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[[FreeSurferWiki|top]] | [[Tutorials|previous]] | [[FsFastTutorialV5.1|FSFAST Tutorial Top]]   Group Level Analysis
[[FreeSurferWiki|top]] | [[Tutorials|previous]] | [[FsFastTutorialV5.1|FSFAST Tutorial Top]]
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In general, the group analysis for fMRI is very similar to that of the
structural data. There is a tutorial for this at GroupAnalysis. There
are several specific differences for fMRI which are described here. In
the surface-based GroupAnalysis, you would run mris_preproc to create
a single file with a 'stack' of all of your subjects (one subject for
each frame) in the common surface space, smoothed the data on the
surface, then run mri_glmfit.
= Group Level Analysis =
In general, the group analysis for fMRI is very similar to that of the structural data. There is a tutorial for this at GroupAnalysis. There are several specific differences for fMRI which are described here.
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For the fMRI, the analyzed data are already in the common space and
smoothed. You will need to
In the '''surface-based''' GroupAnalysis, you would:
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 * Concatenate them into one file  1. Run mris_preproc to resample each subject into the common space and then concatenate all of your subjects (one subject for each frame) into one file.
 1. Smoothed the data on the surface, then
 1. Run mri_glmfit and mri_glmfit-sim

For the ''__fMRI__'' you will need to:

 * Concatenate them into one file
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In the structural stream (see GroupAnalysis), the subject's data were
concatenated into one file with mris_preproc . For the functional
stream, the program is called isxconcat-sess:
In the structural stream (see GroupAnalysis), the subject's data were concatenated into one file with mris_preproc . For the functional stream, the program is called isxconcat-sess:
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When this is complete, a directory called 'group' will be created. cd into this directory and see what's there:
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When this is complete, a directory called 'group' will be created. cd into
this directory and see what's there:
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 * sess.info.txt : other information about each session   * sess.info.txt : other information about each session
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Each of the volumes is in the output space, as can be verified with
mri_info.
Each of the volumes is in the output space, as can be verified with mri_info.
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Details on how to run the GLM are given in GroupAnalysis, including
the use of FSGD files to construct complicated group-level design
matrices. Here we are going to use a very simple design in which test
whether the mean across the groups equals 0 (the One Sample Group
Mean, or OSGM). This just requires a design matrix with a single
column of all ones (created with the --osgm flag):
Details on how to run the GLM are given in GroupAnalysis, including the use of FSGD files to construct complicated group-level design matrices. Here we are going to use a very simple design in which test whether the mean across the groups equals 0 (the One Sample Group Mean, or OSGM). This just requires a design matrix with a single column of all ones (created with the --osgm flag):
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mri_glmfit --y ces.nii.gz --wls cesvar.nii.gz --osgm --surface fsaverage lh --glmdir my-glm.wls  mri_glmfit --y ces.nii.gz --wls cesvar.nii.gz --osgm --surface fsaverage lh --glmdir my-glm.wls
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The one difference between this and the call in the structrual steam
is the presence of the '--wls cesvar.nii.gz' option. cesvar.nii.gz is
the variance of each session at each voxel. This is used to de-weight
a session with high variance. This is not a true mixed effects
analysis (this has been referred to as 'psuedo mixed effects'; see
Thirion, 2007, Neuroimage). This step is not performed in the
structural stream because we do not have variance information for each
subject. 
The one difference between this and the call in the structrual steam is the presence of the '--wls cesvar.nii.gz' option. cesvar.nii.gz is the variance of each session at each voxel. This is used to de-weight a session with high variance. This is not a true mixed effects analysis (this has been referred to as 'psuedo mixed effects'; see Thirion, 2007, Neuroimage). This step is not performed in the structural stream because we do not have variance information for each subject.
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The correction is the same as for the structural group analysis. For
example, run:
The correction is the same as for the structural group analysis. For example, run:
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Will find clusters defined by a cluster-wise threshold of 2 (p<.01)
with a positive sign. 
Will find clusters defined by a cluster-wise threshold of 2 (p<.01) with a positive sign.
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Perform the same operations above for the right hemisphere (ie, go into workmem.sm05.rh):
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Perform the same operations above for the right hemisphere (ie, go
into workmem.sm05.rh):
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Perform the same operations above for the MNI 305 space analysis (ie, go
into workmem.sm05.mni305). There are a couple of things that are
different about this analysis.
Perform the same operations above for the MNI 305 space analysis (ie, go into workmem.sm05.mni305). There are a couple of things that are different about this analysis.
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This directory has the same files as the surface-based results, though
their dimensions are different. All the volumes here are true volumes.
There is an addition file that is not in the surface-based results:
This directory has the same files as the surface-based results, though their dimensions are different. All the volumes here are true volumes. There is an addition file that is not in the surface-based results:
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This is a mask that only covers the subcortical structures. This will
be used to help prevent the re-analysis of cortical structures.
This is a mask that only covers the subcortical structures. This will be used to help prevent the re-analysis of cortical structures.
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The mri_glmfit command is the same as for the surface-based analysis
but without the (--surface fsaverage lh) part and with the
specification of a mask
The mri_glmfit command is the same as for the surface-based analysis but without the (--surface fsaverage lh) part and with the specification of a mask

top | previous | FSFAST Tutorial Top

1. Group Level Analysis

In general, the group analysis for fMRI is very similar to that of the structural data. There is a tutorial for this at GroupAnalysis. There are several specific differences for fMRI which are described here.

In the surface-based GroupAnalysis, you would:

  1. Run mris_preproc to resample each subject into the common space and then concatenate all of your subjects (one subject for each frame) into one file.
  2. Smoothed the data on the surface, then
  3. Run mri_glmfit and mri_glmfit-sim

For the fMRI you will need to:

  • Concatenate them into one file
  • Do not smooth (already smoothed during first-level analysis)
  • Run mri_glmfit using weighted least squares (WLS)
  • Correct for multiple comparisons
  • Perform the above in each space (lh, rh, and mni305)
  • Correct for multiple comparisons across the three spaces
  • Optionally merge the three spaces into one volume space

2. Concatenating the Data

In the structural stream (see GroupAnalysis), the subject's data were concatenated into one file with mris_preproc . For the functional stream, the program is called isxconcat-sess:

isxconcat-sess -sf sessidlist -analysis workmem.sm05.lh -contrast encode-v-base -o group
  • -sf sessidlist : use all the subjects listed in sessidlist (order is important!)
  • -analysis workmem.sm05.lh : analysis from mkanalysis-sess and selxavg3-sess
  • -contrast encode-v-base : contrast from mkcontrast-sess
  • -o group : output folder is called 'group'
  • -all-contrasts can be used instead of -contrast

Run the concatenation for the right hemisphere and mni305 spaces

isxconcat-sess -sf sessidlist -analysis workmem.sm05.rh -contrast encode-v-base -o group
isxconcat-sess -sf sessidlist -analysis workmem.sm05.mni305 -contrast encode-v-base -o group

When this is complete, a directory called 'group' will be created. cd into this directory and see what's there:

cd $FSFTUTDIR/group
ls
  • grouplist.txt : list of the sessions
  • subjectlist.txt : list of the corresponding FreeSurfer subject IDs

  • sess.info.txt : other information about each session
  • workmem.sm05.lh - left hemisphere analysis output folder
  • workmem.sm05.rh - right hemisphere analysis output folder
  • workmem.sm05.mni305 - MNI 305 analysis output folder

Go into the workmem.sm05.lh and see what's there:

cd $FSFTUTDIR/group/workmem.sm05.lh
ls

You will see several files and folders:

  • analysis.info - copy of the analysis.info created by mkanalysis-sess
  • meanfunc.nii.gz - a stack of the mean functional images for each session
  • masks.nii.gz - a stack of the masks of all the individual subjects
  • mask.nii.gz - a single mask based on the intersection of all masks
  • fsnr.nii.gz - a stack of the functional SNRs for each session
  • ffxdof.dat - text file with the total number of DOF summed over all sessions
  • encode-v-base - group contrast folder

Each of the volumes is in the output space, as can be verified with mri_info.

Go into the contrast folder and see what's there:

cd $FSFTUTDIR/group/workmem.sm05.lh/encode-v-base
ls
  • ces.nii.gz - stack of all the contrast values from the lower level, one for each session
  • cesvar.nii.gz - stack of all the contrast variances from the lower level, one for each session

These are going to be the inputs for the group GLM analysis.

3. Running the GLM

Details on how to run the GLM are given in GroupAnalysis, including the use of FSGD files to construct complicated group-level design matrices. Here we are going to use a very simple design in which test whether the mean across the groups equals 0 (the One Sample Group Mean, or OSGM). This just requires a design matrix with a single column of all ones (created with the --osgm flag):

mri_glmfit --y ces.nii.gz --wls cesvar.nii.gz --osgm --surface fsaverage lh --glmdir my-glm.wls
  • --y ces.nii.gz : the input values to analyze
  • --wls cesvar.nii.gz : variance weighting
  • --osgm : use One-Sample Group Mean
  • --surface fsaverage lh : indicates surface based data (not used for volume data)
  • --glmdir my-glm.wls : output directory

The one difference between this and the call in the structrual steam is the presence of the '--wls cesvar.nii.gz' option. cesvar.nii.gz is the variance of each session at each voxel. This is used to de-weight a session with high variance. This is not a true mixed effects analysis (this has been referred to as 'psuedo mixed effects'; see Thirion, 2007, Neuroimage). This step is not performed in the structural stream because we do not have variance information for each subject.

4. Visualizing the GLM

tksurfer fsaverage lh inflated -aparc -overlay glm.wls/osgm/sig.nii.gz

5. Correct for Multiple Comparisons

The correction is the same as for the structural group analysis. For example, run:

mri_glmfit-sim --glmdir my-glm.wls --cache 2 pos

Will find clusters defined by a cluster-wise threshold of 2 (p<.01) with a positive sign.

6. Right Hemisphere

Perform the same operations above for the right hemisphere (ie, go into workmem.sm05.rh):

cd $FSFTUTDIR/group/workmem.sm05.rh

7. Subcortical (MNI 305 Space)

Perform the same operations above for the MNI 305 space analysis (ie, go into workmem.sm05.mni305). There are a couple of things that are different about this analysis.

cd $FSFTUTDIR/group/workmem.sm05.mni305
ls

This directory has the same files as the surface-based results, though their dimensions are different. All the volumes here are true volumes. There is an addition file that is not in the surface-based results:

  • subcort.mask.nii.gz

This is a mask that only covers the subcortical structures. This will be used to help prevent the re-analysis of cortical structures.

tkmedit fsaverage orig.mgz -aparc+aseg -overlay subcort.mask.nii.gz -fthresh 0.5

The mri_glmfit command is the same as for the surface-based analysis but without the (--surface fsaverage lh) part and with the specification of a mask

cd workmem.sm05.mni305/encode-v-base
mri_glmfit --y ces.nii.gz --wls cesvar.nii.gz --osgm  --glmdir glm.wls --mask ../subcort.mask.nii.gz
tkmedit fsaverage orig.mgz -aparc+aseg -overlay glm.wls/osgm/sig.nii.gz

FsFastTutorialV5.1/FsFastGroupLevel (last edited 2017-02-21 15:55:10 by 172)