Lab Tutorials

4.2 High-level Analysis with FSL

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High-level analysis in FSL aggregates results from individual subjects to draw inferences about a population. The typical steps involve:

  • Data collection: Gather first-level FEAT outputs (cope, varcope, and sometimes t-statistic images).
  • Design specification: Define your design matrix and contrast vectors to model group-level effects.
  • Estimation: Use FSL’s FEAT (or FLAME) for mixed-effects analysis.
  • Inference: Correct for multiple comparisons and interpret cluster or voxel-level results.

1. Prepare your first-level data

After the first-level analysis, you have the necessary data organized. For example, you put all subject-wise data in subfolder derivatives:

2. Create Design and Contrast Matrix

Open Feat & GUI, and add all first-level feat folders or COPE files, depending on your experimental design.

Andy Jahn’s brain book website provides excellent instructions on this. Please check his second-level analysis and third-level analysis for details.

Essentially, you need to create a higher-level analysis by setting up a new FEAT design file. Here is an example of such design file:

# Higher-level FEAT design example for one-sample t-test
 
# Set the basic parameters
set fmri(npts) 1
set fmri(outputdir) "group_level.feat"
 
# Input subject-level cope images
set feat_files(1) "/path/to/sub01/first_level.feat/stats/cope1.nii.gz"
set feat_files(2) "/path/to/sub02/first_level.feat/stats/cope1.nii.gz"
set feat_files(3) "/path/to/sub03/first_level.feat/stats/cope1.nii.gz"
# ... add more subjects as needed
 
# Define the design matrix file paths
set fmri(groupfile) "feat_dir_list.txt"
set fmri(cope1) 1
 
# Set up the design for a one-sample t-test
set fmri(contrasts) "1"
set fmri(contrast_names1) "Mean Effect"
 
# Use FLAME stage 1 (or stage 1+2 for more accurate variance estimation)
set fmri(flame_level) 2

The above example uses the FLAME method (FMRIB’s Local Analysis of Mixed Effects).

3. Inspecting and Interpreting Results

After running your higher-level analysis, you should review:

  • Statistical maps: (e.g., zstat images) to identify significant clusters.
  • Cluster reports: Check the clusters in the FEAT report.
  • Overlay images: Use FSLview or FSLeyes to overlay significant clusters on anatomical images.

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