Transcript BOLD fMRI - Duke University

Preprocessing of
FMRI Data
fMRI Graduate Course
October 23, 2002
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
Signal, noise, and the General
Linear Model
Y  M  
Amplitude (solve for)
Measured Data
Noise
Design Model
Cf. Boynton et al., 1996
Signal-Noise-Ratio (SNR)
Task-Related
Variability
Non-task-related
Variability
Preprocessing Steps
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Slice Timing Correction
Motion Correction
Coregistration
Normalization
Spatial Smoothing
Segmentation
Region of Interest Identification
Tools for Preprocessing
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SPM
Brain Voyager
VoxBo
AFNI
Custom BIAC scripts (Favorini, McKeown)
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
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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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Slice1
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TR:-1
TR:0
TR:1
TR:2
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Base HDR + Noise
1.8
1.6
r = 0.77
Noise1
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r = 0.81
Noise2
Noise3
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r = 0.80
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Base HDR + Slice Timing Errors
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Slice1
Slice11
r = 0.92
r = 0.85
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Slice12
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r = 0.62
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HDR + Noise + Slice Timing
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Slice1
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Slice11
r = 0.65
r = 0.67
Slice12
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r = 0.19
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TR:-1
TR:0
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Interpolation Strategies
• Linear interpolation
• Spline interpolation
• Sinc interpolation
Motion Correction
Head Motion: Good and Bad
Correcting Head Motion
• Rigid body transformation
– 6 parameters: 3 translation, 3 rotation
• Minimization of some cost function
– E.g., sum of squared differences
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
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
Coregistration
Should you Coregister?
• Advantages
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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)
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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, National fMRI Database, International Consortium
for Brain Mapping
Normalization to Template
Normalization Template
Normalized Data
Anterior and Posterior Commissures
Anterior
Commissure
Posterior
Commissure
Should you normalize?
• Advantages
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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
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
0.04
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Anatomical
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Functional
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)
• Reference Works
– Print atlases
– Online atlases
• Analysis Tools
– roi_analysis_script.m
ROI Examples
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Left Hemisphere - Gaze Shifts
Right Hemisphere - Gaze Shifts
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Distance Posterior from the Anterior Commissure (in mm)
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
– Course Notes
• http://www.fil.ion.ucl.ac.uk/spm/course/notes01.ht
ml
– Instructions
• Brain viewers
– http://www.bic.mni.mcgill.ca/cgi/icbm_view/