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Contents

  1. Introduction to FSFAST Preprocessing
  2. Quality Assurance
    1. Motion Correction
    2. Functional-Anatomical Cross-modal Registration
  3. Study Questions
  4. Preprocessing Details (Skip or defer to the end for FreeSurfer Course)
    1. Template
    2. Masking
    3. Intensity Normalization and Global Mean Time Course
    4. Functional-Anatomical Cross-modal Registration
    5. Motion Correction (MC)
    6. Slice-Timing Correction (STC)
    7. Resampling to Common Spaces and Spatial Smoothing

1. Introduction to FSFAST Preprocessing

Once the data have been arranged in the proper directory structure and naming convention, they are ready to be preprocessed. Preprocessing includes

  1. Template Creation
  2. Brain Mask Creation

  3. Registration with FreeSurfer Anatomical

  4. Motion Correction
  5. Slice Timing Correction (if using)
  6. Spatial Normalization
  7. Masking
  8. Spatial Smoothing

In FS-FAST, it is assumed that each data set will be analyzed in three normalization spaces:

You will need to decide how much to smooth the data and whether you want to do slice-timing correction. In this analysis, we will smooth the data by 5mm Full-Width/Half-Max (FWHM) and correct for slice timing. The slice-timing for this particular data set was 'Ascending', meaning that the first slice was acquired first, the second slice was acquired second, etc. To preprocess the data, you could run a command similar to the one below.

Do not run this step! The data has already been pre-processed for you. 

export SUBJECTS_DIR=$TUTORIAL_DATA/fsfast-tutorial.subjects
cd $TUTORIAL_DATA/fsfast-functional

preproc-sess -s sess01 -fsd bold -stc up -surface fsaverage lhrh -mni305 -fwhm 5 -per-run

If you accidentally run this command, you can hit Ctrl+C to cancel it. You should already have the output data available to you and can proceed to the next step.

This command has several arguments:

This command does a lot, and it can take quite a long time to run, especially for many subjects. Look at the contents of one of the run directories: 

ls $TUTORIAL_DATA/fsfast-functional/sess01/bold/001

You will see many files there, but there are three important ones:

These are time series data, and their names indicate what has been performed on them:

dirstruct

To learn more, see Preprocessing Details below.

2. Quality Assurance

2.1. Motion Correction

The motion plots can be viewed with the following command:

A proper installation of gnuplot is required for the plot-twf-sess command to work.

If gnuplot is not installed on your machine, the output can be viewed below. 

plot-twf-sess -s sess01 -fsd bold -mc

The output of this command is shown below. This gives the vector motion at each time point for each run. Note that it is always positive because this is a magnitude. It is also 0 at the middle time point because the middle time point is used as the reference.

plot-twf-sess.png

There are no rules for how much motion is too much motion. Generally speaking, sudden motions are the worst as are task-related motion.

2.2. Functional-Anatomical Cross-modal Registration

You can get a summary of registration quality using the following command: 

tkregister-sess -s sess01 -s sess02 -s sess03 -fsd bold -per-run -bbr-sum

This prints out a value for each run for each session: 

sess01     001    0.5740
sess01     002    0.5796
sess01     003    0.5832
sess01     004    0.5747
sess02     001    0.5159
sess03     001    0.6021

Actual values depend upon exactly how you have acquired your data. Generally, anything over 0.8 indicates that something is probably wrong, such as:

You can view registrations using the following command: 

tkregister-sess -s sess02 -fsd bold -per-run

tkregister

If you want to learn more about using tkregister or freeview see Multimodal Tutorial.

3. Study Questions

4. Preprocessing Details (Skip or defer to the end for FreeSurfer Course)

You do NOT need to perform the steps below to understand the rest of the tutorial. This is provided to allow you to understand the inner workings of FSFAST Preprocessing better. Go back into one of the run directories and see what preproc-sess creates. To do this, 

cd $TUTORIAL_DATA/fsfast-functional/sess01/bold/001
ls

This directory previously held only f.nii.gz, workmem.par, and wmfir.par before preprocessing. Now there are a lot of files, each indicative of a different preprocessing stage. Now type 

ls -ltr

This command sorts the files by creation time with the oldest at the top and the newest at the bottom. The preprocessing is progressive, meaning that the output of one stage is the input to the next.

4.1. Template

This stage creates template.nii.gz (and template.log). This is the middle time point from the raw functional data (f.nii.gz). This is the reference used to motion correct and register the functionals for this run to the anatomical. It is also used to create masks of the brain.

4.2. Masking

The masks for this run are stored in the 'masks' directory. Run 'ls -ltr masks'. You will see a file called 'brain.nii.gz'. This is a binary mask created using the FSL BET program. There is also a file called 'brain.e3.nii.gz' which is the mask eroded by three voxels. These have the same dimensions as the template. View the masks with: 

freeview -v template.nii.gz masks/brain.nii.gz:colormap=heat -viewport coronal

freeview -v template.nii.gz masks/brain.e3.nii.gz:colormap=heat -viewport coronal

You can play with the opacity of the mask by moving the opacity slider or entering a new value less than 1 and hitting enter. The brain.nii.gz is used to constrain voxel-wise operations. The eroded mask (brain.e3.nii.gz) is used to compute the mean functional value used for intensity normalization and global mean time course. There are other masks there that we will get to later.

4.3. Intensity Normalization and Global Mean Time Course

By default, FSFAST will scale the intensities of all voxels and time points to help assure that they are of the same value across runs, sessions, and subjects. It does this by dividing by the mean across all voxels and time points inside the brain.e3.nii.gz mask, then multiplying by 100. This value is stored in global.meanval.dat. This is a simple text file which you can view. At this point, this value is stored and used later. A waveform is also constructed of the mean at each time point (text file global.waveform.dat). This can be used as a nuisance regressor.

4.4. Functional-Anatomical Cross-modal Registration

The next six files (init.register.dof6.dat, register.dof6.dat, register.dof6.dat.mincost, register.dof6.dat.sum, register.dof6.dat.log, register.dof6.dat.param) deal with the registration from the functional to the same-subject FreeSurfer anatomical. There are only two files here that are really important: register.dof6.dat and register.dof6.dat.mincost.

The registration is will be revisited below when we talk about Quality Assurance

4.5. Motion Correction (MC)

The motion correction stage produces these files: fmcpr.mat.aff12.1D, fmcpr.nii.gz, mcprextreg, mcdat2extreg.log, fmcpr.nii.gz.mclog, fmcpr.mcdat. There are only three important file here:

4.6. Slice-Timing Correction (STC)

Slice-timing correction compensates for the fact that each of the 30 slices was acquired separately over the course of 2 sec. It does this by interpolating between time points to align each slice to the time of the middle of the TR. The file created with this is fmcpr.up.nii.gz (and fmcpr.up.nii.gz.log).

4.7. Resampling to Common Spaces and Spatial Smoothing

At this point, the functional data has stayed in the 'native functional space', ie, 64x64x30, 3.4x3.4x5mm3. Now it will be sampled into the 'Common Space'. The Common Space is a geometry where all subjects are in voxel-for-voxel registration. There are three such spaces in FSFAST:

Each of these is the entire 4D functional data set resampled into the common space. The spatial smoothing is performed after resampling. Surface-based (2D) smoothing is used for the surfaces; 3D for the volumes. Check the dimensions of the MNI305 space volume: 

mri_info --dim fmcpr.up.sm5.mni305.2mm.nii.gz
mri_info --res fmcpr.up.sm5.mni305.2mm.nii.gz

The dimension will be '76 76 93 142' meaning that there are 76 columns, 76 rows, 93 slices but still 142 time points (same as the raw data). The resolution will be '2.0 2.0 2.0 2000' meaning that each voxel is 2mm in size and the TR is still 2 sec. The transformation to this space is based on the 12DOF talairach.xfm created during the FreeSurfer reconstruction. Check the dimensions of the Left Hemisphere 'volume': 

mri_info --dim fmcpr.up.sm5.fsaverage.lh.nii.gz
mri_info --res fmcpr.up.sm5.fsaverage.lh.nii.gz

The dimension is '163842 1 1 142'. This 'volume' has 163842 'columns', 1 'row', and 1 'slice' (still 142 time points). You are probably confused right now. That's ok, it's natural. At this point the notion of a 'volume' has been lost. Each 'voxel' is actually a vertex (of which there are 163842 in the left hemisphere of fsaverage). Storing it in a NIFTI 'volume' is just a convenience. The 'resolution' is '1.0 1.0 1.0 2000'. The values for the first 3 dimensions are meaningless because there are no columns, rows, or slices on the surface so the distances between them are meaningless. The last value indicates the time between frames and is still accurate (2 sec). The transformation to this space is based on the surface-based intersubject registration created during the FreeSurfer reconstruction.
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