Index

Name

fcseed-sess

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

Optional Flagged Arguments

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

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

othercommand1, othercommand2

Links

FreeSurfer, FsFast

Methods Description

description
description

References

References/Lastname###

Reporting Bugs

Report bugs to < analysis-bugs@nmr.mgh.harvard.edu >

Author/s

JaneSmith