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!


fcseed-sess -segid <SegID#> -fillthresh 0.5 -s bert -mean


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



all white matter as seed (erroded by 3 voxels)

Useful to use as nuisance regressor time-course


ventricles & Cerebrospinal fluid as seed

Useful to use as nuisance regressor time-course



output mask for segmentation-based. Good for checking



delete and overwrite any existing files

||-mean || use mean || compute spatial mean seed region time-course for seed region ||


use pca

compute principal component analysis for seed instead of spatial mean. seed.dat file will contain one component time-course per row



as created by funcroi-confg


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.



time course data from seed region


fcseed-sess run log


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.


Example 1

Example 2

Analysis Example

First, create an analysis folder and setup file using mkanalysis-sess




See Also

othercommand1, othercommand2


FreeSurfer, FsFast

Methods Description




Reporting Bugs

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