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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

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i improving snr cont ii preprocessing

I. Improving SNR (cont.)II. Preprocessing

BIAC Graduate fMRI Course

October 12, 2004

theoretical effects of 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
measured effects of field strength
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
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
Fundamental Rule of SNR

For Gaussian noise, experimental power increases with the square root of the number of observations

example of trial averaging
Example of Trial Averaging

Average of 16 trials with SNR = 0.6

increasing power increases spatial extent
Increasing Power increases Spatial Extent

Subject 1

Subject 2

Trials Averaged


500 ms

500 ms



16-20 s







effects of signal noise ratio on extent of activation empirical data
Effects of Signal-Noise Ratio on extent of activation: Empirical Data

Subject 1

Subject 2

Number of Significant Voxels

VN = Vmax[1 - e(-0.016 * N)]

Number of Trials Averaged

active voxel simulation

1000 Voxels, 100 Active

Active Voxel Simulation

Signal + Noise (SNR = 1.0)

  • 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
Effects of Signal-Noise Ratio on extent of activation:Simulation Data

SNR = 1.00

SNR = 0.52 (Young)

Number of Activated Voxels

SNR = 0.35 (Old)

SNR = 0.25

SNR = 0.15

SNR = 0.10

Number of Trials Averaged

explicit and implicit signal averaging
Explicit and Implicit Signal Averaging



r =.82; t(10) = 4.3; p < .001

r =.42; t(129) = 5.3; p < .0001

  • 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
what is preprocessing
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
tools for preprocessing
Tools for Preprocessing
  • SPM
  • Brain Voyager
  • VoxBo
  • AFNI
  • Custom BIAC scripts
why do we correct for slice timing
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
Effects of uncorrected slice timing
  • Base Hemodynamic Response
  • Base HDR + Noise
  • Base HDR + Slice Timing Errors
  • Base HDR + Noise + Slice Timing Errors
base hdr noise
Base HDR + Noise

r = 0.77

r = 0.81

r = 0.80

base hdr slice timing errors
Base HDR + Slice Timing Errors

r = 0.92

r = 0.85

r = 0.62

hdr noise slice timing
HDR + Noise + Slice Timing

r = 0.65

r = 0.67

r = 0.19

interpolation strategies
Interpolation Strategies
  • Linear interpolation
  • Spline interpolation
  • Sinc interpolation
severe head motion simulation
Severe Head Motion: Simulation

Two 4s movements of 8mm in -Y direction (during task epochs)


severe head motion real data
Severe Head Motion: Real Data

Two 4s movements of 8mm in -Y direction (during task epochs)


correcting head 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
limitations of 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
should you coregister
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
standardized spaces
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 to Template

Normalization Template

Normalized Data

should you normalize
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
Slice-Based Normalization

Before Adjustment (15 Subjects)

After Adjustment to Reference Image

Registration courtesy Dr. Martin McKeown (BIAC)

techniques for 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
Effects of Smoothing on Activity

Unsmoothed Data

Smoothed Data (kernel width 5 voxels)

should you spatially smooth
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
  • Classifies voxels within an image into different anatomical divisions
    • Gray Matter
    • White Matter
    • Cerebro-spinal Fluid (CSF)

Image courtesy J. Bizzell & A. Belger

filtering approaches
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
why use an roi based approach
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 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

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
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/