:
After LTR:

42. 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
After High-pass
linear drift
~1/2 cycle/time course
~2 cycles/time course

43. 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
Before Gaussian (Low Pass) filtering
After Gaussian (Low Pass) filtering

44. 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