preprocess




Spatial Normalization: AFNI, SPM, and FSL – Effect on Functional Data

Purpose:

Characterize relative accuracy of SPM5’s high-order, FSL’s fnirt, and AFNI’s @auto_tlrc methods for spatially normalizing images. @auto_tlrc is at a disadvantage because it is a 9-parameter affine warp while both SPM and FSL use many more parameters. The method in SPM5 uses a discrete cosine transform as a basis set, and this is parameterized by roughly 450 3-element vectors. FSL’s fnirt uses a basis set that appears to have a similar spatial resolution but it enforces a diffeomorphic constraint. See the FSL website fnirt and the SPM website

Goal

Evaluate effect of normalization method on group-level t-statistics

Methods

  • Data from a study of children from ages 10-14
  • N = 48 subjects
  • Angry/happy/neutral faces task
  • Compute Main effect of task using AFNI
  • Correct for multiple comparisons using a Bonferroni correction.
  • Compare 4 methods of spatial normalization:
    • AFNI: @auto_tlrc
    • SPM: SPM5 high parameter warp.
    • FSL — flirt
      • Use flirt to register EPIs to the structural
      • Then use applywarp to apply high-parameter warp computed by fnirt.
    • FSL — resample
      • Use 3dresample to register EPIs to structural using a rigid body transformation (assumes no motion between T1 and EPI)
      • Use fnirt to apply high parameter warp

Results

Comparison of activation maps computed with each method at 8 levels.

  1. z = -20 mm
  2. z = -10 mm
  3. z = +2 mm
  4. z = +10 mm
  5. z = +20 mm
  6. z = +30 mm
  7. z = +44 mm
  8. z = +52 mm

Summary

  • Differences not tremendous among afni, spm, fsl — resample
  • FSL — flirt yields higher t-statistics except for regions that disappear (see insula at z = +10 mm)




Last modified May 17, 2009