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Computes seeds (regressors) that can be used for functional connectivity analysis or for use as nuisance regressors. Seed regions can be defined in two ways: (1) as an anatomical region in a segmentation such as aparc+aseg, or (2) as an ROI created with funcroi-config. The seed regions are always subject-specific. The output is a text file in the same directory as the raw data. This file will be named based on the -o flag. |
Computes seeds (regressors) that can be used for functional connectivity analysis or for use as nuisance regressors. Seed regions can be defined in two ways: (1) as an anatomical region in a segmentation such as aparc+aseg, or (2) as an ROI created with funcroi-config. The seed regions are always subject-specific. The output is a text file in the same directory as the raw data. This file will be named based on the -o flag. |
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For segmentation-based, the segmentation must exist in $SUBJECTS_DIR/$subject/mri. By default the segmentation is aparc+aseg. This can be changed with -seg (eg, -seg aparc+aseg would be the same as the default). You must specify a segmentation index with -segid. Eg, if you are using aparc+aseg, then 17 would be left hippocampus (this is defined in $FREEESURFER_HOME/FreeSurferColorLUT.txt). You can specify any number of segmentations; they will be combined into one seed region (eg, (-segid 17 -segid 53 would produce one seed region from both hippocampi). |
For segmentation-based, the segmentation must exist in $SUBJECTS_DIR/$subject/mri. By default the segmentation is aparc+aseg. This can be changed with -seg (eg, -seg aparc+aseg would be the same as the default). You must specify a segmentation index with -segid. Eg, if you are using aparc+aseg, then 17 would be left hippocampus (this is defined in $FREEESURFER_HOME/FreeSurferColorLUT.txt). You can specify any number of segmentations; they will be combined into one seed region (eg, (-segid 17 -segid 53 would produce one seed region from both hippocampi). |
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The segmentation will be converted from the 1mm anatomical space into the native functional space. For this, you can specify a fill threshold. This governs how much an anatomical segmentation must fill a functional voxel must be in order for it to be considered part of the seed region. This is a number between 0 (the smallest part of a voxel) to 1 (all of the voxel). To avoid quatifification artifacts, it is recommended that this not be set above .8. Default is .5. |
The segmentation will be converted from the 1mm anatomical space into the native functional space. For this, you can specify a fill threshold. This governs how much an anatomical segmentation must fill a functional voxel must be in order for it to be considered part of the seed region. This is a number between 0 (the smallest part of a voxel) to 1 (all of the voxel). To avoid quatifification artifacts, it is recommended that this not be set above .8. Default is .5. |
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There are two default segmentations: (1) white matter (-wm) and (2) ventricular CSF (-vcsf). The white matter option first creates a mask of the WM in the anatomical space by finding the voxels in the aparc+aseg.mgz with indices 2 and 41. It then erodes the mask by 3 voxels. It then converts the mask to native functional space with fillthresh=0.5 The CSF segmentation uses segmentation indices 4 5 14 43 44 31 and 63 with fillthresh=.75. Both use a PCA output. These are good to use as nuisance regressors for functional connectivity analysis. |
There are two default segmentations: (1) white matter (-wm) and (2) ventricular CSF (-vcsf). The white matter option first creates a mask of the WM in the anatomical space by finding the voxels in the aparc+aseg.mgz with indices 2 and 41. It then erodes the mask by 3 voxels. It then converts the mask to native functional space with fillthresh=0.5 The CSF segmentation uses segmentation indices 4 5 14 43 44 31 and 63 with fillthresh=.75. Both use a PCA output. These are good to use as nuisance regressors for functional connectivity analysis. |
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Create a seed waveform by spatially averaging the entire left hemisphere hippocampus: |
Create a seed waveform by spatially averaging the entire left hemisphere hippocampus: |
Index
Contents
Name
Computes seeds (regressors) that can be used for functional connectivity analysis or for use as nuisance regressors.
NOTE: this program is still experimental. Use at your own risk!
Synopsis
fcseed-sess -segid <SegID#> -fillthresh 0.5 -s bert -mean
Arguments
Required Flagged Arguments
-sf sessidfile |
supply text file with list of subjects |
-df srchdirfile |
search in this dir for subjects |
-s sessid |
single subject processing |
-d srchdir |
search in this dir for single subject |
-fsd fsdir name |
dir name for location of bold data & analyses within subjectdir |
Optional Flagged Arguments
-seg segid <-segid segid2 ...> |
use FreeSurfer segmentation as seed |
use FreeSurfer Segmentation IDs for common ROIs (found in $FREESURFER_HOME/FreesurferColorLUT.txt) |
-wm |
all white matter as seed (erroded by 3 voxels) |
Useful to use as nuisance regressor time-course |
-vcsf |
ventricles & Cerebrospinal fluid as seed |
Useful to use as nuisance regressor time-course |
-m |
maskfile |
output mask for segmentation-based. Good for checking |
-overwrite |
overwrite |
delete and overwrite any existing files |
-mean |
use mean |
compute spatial mean seed region time-course for seed region |
-pca |
use pca |
compute principal component analysis for seed instead of spatial mean. seed.dat file will contain one component time-course per row |
-roi |
roiconfig |
as created by funcroi-confg |
-version |
print version |
|
-help |
print help text |
using -roi flag: ROI-based Seed Regions
The ROI-based seed region is the result of a functional ROI analysis (see funcroi-config). Note that the functional ROI may have a different FSD than the functional connectivity analysis. This can be helpful when creating an ROI from a task but applying it to rest data.
Outputs
seedregion.dat |
time course data from seed region |
seedregion.log |
fcseed-sess run log |
Description
Computes seeds (regressors) that can be used for functional connectivity analysis or for use as nuisance regressors. Seed regions can be defined in two ways: (1) as an anatomical region in a segmentation such as aparc+aseg, or (2) as an ROI created with funcroi-config. The seed regions are always subject-specific. The output is a text file in the same directory as the raw data. This file will be named based on the -o flag.
For segmentation-based, the segmentation must exist in $SUBJECTS_DIR/$subject/mri. By default the segmentation is aparc+aseg. This can be changed with -seg (eg, -seg aparc+aseg would be the same as the default). You must specify a segmentation index with -segid. Eg, if you are using aparc+aseg, then 17 would be left hippocampus (this is defined in $FREEESURFER_HOME/FreeSurferColorLUT.txt). You can specify any number of segmentations; they will be combined into one seed region (eg, (-segid 17 -segid 53 would produce one seed region from both hippocampi).
The segmentation will be converted from the 1mm anatomical space into the native functional space. For this, you can specify a fill threshold. This governs how much an anatomical segmentation must fill a functional voxel must be in order for it to be considered part of the seed region. This is a number between 0 (the smallest part of a voxel) to 1 (all of the voxel). To avoid quatifification artifacts, it is recommended that this not be set above .8. Default is .5.
There are two default segmentations: (1) white matter (-wm) and (2) ventricular CSF (-vcsf). The white matter option first creates a mask of the WM in the anatomical space by finding the voxels in the aparc+aseg.mgz with indices 2 and 41. It then erodes the mask by 3 voxels. It then converts the mask to native functional space with fillthresh=0.5 The CSF segmentation uses segmentation indices 4 5 14 43 44 31 and 63 with fillthresh=.75. Both use a PCA output. These are good to use as nuisance regressors for functional connectivity analysis.
Examples
Example 1
- Create a seed waveform by spatially averaging the entire left hemisphere hippocampus:
- fcseed-sess -o lh.hippo.dat -segid 17 -s session -fsd rest
- This will create files called lh.hippo.dat in session/rest/RRR where RRR is the run directory.
Example 2
- Create white matter and ventricular CSF nuisance regressors
- fcseed-sess -o wm.dat -wm -s session -fsd rest fcseed-sess -o vcsf.dat -vcsf -s session -fsd rest
Analysis Example
First, create an analysis folder and setup file using mkanalysis-sess
i.e.:
- mkanalysis-sess -a fc-lh.hippo.rhemi
- -notask -taskreg lh.hippo.dat 1 -nuisreg wm.dat 3 -nuisreg vcsf.dat 3 -surface fsaverage rh -fwhm 5 -fsd rest -TR 2
- This analysis is called "fc-lh.hippo.rhemi". It uses the single waveform found in lh.hippo.dat as the "task regressor". It also adds 3 PCA waveforms from both the white matter and the CSF as nuisance regressors. Note that a contrast does not need to be made because one is automatically created with an -taskreg. This data can be analyzed with selxavg3-sess and isxconcat-sess just as if it were any task-based analysis.
Bugs
None
See Also
Links
Methods Description
description description
References
Reporting Bugs
Report bugs to < analysis-bugs@nmr.mgh.harvard.edu >