| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
|
[wiki:Self:FsTutorial top] | [wiki:Self:FsTutorial/FslFeatFreeSurfer previous] == Overlaying FSL Feat statistical maps and Higher-Level Analysis == *To follow this exercise exactly be sure you've downloaded the [wiki:Self:FsTutorial/Data tutorial data set] before you begin. If you choose not to download the data set you can follow these instructions on your own data, but you will have to substitute your own specific paths and subject names. In this exercise, the data set of subject '''bert''' is used for demonstration. To begin the exercises, first enter the following directory, and then set the current directory to be the default subjects directory using this command: {{{ cd $FREESURFER_HOME/subjects/buckner_data/tutorial_subjs setenv SUBJECTS_DIR ${PWD} }}} === 1.0 Overlaying the statistical map onto the bert's orig volume === Use the following command to display the zmap (zstat1.nii.gz) from the first run overlaid onto the bert's orig volume. It will display the automatic segmentation, and will also set the threshold at z = 2.3: {{{ tkmedit bert orig.mgz lh.white -aux brain.mgz \ -overlay ./fbert1.feat/stats/zstat1.nii.gz \ -overlay-reg ./fbert1.feat/reg/freesurfer/anat2exf.register.dat \ -segmentation aparc+aseg.mgz -fthresh 2.3 -fmax 4.3 }}} You should see the image below:[[BR]] attachment:tkm-zstat1-cor-128.th23.small.jpg[[BR]] When you click or mouse over a voxel, the cortical or subcortical structure that that voxel belongs to will be displayed in the control panel. You can view any of the volumes in the stats dir in this way as well as the clustered maps in the feat directory. === 2.0 View statistical maps on bert's surface === To view any of the statistical maps on bert's surface, close the tkmedit GUI (or open a new terminal window) and run: {{{ tksurfer bert lh inflated \ -overlay ./fbert1.feat/stats/zstat1.nii.gz \ -overlay-reg ./fbert1.feat/reg/freesurfer/anat2exf.register.dat \ -fthresh 2.3 -fmid 3.3 -fslope 1 -annot aparc.annot }}} Change the cortical parcellation to outline mode with View->LabelStyle->Outline. You should see the image below:[[BR]] attachment:tks-zstat1-rh-lat.th23.small.jpg [[BR]] When you click or mouse over a vertex, the control panel will display the name of the cortical structure. You can view any of the volumes in the stats dir in this way as well as the clustered maps in the feat directory. You can also run the tkmedit and tksurfer commands above in separate shells and use the Save-Point/Goto-Point functionality to navigate through the volume and surface. === 3.0 Displaying Same-Subject, Cross-Run GFEAT Results === Typically, one collects more than one run/series of functtional data for each subject. The individual runs are analyzed separately, then combined in standard space with GFEAT using a fixed-effects model. Since the data are no longer in the subject's native functional space, a different registration matrix is needed to map the GFEAT results to the individual. Each run of reg-feat2anat will create a reg/freesurfer/anat2std.register.dat. Any one of these can be used to map the GFEAT data to the subject's anatomy. First, verify that the registration is good with: {{{ tkregister2 --mov fbert.gfeat/mean_func.nii.gz --surf \ --reg fbert1.feat/reg/freesurfer/anat2std.register.dat }}} mean_func.nii.gz is the mean of the example_func's in standard space. Now show gfeat results on anatomical volume: {{{ tkmedit bert orig.mgz -seg aparc+aseg.mgz \ -ov fbert.gfeat/cope1.feat/stats/zstat1.nii.gz \ -ovreg fbert1.feat/reg/freesurfer/anat2std.register.dat \ -fthresh 2.3 -fmax 4.3 }}} Here we've used the anat2std.register.dat from the first run. Now show gfeat results on the surface: {{{ tksurfer bert lh inflated -annot aparc.annot \ -ov fbert.gfeat/cope1.feat/stats/zstat1.nii.gz \ -ovreg fbert1.feat/reg/freesurfer/anat2std.register.dat \ -fthresh 2.3 -fmid 3.3 -fslope 1 }}} === 3.0 Resampling COPEs to common surface and Higher-Level Analysis === When performing surface-based group analysis of functional data, the COPEs must be sampled to the common surface space (similar to how you must reslice the COPEs from the native functional space into standard space prior to group analysis). The surface sampling can be done in two ways. First, you can use feat2surf as in: {{{ feat2surf --feat fbert1.feat --cope-only }}} This will create fbert.feat/reg_surf-lh-fsaverage/stats for the left hemi (and another for the right). There will be cope1.nii.gz. This looks like a volume because it is in nifti format, but it is really a surface stored in a volume format (note it's dimensions are 1974 x 1 x 83 = 163842 = number of vertices in fsaverage's surface). You can then concatenate these files from different subjects to perform group analysis with FreeSurfer's mri_glmfit or FSL's randomise or flame. Alternatively, when you have multiple runs/subjects, you can use mris_preproc, something like: {{{ mris_preproc --target fsaverage --hemi lh --out xrun/lh.cope1.nii.gz \ --iv fbert1.feat/stats/cope1.nii.gz fbert1.feat/reg/freesurfer/anat2exf.register.dat \ --iv fbert2.feat/stats/cope1.nii.gz fbert2.feat/reg/freesurfer/anat2exf.register.dat }}} This will create the output directory (xrun) and lh.cope1.nii.gz, the copes from each run sampled onto the left hemi of the common surface. In this case it will have only two frames for the two runs. This can then be used as input mri_glmfit, randomise, or flame. You can also resample the variances of the copes with: {{{ mris_preproc --target fsaverage --hemi lh --out xrun/lh.varcope1.nii.gz \ --iv fbert1.feat/stats/varcope1.nii.gz fbert1.feat/reg/freesurfer/anat2exf.register.dat \ --iv fbert2.feat/stats/varcope1.nii.gz fbert2.feat/reg/freesurfer/anat2exf.register.dat }}} The command below can be used to run a fixed-effects analysis across both runs. Normally, you would probably run a random effects or weight random effects analysis, but since we only have two runs here, we'll use a fixed-effects. The dof=163 is the sum of the dofs from fbert?.feat/stats/dof. The model here is a simple one-sample group mean (OSGM) in which the desgin matrix is simply a column of 1s. For more elaborate designs, use an FSGD file instead of --osgm. {{{ mri_glmfit --y xrun/lh.cope1.nii.gz --yffxvar xrun/lh.varcope1.nii.gz --ffxdof 163 \ --osgm --glmdir xrun/lh.osgm.ffx --surf fsaverage lh --label lh.cortex.label }}} To view the higher-level results, run: {{{ tksurfer fsaverage lh inflated -overlay xrun/lh.osgm.ffx/osgm/sig.mgh \ -fthresh 4 -fmid 5 -fslope 1 -annot aparc.annot }}} There are several differences between this invokation of tksurfer and the one further above. * The subject is fsaverage, not bert * The input is a sig map, which is -log10(p) * The thresholds are significance thresholds, not z. So, 4 means p < .0001. |
|
| Line 123: | Line 7: |
| [wiki:Self:FsTutorial top] | [wiki:Self:FsTutorial/FslFeatFreeSurfer previous] | |
| Line 124: | Line 9: |
| === 1.0 Resampling COPEs to common surface === | |
| Line 125: | Line 11: |
|
In order to perform surface-based group analysis, you need to have all the copes sampled into a common space. This is done with mris_preproc: |
|
| Line 126: | Line 14: |
|
{{{ mris_preproc --target fsaverage --hemi lh --fwhm 5 --out xrun/lh.cope1.nii.gz \ --iv fbert1.feat/stats/cope1.nii.gz fbert1.feat/reg/freesurfer/anat2exf.register.dat \ --iv fbert2.feat/stats/cope1.nii.gz fbert2.feat/reg/freesurfer/anat2exf.register.dat }}} |
|
| Line 127: | Line 20: |
|
In this case, the common space is the left hemisphere of fsaverage. Surface-based smoothing of 5mm FWHM is used. The output lh.cope1.nii.gz looks like a volume because it is in nifti format, but it is really a surface stored in a volume format (note it's dimensions are 1974 x 1 x 83 = 163842 = number of vertices in fsaverage's surface). It has 2 frames, one for each run. |
|
| Line 128: | Line 27: |
|
Note that each volume (cope1.nii.gz) is accompanied by a registration matrix (anat2exf.register.dat). The above example extracts data from each run of the same subject. If you have multiple subjects, you can specify the gfeat/cope1/stats/cope1.nii.gz paired with the anat2std.register.dat matrix. You can also extract the cope variances (varcopes) in the same way: {{{ mris_preproc --target fsaverage --hemi lh --fwhm 5 --out xrun/lh.varcope1.nii.gz \ --iv fbert1.feat/stats/varcope1.nii.gz fbert1.feat/reg/freesurfer/anat2exf.register.dat \ --iv fbert2.feat/stats/varcope1.nii.gz fbert2.feat/reg/freesurfer/anat2exf.register.dat }}} === 2.0 Surface-based Group Analysis === Once you have the copes in the common surface space, the (nearly) same as you would with a thickness study. With a functional study, you also have the flexibility to run fixed- and weighted random-effects analysis. The command below can be used to run a fixed-effects analysis across both runs. Normally, you would probably run a random effects or weight random effects analysis, but since we only have two runs here, we'll use a fixed-effects. The dof=163 is the sum of the dofs from fbert?.feat/stats/dof. The model here is a simple one-sample group mean (OSGM) in which the design matrix is simply a column of 1s. For more elaborate designs, use an FSGD file instead of --osgm. {{{ mri_glmfit --y xrun/lh.cope1.nii.gz --yffxvar xrun/lh.varcope1.nii.gz --ffxdof 163 \ --osgm --glmdir xrun/lh.osgm.ffx --surf fsaverage lh --label lh.cortex.label }}} This will create a directory called xrun/lh.osgm.ffx: {{{ ls xrun/lh.osgm.ffx ar1.mgh -- spatial AR1 map beta.mgh -- regression coefficients (same as PEs) fwhm.dat -- estimate of spatial FWHM (not valid with 2 data points) mask.mgh -- binary mask (if used) mri_glmfit.log -- log file osgm -- contrast directory rstd.mgh -- residual standard deviation rvar.mgh -- residual standard variance Xg.dat -- design matrix (text) X.mat -- design matrix (matlab) }}} The most important results are in the contrast directory: {{{ ls xrun/lh.osgm.ffx/osgm C.dat - contrast matrix cnr.mgh - contrast-to-noise ratio F.mgh - F ratio gamma.mgh - contrast size (same as COPE) maxvox.dat - info about the vertex with max F stat sig.mgh - significance map (-log10(p)) }}} To view the higher-level results, run: {{{ tksurfer fsaverage lh inflated -overlay xrun/lh.osgm.ffx/osgm/sig.mgh \ -fthresh 4 -fmid 5 -fslope 1 -annot aparc.annot }}} There are several differences between this invocation of tksurfer and the one used during the individual analysis before: * The subject is fsaverage, not bert * The input is a sig map, which is -log10(p) * The thresholds are significance thresholds, not z. So, 4 means p < .0001. === 3.0 Alternative method to resample COPEs to common surface === This command will resample the copes to the common surface space, but does not concatenate them together as mris_preproc does. Rather, it stores the result in fbert1.feat/reg_surf-lh-fsaverage/stats for the left (reg_surf-rh-fsaverage for right). First, you can use feat2surf: {{{ feat2surf --feat fbert1.feat --cope-only }}} |
[wiki:FsTutorial top] | [wiki:FsTutorial/FslFeatFreeSurfer previous]
1.0 Resampling COPEs to common surface
In order to perform surface-based group analysis, you need to have all the copes sampled into a common space. This is done with mris_preproc:
mris_preproc --target fsaverage --hemi lh --fwhm 5 --out xrun/lh.cope1.nii.gz \ --iv fbert1.feat/stats/cope1.nii.gz fbert1.feat/reg/freesurfer/anat2exf.register.dat \ --iv fbert2.feat/stats/cope1.nii.gz fbert2.feat/reg/freesurfer/anat2exf.register.dat
In this case, the common space is the left hemisphere of fsaverage. Surface-based smoothing of 5mm FWHM is used. The output lh.cope1.nii.gz looks like a volume because it is in nifti format, but it is really a surface stored in a volume format (note it's dimensions are 1974 x 1 x 83 = 163842 = number of vertices in fsaverage's surface). It has 2 frames, one for each run.
Note that each volume (cope1.nii.gz) is accompanied by a registration matrix (anat2exf.register.dat). The above example extracts data from each run of the same subject. If you have multiple subjects, you can specify the gfeat/cope1/stats/cope1.nii.gz paired with the anat2std.register.dat matrix.
You can also extract the cope variances (varcopes) in the same way:
mris_preproc --target fsaverage --hemi lh --fwhm 5 --out xrun/lh.varcope1.nii.gz \ --iv fbert1.feat/stats/varcope1.nii.gz fbert1.feat/reg/freesurfer/anat2exf.register.dat \ --iv fbert2.feat/stats/varcope1.nii.gz fbert2.feat/reg/freesurfer/anat2exf.register.dat
2.0 Surface-based Group Analysis
Once you have the copes in the common surface space, the (nearly) same as you would with a thickness study. With a functional study, you also have the flexibility to run fixed- and weighted random-effects analysis.
The command below can be used to run a fixed-effects analysis across both runs. Normally, you would probably run a random effects or weight random effects analysis, but since we only have two runs here, we'll use a fixed-effects. The dof=163 is the sum of the dofs from fbert?.feat/stats/dof. The model here is a simple one-sample group mean (OSGM) in which the design matrix is simply a column of 1s. For more elaborate designs, use an FSGD file instead of --osgm.
mri_glmfit --y xrun/lh.cope1.nii.gz --yffxvar xrun/lh.varcope1.nii.gz --ffxdof 163 \
--osgm --glmdir xrun/lh.osgm.ffx --surf fsaverage lh --label lh.cortex.labelThis will create a directory called xrun/lh.osgm.ffx:
ls xrun/lh.osgm.ffx ar1.mgh -- spatial AR1 map beta.mgh -- regression coefficients (same as PEs) fwhm.dat -- estimate of spatial FWHM (not valid with 2 data points) mask.mgh -- binary mask (if used) mri_glmfit.log -- log file osgm -- contrast directory rstd.mgh -- residual standard deviation rvar.mgh -- residual standard variance Xg.dat -- design matrix (text) X.mat -- design matrix (matlab)
The most important results are in the contrast directory:
ls xrun/lh.osgm.ffx/osgm C.dat - contrast matrix cnr.mgh - contrast-to-noise ratio F.mgh - F ratio gamma.mgh - contrast size (same as COPE) maxvox.dat - info about the vertex with max F stat sig.mgh - significance map (-log10(p))
To view the higher-level results, run:
tksurfer fsaverage lh inflated -overlay xrun/lh.osgm.ffx/osgm/sig.mgh \ -fthresh 4 -fmid 5 -fslope 1 -annot aparc.annot
There are several differences between this invocation of tksurfer and the one used during the individual analysis before:
- The subject is fsaverage, not bert
- The input is a sig map, which is -log10(p)
The thresholds are significance thresholds, not z. So, 4 means p < .0001.
3.0 Alternative method to resample COPEs to common surface
This command will resample the copes to the common surface space, but does not concatenate them together as mris_preproc does. Rather, it stores the result in fbert1.feat/reg_surf-lh-fsaverage/stats for the left (reg_surf-rh-fsaverage for right).
First, you can use feat2surf:
feat2surf --feat fbert1.feat --cope-only