fmri – week 7 – snr & preprocessing scott huettel, duke university signal-to-noise ratio...
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FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Signal-to-Noise Ratio & Preprocessing
FMRI Undergraduate Course (PSY 181F) FMRI Graduate Course (NBIO 381, PSY
362)
Dr. Scott Huettel, Course Director
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
What is signal? What is noise?
• Signal, literally: Amount of current in receiver coil• Signal, practically: Task-related variability
• Noise, literally and practically: Non-task-related variability
• How can we control the amount of signal?– Scanner properties (e.g., field strength)– Experimental task timing: efficient design– Subject compliance with task: through training
• How can we reduce the noise? – Choose good pulse sequences and have stable scanner – Minimize head motion, to some degree, with restrictors– Effectively preprocess our data
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
I. Introduction to SNR (Signal to Noise Ratio)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Signal, Noise, and the General Linear Model
MYMeasured Data
Amplitude (solve for)
Design Model
Noise
Cf. Boynton et al., 1996
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Signal Size in fMRI
45A 50B
50 - 45C
E(50-45)/45D
Let’s look at two TRs (#s 45 and
50) of one dataset.
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Voxel 3
Voxel 2
Voxel 1
690 730 770
790 830 870
770 810 850
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Signal-Noise-Ratio (SNR)
Task-Related Variability
Non-task-related Variability
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Differences in SNR
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Effects of SNR: Simulation Data
• Hemodynamic response– Unit amplitude– Flat prestimulus baseline
• Gaussian Noise– Temporally uncorrelated (white)– Noise assumed to be constant over epoch
• SNR varied across simulations– Max: 2.0, Min: 0.125
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
SNR = 2.0
-1
-0.5
0
0.5
1
1.5
2
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
SNR = 1.0
-1
-0.5
0
0.5
1
1.5
2
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
SNR = 0.5
-1
-0.5
0
0.5
1
1.5
2
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
SNR = 0.25
-3
-2
-1
0
1
2
3
4
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
SNR = 0.125
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
SNR = 4.0 SNR = 2.0
SNR = 1.0 SNR = .5
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
What are typical SNRs for fMRI data?
• Signal amplitude– Percent signal change: 0.2-2%
• Noise amplitude– Percent signal change: 0.5-5%
• SNR range– Total range: <0.1 to 4.0 – Typical: 0.2 – 0.5 ??
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
II. Properties of Noise in fMRI
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Types of Noise• Thermal noise
– Responsible for variation in background of image– Eddy currents, scanner heating
• Power fluctuations– Typically caused by scanner problems
• Artifact-induced problems
• Variation in subject cognition– Timing of processes
• Head motion• Physiological fluctuations
– Respiration– Cardiac pulsation
• Differences across brain regions– Functional differences– Large vessel effects
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Why is noise assumed to be Gaussian?
• Central limit theorem– If X1 … Xn are a set of independent random
variables, each with an arbitrary probability distribution, then the sum of the set of variables (probability density function) will be distributed normally.
Note: Gaussian refers to the normal, bell-curve distribution.
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Task-related noise?
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Standard Deviation
Image
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Spatial Distribution of Noise
A: Anatomical ImageB: Noise imageC: Physiological noiseD: Motion-related noiseE: Phantom (all noise)F: Phantom
(Physiological)
- Kruger & Glover (2001)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
650
660
670
680
690
700
710
720
730
740
750
1 51 101 151 201
Low and High Frequency Noise
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Noise vs. Variability
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Variability in Subject Behavior: Issues
• Cognitive processes are not static– May take time to engage– Often variable across trials– Subjects’ attention/arousal wax and wane
• Subjects adopt different strategies– Feedback- or sequence-based– Problem-solving methods
• Subjects engage in non-task cognition– Non-task periods do not have the absence of
thinking
What can we do about these problems?
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Response Time Variability
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Intersubject Variability
A & B: Responses across subjects for 2 sessions
C & D: Responses within single subjects across days
E & F: Responses within single subjects within a session
- Aguirre et al., 1998
BA
C D
E F
Note: These data were collected using a periodic
design that allowed timing of stimulus presentation.
This served to exacerbate differences in HDR onset
(e.g., some, but not all, subjects timed!).
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Consistency in the HDR
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Potential Corrections for Inter-Subject Variability
• Use of individual subject’s hemodynamic responses– Corrects for differences in latency/shape
• HDR-free analysis methods– Finite-impulse response (FIR) techniques– Use of basis functions to look for task-evoked changes– Other measures (e.g., area under the curve)
• Corrections across subjects– Random-effects analyses – Use statistical tests across subjects as dependent
measure (rather than averaged data)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Spatial Variability?Visual Paradigm
McGonigle et al., 2000
Cognitive Paradigm
“The following precautions were taken: the same operators always controlled the scanner, ambient light and sound levels were similar
between sessions, and spoken instructions to the subject were always exactly the same.
One obvious factor that we could not control was that our subject was always aware that he had performed the task before in the scanner, only
under slightly different circumstances. We called this the “Groundhog Day” effect.”
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
“A reanalysis…”
Smith et al., 2005 Reducing the threshold gives much less apparent
variability.
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
III. Methods for Improving SNR
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Increasing field strength?
• SNR = signal / noise
• SNR increases linearly with field strength– Signal increases with square of field strength– Noise increases linearly with field strength– A 4.0T scanner should have 2.7x SNR of 1.5T
scanner
• T1 and T2* both change with incr. field strength– T1 increases, reducing signal recovery (bad)– T2* decreases, increasing BOLD contrast (good)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Adapted from Turner, et al. (1993)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Measured Effects of Field Strength
• SNR increases by less than theoretical prediction– Sub-linear increases in SNR; large vessel effects may be
independent of field strength– Where tested, clear advantages of higher field have
been demonstrated– But, physiological noise may counteract gains at high
field ( > ~4.0T ??, > ~7.0T ??)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Measured Effects of Field Strength
• SNR increases by less than theoretical prediction– Sub-linear increases in SNR; large vessel effects may be
independent of field strength– Where tested, clear advantages of higher field have
been demonstrated– But, physiological noise may counteract gains at high
field ( > ~4.0T ??, > ~7.0T ??)
• High-field scanning may provide better data from capillaries and small veins
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Measured Effects of Field Strength
• SNR increases by less than theoretical prediction– Sub-linear increases in SNR; large vessel effects may be
independent of field strength– Where tested, clear advantages of higher field have
been demonstrated– But, physiological noise may counteract gains at high
field ( > ~4.0T ??, > ~7.0T ??)
• High-field scanning may provide better data from capillaries and small veins
• Increased susceptibility artifacts – Some regions (e.g., ventral frontal lobe) may be more
distorted at high field
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
1.5T
4.0T
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Collect more data?
• Static signal, variable noise– Assumes that the MR data recorded (on each trial) are
composed of a signal + (random) noise
• Effects of trial averaging (simplest case)– Signal is present on every trial,
so it remains constant when averaged– Noise randomly varies across trials,
so it decreases with averaging– Thus, SNR increases with averaging
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Increasing Power increases Spatial Extent
Subject 1 Subject 2Trials Averaged
4
16
36
64
100
144
500 ms
16-20 s
500 ms
…
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.85
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.44 2.41
0.00 0.00 0.00 0.00 0.00 0.00 3.36 3.68 2.79 1.78 1.84
0.00 0.00 0.00 0.00 5.88 6.79 8.36 2.09 -0.50 -3.08 -0.96
0.00 0.00 3.20 5.46 2.00 6.50 6.13 5.67 -0.06 -3.41 -1.56
2.66 2.42 0.01 5.81 5.88 5.86 6.84 5.63 3.71 -1.76 -2.25
3.74 3.42 1.43 0.68 2.13 6.47 8.05 8.96 10.27 2.45 0.29
4.60 2.27 0.77 1.41 0.80 1.71 9.65 9.91 12.19 3.17 1.75
0.94 1.38 1.22 2.96 0.30 -1.58 2.19 4.10 5.84 3.06 0.53
-0.46 -1.11 -0.31 1.27 -0.94 -4.97 -3.26 -1.93 -1.07 0.28 -1.21
-4.05 -2.33 -2.67 -2.17 -1.64 -7.44 -7.22 -4.83 -3.93 0.00 0.55
A B
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
0
10
20
30
40
50
60
70
80
90
100
0 25 50 75 100 125 150 175 200
Number of Trials Averaged (N)
Nu
mb
er
of
Sig
nifi
can
t V
oxels
(V
n)
Subject 1
Subject 2
VN = Vmax[1 - e(-0.016 * N)]
Effects of Signal-Noise Ratio on extent of activation: Empirical Data
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Active Voxel Simulation
Signal + Noise (SNR = 1.0)
Noise1000 Voxels, 100 Active
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1 3 5 7 9
11
13
15
17
19
-2
-1.5
-1
-0.5
0
0.5
1
1.5
21 3 5 7 9
11
13
15
17
19
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1 3 5 7 9
11
13
15
17
19
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1 3 5 7 9
11
13
15
17
19
• Signal waveform taken from observed data.
• Signal amplitude distribution: Gamma (observed).
• Assumed Gaussian white noise.
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Effects of Signal-Noise Ratio on extent of activation:
Simulation Data
0
20
40
60
80
100
120
0 50 100 150 200
SNR = 0.10
SNR = 0.15
SNR = 0.25
SNR = 1.00
SNR = 0.52 (Young)
SNR = 0.35 (Old)
Number of Trials Averaged
Nu
mb
er
of
Act
ivate
d V
oxels
Old (66 trials) Young (70 trials) Ratio (Y/O)Observed 26 53 2.0Predicted 57% 97% 1.7
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Caveats
• Signal averaging is based on assumptions– Data = signal + temporally invariant noise– Noise is uncorrelated over time
• If assumptions are violated, then pure averaging ignores potentially valuable information– Amount of noise varies over time– Some noise is temporally correlated (physiology)
• Same principle holds for more complex analyses: increasing the quantity of data improves SNR
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
IV. Preprocessing of FMRI Data
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
What is preprocessing?
• Correcting for non-task-related variability in experimental data– Usually done without consideration of
experimental design; thus, pre-analysis– Occasionally called post-processing, in
reference to being after acquisition
• Attempts to remove, rather than model, data variability
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
A. Quality Assurance
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
B. Slice Timing Correction
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
C. Motion Correction: Good, Bad,…
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
… and catastrophically bad
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Why does head motion introduce problems?
507 89 154
119 171 83
179 117 53
663 507 89
520 119 171
137 179 117
A B C
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Simulated Head Motion
Motion
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Severe Head Motion: Real Data
Two 4s movements of 8mm in -Y direction (during task epochs)
Motion
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Correcting Head Motion
• Rigid body transformation– 6 parameters: 3 translation, 3 rotation
• Minimization of some cost function– E.g., sum of squared differences– Mutual information
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Effects of Head Motion Correction
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Limitations of Motion Correction
• Artifact-related limitations– Loss of data at edges of imaging volume– Ghosts in image do not change in same
manner as real data
• Distortions in fMRI images– Distortions may be dependent on position in
field, not position in head
• Intrinsic problems with correction of both slice timing and head motion
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
What are the best approaches for minimizing the influence of head
motion?
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Prevention…
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
… and training!
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
D. Co-Registration and Normalization
Matching two images of the same subject’s brain
collected using different pulse sequences.
Matching an image from a single subject, typically a
high-resolution anatomical, to a template in a standardized
space.
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Standardized Spaces
• Talairach space (proportional grid system)– From atlas of Talairach and Tournoux (1988)– Based on single subject (60y, Female, Cadaver)– Single hemisphere– First commonly used system
• Montreal Neurological Institute (MNI) space– Combination of many MRI scans on normal controls
• All right-handed subjects– Approximated to Talairach space
• Slightly larger• Taller from AC by 5mm; deeper from AC by 10mm
– Used by FSL, SPM, fMRI Data Center, International Consortium for Brain Mapping, many others
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Comparing Talairach and MNI
Images from Cambridge Brain Imaging Unit
http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Normalization to TemplateNormalization Template Normalized Data
SPM
FSL
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Anterior and Posterior Commissures
Anterior Commissure
Posterior Commissure
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
When wouldn’t you normalize?
• Advantages– Allows generalization of results to larger
population– Improves comparison with other studies– Provides coordinate space for reporting results– Enables averaging across subjects
• Other options?– Anatomical region-of-interest approach– Functional mapping within subjects
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
E. Spatial Smoothing
• Application of Gaussian kernel– Usually expressed
in #mm FWHM– “Full Width – Half
Maximum”– Typically ~2 times
voxel size
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Effects of Smoothing on Activity
Unsmoothed Data
Smoothed Data (kernel width ~12mm)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Before smoothing
(hypothetical data)
After smoothing (hypothetical
data)
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
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
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
F. Temporal Filtering
• Identify unwanted frequency variation– Drift (low-frequency)– Physiology (high-frequency)– Task overlap (high-frequency)
• Reduce power around those frequencies through application of filters
• Potential problem: removal of frequencies composing response of interest
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Power Spectra: Frequencies to Eliminate
FMRI – Week 7 – SNR & Preprocessing Scott Huettel, Duke University
Summary
• There is sometimes signal in your data.– Try to maximize it!
• There is always noise in your data.– Try to prevent it! …via design,
precautions– Try to minimize it! …via preprocessing