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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 l.jpg

I. Improving SNR (cont.)II. Preprocessing

BIAC Graduate fMRI Course

October 12, 2004



Theoretical effects of field strength l.jpg
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



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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


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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


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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

Average of 16 trials with SNR = 0.6


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Increasing Power increases Spatial Extent

Subject 1

Subject 2

Trials Averaged

4

500 ms

500 ms

16

36

16-20 s

64

100

144


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A

B


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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


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1000 Voxels, 100 Active Empirical Data

Active Voxel Simulation

Signal + Noise (SNR = 1.0)

  • Signal waveform taken from observed data.

  • Signal amplitude distribution: Gamma (observed).

  • Assumed Gaussian white noise.

Noise


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Effects of Signal-Noise Ratio on extent of activation: Empirical DataSimulation 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


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Explicit and Implicit Signal Averaging Empirical Data

A

B

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

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


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Caveats Empirical Data

  • 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



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What is preprocessing? Empirical Data

  • 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


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Quality Assurance Empirical Data


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Tools for Preprocessing Empirical Data

  • SPM

  • Brain Voyager

  • VoxBo

  • AFNI

  • Custom BIAC scripts


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Slice Timing Correction Empirical Data


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Why do we correct for slice timing? Empirical Data

  • 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


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Effects of uncorrected slice timing Empirical Data

  • Base Hemodynamic Response

  • Base HDR + Noise

  • Base HDR + Slice Timing Errors

  • Base HDR + Noise + Slice Timing Errors


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Base HDR: 2s TR Empirical Data


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Base HDR + Noise Empirical Data

r = 0.77

r = 0.81

r = 0.80


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Base HDR + Slice Timing Errors Empirical Data

r = 0.92

r = 0.85

r = 0.62


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HDR + Noise + Slice Timing Empirical Data

r = 0.65

r = 0.67

r = 0.19


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Interpolation Strategies Empirical Data

  • Linear interpolation

  • Spline interpolation

  • Sinc interpolation


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Motion Correction Empirical Data





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Simulated Head Motion Empirical Data


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Severe Head Motion: Simulation Empirical Data

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

Motion


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Severe Head Motion: Real Data Empirical Data

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

Motion


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Correcting Head Motion Empirical Data

  • Rigid body transformation

    • 6 parameters: 3 translation, 3 rotation

  • Minimization of some cost function

    • E.g., sum of squared differences

    • Mutual information



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Limitations of Motion Correction Empirical Data

  • 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 l.jpg
Coregistration head motion on your data?


Should you coregister l.jpg
Should you Coregister? head motion on your data?

  • 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 l.jpg
Normalization head motion on your data?


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Standardized Spaces head motion on your data?

  • 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 l.jpg
Normalization to Template head motion on your data?

Normalization Template

Normalized Data


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Posterior Commissure head motion on your data?

Anterior Commissure

Anterior and Posterior Commissures


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Should you normalize? head motion on your data?

  • 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 l.jpg
Slice-Based Normalization head motion on your data?

Before Adjustment (15 Subjects)

After Adjustment to Reference Image

Registration courtesy Dr. Martin McKeown (BIAC)


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Spatial Smoothing head motion on your data?


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Techniques for Smoothing head motion on your data?

  • Application of Gaussian kernel

    • Usually expressed in #mm FWHM

    • “Full Width – Half Maximum”

    • Typically ~2 times voxel size


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Effects of Smoothing on Activity head motion on your data?

Unsmoothed Data

Smoothed Data (kernel width 5 voxels)


Should you spatially smooth l.jpg
Should you spatially smooth? head motion on your data?

  • 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 l.jpg
Segmentation head motion on your data?

  • 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 l.jpg
Histogram of Voxel Intensities head motion on your data?


Bias field correction l.jpg
Bias Field Correction head motion on your data?


Temporal filtering l.jpg
Temporal Filtering head motion on your data?


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Filtering Approaches head motion on your data?

  • 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 l.jpg
Power Spectra head motion on your data?


Region of interest drawing l.jpg
Region of Interest Drawing head motion on your data?


Why use an roi based approach l.jpg
Why use an ROI-based approach? head motion on your data?

  • 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 l.jpg
Drawing ROIs head motion on your data?

  • 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 l.jpg
ROI Examples head motion on your data?


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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.


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Additional Resources here by determining how context modulates brain activity in elicited when a subject watches a character shift gaze toward or away from a target.

  • 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/


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