I. Improving SNR (cont.)
II. Preprocessing
BIAC Graduate fMRI Course
October 12, 2004
Increasing Field Strength
Theoretical Effects of 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 field strength– T1 increases, reducing signal recovery– T2* decreases, increasing BOLD contrast
Adapted from Turner, et al. (1993)
Measured Effects of Field Strength
• SNR usually 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)
• Spatial extent increases with field strength• Increased susceptibility artifacts
Trial Averaging
• Static signal, variable noise– Assumes that the MR data recorded on each trial are
composed of a signal + (random) noise
• Effects of averaging– Signal is present on every trial, so it remains constant
through averaging– Noise randomly varies across trials, so it decreases
with averaging– Thus, SNR increases with averaging
Fundamental Rule of SNR
For Gaussian noise, experimental power increases with the square root of the
number of observations
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Example of Trial Averaging-1.5
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Average of 16 trials with SNR = 0.6
Increasing Power increases Spatial Extent
Subject 1 Subject 2Trials Averaged
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Peak latency of reference HDR
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Correlation of data with prediction
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Subject1 Subject 2
Number of Trials Averaged
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VN = Vmax[1 - e(-0.016 * N)]
Effects of Signal-Noise Ratio on extent of activation: Empirical Data
Active Voxel Simulation
Signal + Noise (SNR = 1.0)
Noise1000 Voxels, 100 Active
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• Signal waveform taken from observed data.
• Signal amplitude distribution: Gamma (observed).
• Assumed Gaussian white noise.
Effects of Signal-Noise Ratio on extent of activation:
Simulation Data
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SNR = 0.10
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SNR = 0.52 (Young)
SNR = 0.35 (Old)
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Old (66 trials) Young (70 trials) Ratio (Y/O)Observed 26 53 2.0Predicted 57% 97% 1.7
Explicit and Implicit Signal Averaging
r =.42; t(129) = 5.3; p < .0001
r =.82; t(10) = 4.3; p < .001
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Caveats
• Signal averaging is based on assumptions– Data = signal + temporally invariant noise– Noise is uncorrelated over time
• If assumptions are violated, then averaging ignores potentially valuable information– Amount of noise varies over time– Some noise is temporally correlated (physiology)
• Nevertheless, averaging provides robust, reliable method for determining brain activity
II. Preprocessing of FMRI Data
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
Quality Assurance
Tools for Preprocessing
• SPM
• Brain Voyager
• VoxBo
• AFNI
• Custom BIAC scripts
Slice Timing Correction
Why do we correct for slice timing?
• Corrects for differences in acquisition time within a TR– Especially important for long TRs (where expected HDR
amplitude may vary significantly)– Accuracy of interpolation also decreases with increasing TR
• When should it be done?– Before motion correction: interpolates data from (potentially)
different voxels• Better for interleaved acquisition
– After motion correction: changes in slice of voxels results in changes in time within TR
• Better for sequential acquisition
Effects of uncorrected slice timing
• Base Hemodynamic Response
• Base HDR + Noise
• Base HDR + Slice Timing Errors
• Base HDR + Noise + Slice Timing Errors
Base HDR: 2s TR
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HDR + Noise + Slice Timing
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Interpolation Strategies
• Linear interpolation
• Spline interpolation
• Sinc interpolation
Motion Correction
Head Motion: Good, Bad,…
… and catastrophically bad
Why does head motion introduce problems?
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Simulated Head Motion
Severe Head Motion: Simulation
Two 4s movements of 8mm in -Y direction (during task epochs)
Motion
Severe Head Motion: Real Data
Two 4s movements of 8mm in -Y direction (during task epochs)
Motion
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
Effects of Head Motion Correction
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
What is the best approach for minimizing the influence of head motion on your data?
Coregistration
Should you Coregister?
• Advantages– Aids in normalization– Allows display of activation on anatomical images– Allows comparison across modalities– Necessary if no coplanar anatomical images
• Disadvantages– May severely distort functional data– May reduce correspondence between functional and
anatomical images
Normalization
Standardized Spaces
• Talairach space (proportional grid system)– From atlas of Talairach and Tournoux (1988)– Based on single subject (60y, Female, Cadaver)– Single hemisphere– Related to Brodmann coordinates
• Montreal Neurological Institute (MNI) space– Combination of many MRI scans on normal controls
• All right-handed subjects– Approximated to Talaraich space
• Slightly larger• Taller from AC to top by 5mm; deeper from AC to bottom by 10mm
– Used by SPM, fMRI Data Center, International Consortium for Brain Mapping
Normalization to Template
Normalization Template Normalized Data
Anterior and Posterior Commissures
Anterior Commissure
Posterior Commissure
Should 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
• Disadvantages– Reduces spatial resolution– May reduce activation strength by subject averaging– Time consuming, potentially problematic
• Doing bad normalization is much worse than not normalizing (and using another approach)
Slice-Based Normalization
Before Adjustment (15 Subjects)
After Adjustment to Reference Image
Registration courtesy Dr. Martin McKeown (BIAC)
Spatial Smoothing
Techniques for Smoothing
• Application of Gaussian kernel– Usually expressed in
#mm FWHM– “Full Width – Half
Maximum”– Typically ~2 times
voxel size
Effects of Smoothing on Activity
Unsmoothed Data
Smoothed Data (kernel width 5 voxels)
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
Segmentation
• Classifies voxels within an image into different anatomical divisions– Gray Matter– White Matter– Cerebro-spinal Fluid (CSF)
Image courtesy J. Bizzell & A. Belger
Histogram of Voxel Intensities
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Anatomical
Functional
Bias Field Correction
Temporal Filtering
Filtering Approaches
• 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
Power Spectra
Region of Interest Drawing
Why use an ROI-based approach?
• Allows direct, unbiased measurement of activity in an anatomical region– Assumes functional divisions tend to follow
anatomical divisions
• Improves ability to identify topographic changes– Motor mapping (central sulcus)– Social perception mapping (superior temporal sulcus)
• Complements voxel-based analyses
Drawing ROIs
• Drawing Tools– BIAC software (e.g., Overlay2)– Analyze– IRIS/SNAP (G. Gerig from UNC)
• Reference Works– Print atlases– Online atlases
• Analysis Tools– roi_analysis_script.m
ROI Examples
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Distance Posterior from the Anterior Commissure (in mm)
Left Hemisphere - Gaze Shifts Right Hemisphere - Gaze Shifts
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BIAC is studying biological motion and social perception – here by determining how context modulates brain activity in elicited when a subject watches a character shift gaze toward or away from a target.
Additional Resources
• SPM website– http://www.fil.ion.ucl.ac.uk/spm/course/notes01.html– SPM Manual
• Brain viewers– http://www.bic.mni.mcgill.ca/cgi/icbm_view/