Differences between revisions 4 and 5

Index

Contents

Name
Synopsis
Arguments
1. Required Flagged Arguments
2. Optional Flagged Arguments
Outputs
Description
Examples
Bugs
See Also
Links
Methods Description
References
Reporting Bugs
Author/s

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

||-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	desc
-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

Links

FreeSurfer, FsFast

Methods Description

description
description

References

References/Lastname###

Reporting Bugs

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

Author/s

JaneSmith

-Deletions are marked like this.
+Additions are marked like this.
 Line 24:
 ||-sf sessidfile   || supply text file with list of subjects ||
 Line 28:
-||-fsd fsdir name  || dir name for location of bold data & analyses within subjectdir  ||



||-flag1 arg1 ||brief description ||detailed description (eg, help file information) ||

||-flag2 arg2 ||brief description ||detailed description (eg, help file information) ||
+||-fsd fsdir name  || dir name for location of bold data & analyses within subjectdir  ||
-Line 35:
+Line 32:
-||-flag3 arg3 ||brief description ||detailed description (eg, help file information) ||

||-flag4 arg4 ||brief description ||detailed description (eg, help file information) ||
+||-seg segid <-segid segid2 ...> ||use FreeSurfer segmentation as seed || desc ||

||-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.
-Line 40:
+Line 52:
-||volume1 ||description ||

||volume2 ||description ||
+||seedregion.dat || time course data from seed region ||

||seedregion.log || fcseed-sess run log ||
-Line 45:
+Line 57:
-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.
-Line 49:
+Line 98:
-command foo -i f -o out
+  Create a seed waveform by spatially averaging the entire left

   hemisphere hippocampus:
-Line 51:
+Line 101:
-description
+     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.
-Line 54:
+Line 107:
-command foo -i f -o out -f fvalue
+  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
-Line 56:
+Line 112:
-description
+== 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.