CCN Wiki - User contributions [en]

Freesurfer Group Analysis(FSGD)

2018-05-30T22:36:24Z

Greg:

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer. This method can potentially be used in place of the other GLM group analysis discussed on this wiki using output from selexavg (or prior to since can specify contrasts here in one step).

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If comparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD File=
For T1 vs. T2 booth data analysis, for example, with 2 groups and no covariates (g2v0.fsgd).

The first line needs to stay the same 'GroupDescriptorFile 1'.
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
0 1
T1T2intercept.mtx
0.5 0.5

=GLM=
Run new GLM for each hemi.
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for Multiple Comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:30:30Z

Greg: /* Correct for multi comparisons */

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If comparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD File=
For T1 vs. T2 booth data analysis, for example, with 2 groups and no covariates (g2v0.fsgd).

The first line needs to stay the same 'GroupDescriptorFile 1'.
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
0 1
T1T2intercept.mtx
0.5 0.5

=GLM=
Run new GLM for each hemi.
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for Multiple Comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:29:50Z

Greg: /* glm */

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If comparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD File=
For T1 vs. T2 booth data analysis, for example, with 2 groups and no covariates (g2v0.fsgd).

The first line needs to stay the same 'GroupDescriptorFile 1'.
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
0 1
T1T2intercept.mtx
0.5 0.5

=GLM=
Run new GLM for each hemi.
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:28:56Z

Greg: /* 'DODS' contrast files */

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If comparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD File=
For T1 vs. T2 booth data analysis, for example, with 2 groups and no covariates (g2v0.fsgd).

The first line needs to stay the same 'GroupDescriptorFile 1'.
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
0 1
T1T2intercept.mtx
0.5 0.5

=glm=
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:28:10Z

Greg: /* FSGD */

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If comparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD File=
For T1 vs. T2 booth data analysis, for example, with 2 groups and no covariates (g2v0.fsgd).

The first line needs to stay the same 'GroupDescriptorFile 1'.
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
01
T1T2intercept.mtx
0.5 0.5

=glm=
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:26:06Z

Greg: /* Data */

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If comparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD=
For T1 vs. T2 Data, first line needs to stay the same. (g2v0.fsgd) -- 2 groups no covariates
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
01
T1T2intercept.mtx
0.5 0.5

=glm=
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:25:40Z

Greg: /* Data */

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
If caomparing T1 and T2 data, grab the files used for original GLMs (e.g. ces.nii.gz).
Rename and cat them for each hemi:
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD=
For T1 vs. T2 Data, first line needs to stay the same. (g2v0.fsgd) -- 2 groups no covariates
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
01
T1T2intercept.mtx
0.5 0.5

=glm=
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-30T22:24:36Z

Greg:

=Freesurfer Group Differences Analysis=
Kinda cool method that can be used to compare groups and/or regress out nuisance variables from BOLD data within Freesurfer.

Some sources:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat

https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
Grab the files used for original glms (e.g. ces.nii.gz)
Rename and cat them for each hemi
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD=
For T1 vs. T2 Data, first line needs to stay the same. (g2v0.fsgd) -- 2 groups no covariates
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
01
T1T2intercept.mtx
0.5 0.5

=glm=
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Freesurfer Group Analysis(FSGD)

2018-05-29T20:26:43Z

Greg: Created page with "https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V =Data= Grab t..."

https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples
https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat
https://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G0V

=Data=
Grab the files used for original glms (e.g. ces.nii.gz)
Rename and cat them for each hemi
mri_concat T1_ces.nii.gz T2_ces.nii.gz --o T1T2_ces.nii.gz

=FSGD=
For T1 vs. T2 Data, first line needs to stay the same. (g2v0.fsgd) -- 2 groups no covariates
GroupDescriptorFile 1
Title OSGM_T1T2
Class GroupT1
Class GroupT2
Input FS_T1_501 GroupT1
Input FS_T1_505 GroupT1
Input FS_T1_506 GroupT1
Input FS_T1_508 GroupT1
Input FS_T1_513 GroupT1
Input FS_T1_515 GroupT1
Input FS_T1_529 GroupT1
Input FS_T1_531 GroupT1
Input FS_T1_534 GroupT1
Input FS_T1_542 GroupT1
Input FS_T1_546 GroupT1
Input FS_T1_557 GroupT1
Input FS_T1_564 GroupT1
Input FS_T1_575 GroupT1
Input FS_T1_576 GroupT1
Input FS_T1_579 GroupT1
Input FS_T1_580 GroupT1
Input FS_T1_587 GroupT1
Input FS_T1_589 GroupT1
Input FS_T2_501 GroupT2
Input FS_T2_505 GroupT2
Input FS_T2_506 GroupT2
Input FS_T2_508 GroupT2
Input FS_T2_513 GroupT2
Input FS_T2_515 GroupT2
Input FS_T2_529 GroupT2
Input FS_T2_531 GroupT2
Input FS_T2_534 GroupT2
Input FS_T2_542 GroupT2
Input FS_T2_546 GroupT2
Input FS_T2_557 GroupT2
Input FS_T2_564 GroupT2
Input FS_T2_575 GroupT2
Input FS_T2_576 GroupT2
Input FS_T2_579 GroupT2
Input FS_T2_580 GroupT2
Input FS_T2_587 GroupT2
Input FS_T2_589 GroupT2

='DODS' contrast files=
groupdiff.mtx
1 -1
T1.mtx
1 0
T2.mtx
01
T1T2intercept.mtx
0.5 0.5

=glm=
Command:
mri_glmfit --surface fsaverage rh --glmdir g2v0 --y T1T2_ces.nii.gz --fsgd g2v0.fsgd --C GroupDiff.mtx --C GroupT1.mtx --C GroupT2.mtx --C GroupT1T2.intercept.mtx

=Correct for multi comparisons=
Next:
mri_glmfit-sim --glmdir g2v0 --cache 3 abs --cwpvalthresh .05

Now you've got clusters and .annot files to load up and get sizes/means etc.

Overlay
tksurfer fsaverage lh pial
load overlay -> cache.th30.abs.sig.cluster.mgh

cache.th30.abs.sig.cluster.summary contains cluster info.

Alternatively, you can segment the cache.th30.abs.sig.cluster.annot file into desired clusters.

Selxavg3-sess

2018-05-17T20:05:03Z

Greg: /* Solution */

== Before you begin ==
Ensure that your BOLD data has the correct timing info. See [[Freesurfer_BOLD_files |Freesurfer BOLD files]] for information on changing the TR for your data. If your BOLD data files are not named 'f.nii', you likely have missed a step.

== Running selxavg3-sess ==
This command runs a GLM regression on the BOLD time series for each run separately. This creates a 3D file that stores a statistic for each voxel for each experimental run that indicates how well the activity for that voxel conformed with a predicted time series associated with each of your experimental conditions. Assuming you have run all the previous steps in the [[FreeSurfer#FS-FAST_Setup_Overview | FS-FAST overview]], there will be one, two or three analysis directories in your $SUBJECTS_DIR, generated by [[Configure_mkanalysis-sess|mkanalysis-sess]]. These directories will contain all the information to perform surface-space analyses in your left/right or mni305 (voxel-space, for subcortical regions which are not included in the surface models) models. If you have configured contrasts using [[Configure_mkcontrast-sess|mkcontrast-sess]], then ''selxavg3-sess'' will perform the statistical condition contrasts you had specified in that step.

=== Batch mode ===
If you want to run the same analysis on multiple participants in batch mode, you must first create a plain text file containing all the participant IDs (i.e., the folder names) for those you wish to analyze. If you ran [[Configure_preproc-sess|preproc-sess]] in batch mode, then you can simply reuse the sessid file you created for that purpose. See the preproc-sess instructions for creating that file. Batch mode uses the <code>-sf</code> switch, which is the same as the switch used for running ''preproc-sess'' in batch mode.
selxavg3-sess -sf <sessid_file> -analysis <analysis_dir>

=== Individual mode ===
Alternatively, you may run the analysis on one participant at the time using the <code>-s</code> switch, and providing a single participant ID name.
selxavg3-sess -s <sessid> -analysis <analysis_dir>

== Example ==
Here, we are going to run selxavg3-sess on a single participant (<code>FS_0001</code>) for the analysis configuration found in the folder <code>$SUBJECTS_DIR/LDT.sm6.lh</code>
selxavg3-sess -s FS_0001 -analysis LDT.sm6.lh
Note that the analysis folder indicated in this example is configured only for the left hemisphere. You would most likely want to run this command a second time, and specify the configuration for the right hemisphere. In this way, you will have run the analysis for both cortical hemispheres. A third execution of the command would be required to run analyses for subcortical regions found in MNI305.

== Troubleshooting ==
=== SVD input matrix contains NaN ===
I've encountered this problem twice, only documenting the solution the second time. When running selxavg3-sess, you may encounter the following error:
Creating Design Matrix
... creation time = 0.019 sec
DoMCFit = 1
ntptot = 2592, nX = 81, DOF = 2511
Saving X matrix to /home/chris/SEM/FS_0231/bold/FAM.self.sm4.down.rh/Xtmp.mat
Error using svd
Input to SVD must not contain NaN or Inf.
Error in cond (line 35)
s = svd(A);
Error in fast_selxavg3 (line 279)
XCond = cond(XtX);

The problem is that the Xn matrix generated from the .par files contains several columns of NaN. The root cause of this is when your par files are missing one or more of the conditions that are listed in the analysis. In the above error, the familiarity analysis includes 3 familiarity conditions: Familiar (1), Unfamiliar_Time1 (2) and Unfamiliar_Time2 (3). The ''FAM.par'' files that were produced by a Matlab script only differentiated between familiar and unfamiliar items, and consequently only contained conditions 1 and 2.
====Solution====
The solution was to find the ''FAM.par'' files associated with Time 2 (runs 007 to 012, when both time points are analyzed as a single 12-run session) and recode the 2s into 3s. After doing this, all was well with the world (aside from the geopolitical strife and environmental disasters).
This can be done fairly easily using <code>sed</code> with the -i "in place" switch:
sed -i 's/\t2/\t3/g' FAM.par

More generally, this problem can occur if your condition codes don't start at 1. For example, you have 6 conditions and only want to compare 2 of them (e.g. 5 and 6), they need to be recoded as 1 and 2.

=== ERROR: fast_selxavg3() ===

<code>ERROR: fast_selxavg3() failed\n</code>

Leading up to the error message, we see a message <code>Subscript indices must either be real positive integers or logicals.</code>

When we see messages that refer to indices in the context of an error message, this is often symptomatic of a mismatch between the amount of data available, and the data access request. For example, if only 252 volumes of data are present (numbered 1, 2, ..., 252) but a script or function is asked to access volume 253 or volume 0, then this will likely generate an error.

If the same processing steps work fine with some data, but generate the above error for a particular dataset, check to see that there isn't something funny with the data. One potential culprit is the early termination of the BOLD sequence. For the LDT, which is self-paced, there were a few runs where the MRI acquisition stopped before the task ended. In this case, the timestamps for the runtime behavioral data will make reference to points in time outside of the scope of the MRI data. The scripts we use to generate the .par files from these runtime data have no way of knowing that these timestamps go past the end of the BOLD sequence.
==== Solution ====
Try manually editing the .par file by removing timestamps outside of the MRI acquisition. You will have to do some math (''TR'' × ''Number of Volumes'') to figure out what the last valid timestamp would be.
editfslhd f.nii
For FS_0092, we see
nt = '252'
...
dt = '2.047'
So the latest possible timepoint is 515.8. Any .par file events past this point are not in the data.

Also, while editing the .par file, ensure that any NaN values are set to a real number, which, now that I think of it, is another potential culprit (since NaN is neither a ''real positive integer or logical''). The FSTSExtractor.m script uses RT as the event duration, but this value is set to NaN if the participant didn't respond. Set the NaN value to 0, but also set the weight (the last column) to 0 so that these null events aren't modeled.

An update to the FSTSExtractor function will need to be made so that this problem doesn't pop up, but in the meantime, ensuring that the .par file timestamps are sensible and contain no NaN values fixes this problem. A fast way to find any NaN values:
grep "NaN" `find ./ -name "LDT.par"`

===ERROR: analysis.cfg does not exist ===

This error occurs if you run the selxavg-sess function associated with older FS versions (e.g. check your command: selxavg-sess vs. selxavg3-sess). Freesurfer 5.x no longer creates an analysis.cfg file with the newer selxavg3-sess.

== Check your results ==
You can look at the contrast results in the surface model in ''tksurfer''. First, load the surface you want to overlay your results on. All of our examples have been done using the '''self''' surface, but it is possible to instead map data to the ''fsaverage'' template, which facilitates comparisons between individuals.
tksurfer FS_T1_501 lh pial
Next, you will load the relevant statistical overlay. This file will be found in the subject folder in the '''bold''' subdirectory, which has an analysis folder for each analysis that you ran with selxavg3-sess. In this example, there are two folders:
*$SUBJECTS_DIR
**FS_T1_501
***bold
****booth500.sm6.lh
****booth500.sm6.rh
In each of these analysis folders are several compressed '''.nii.gz''' files. The statistical overlay you will want to overlay will depend on your interests, but most likely you will want to open up a diagnostic (i.e., easily interpretable) contrast map, which you will find in subdirectories within each of the analysis directories. In my case, I had run a word_vs_fix contrast which generated a t-statistic and f-statistic map. I chose to load the t-map for my left-hemisphere surface. To do so, I did the following:
#File>Load Overlay...
#Browse to $SUBJECTS_DIR/FS_T1_501/bold/booth500.sm6.lh/word_vs_fix/t.nii.gz
#Additional '''Registration''' information may be required to map each of the voxels on to your surface space.
#*When mapping voxels onto a self surface, it's safe to select the '''Find registration in data directory'''
#*When mapping voxels onto fsaverage, you can select the '''Compute Identity Matrix''' registration option.
#Click the '''OK''' button. In a moment you should see hot- and cold- colored blobs overlaid on the surface model. Depending on the nature of the contrast and your experience, you might be able to use this overlay to assess whether the data look reasonable. For example a contrast between button-press trials and rest should probably show a robust activation in left motor cortex (assuming right-handed participants).

[[Category: FreeSurfer]]
[[Category: First Level Analysis]]

Selxavg3-sess

2018-05-17T19:57:22Z

Greg: /* ERROR: analysis.cfg does not exist */

== Before you begin ==
Ensure that your BOLD data has the correct timing info. See [[Freesurfer_BOLD_files |Freesurfer BOLD files]] for information on changing the TR for your data. If your BOLD data files are not named 'f.nii', you likely have missed a step.

== Running selxavg3-sess ==
This command runs a GLM regression on the BOLD time series for each run separately. This creates a 3D file that stores a statistic for each voxel for each experimental run that indicates how well the activity for that voxel conformed with a predicted time series associated with each of your experimental conditions. Assuming you have run all the previous steps in the [[FreeSurfer#FS-FAST_Setup_Overview | FS-FAST overview]], there will be one, two or three analysis directories in your $SUBJECTS_DIR, generated by [[Configure_mkanalysis-sess|mkanalysis-sess]]. These directories will contain all the information to perform surface-space analyses in your left/right or mni305 (voxel-space, for subcortical regions which are not included in the surface models) models. If you have configured contrasts using [[Configure_mkcontrast-sess|mkcontrast-sess]], then ''selxavg3-sess'' will perform the statistical condition contrasts you had specified in that step.

=== Batch mode ===
If you want to run the same analysis on multiple participants in batch mode, you must first create a plain text file containing all the participant IDs (i.e., the folder names) for those you wish to analyze. If you ran [[Configure_preproc-sess|preproc-sess]] in batch mode, then you can simply reuse the sessid file you created for that purpose. See the preproc-sess instructions for creating that file. Batch mode uses the <code>-sf</code> switch, which is the same as the switch used for running ''preproc-sess'' in batch mode.
selxavg3-sess -sf <sessid_file> -analysis <analysis_dir>

=== Individual mode ===
Alternatively, you may run the analysis on one participant at the time using the <code>-s</code> switch, and providing a single participant ID name.
selxavg3-sess -s <sessid> -analysis <analysis_dir>

== Example ==
Here, we are going to run selxavg3-sess on a single participant (<code>FS_0001</code>) for the analysis configuration found in the folder <code>$SUBJECTS_DIR/LDT.sm6.lh</code>
selxavg3-sess -s FS_0001 -analysis LDT.sm6.lh
Note that the analysis folder indicated in this example is configured only for the left hemisphere. You would most likely want to run this command a second time, and specify the configuration for the right hemisphere. In this way, you will have run the analysis for both cortical hemispheres. A third execution of the command would be required to run analyses for subcortical regions found in MNI305.

== Troubleshooting ==
=== SVD input matrix contains NaN ===
I've encountered this problem twice, only documenting the solution the second time. When running selxavg3-sess, you may encounter the following error:
Creating Design Matrix
... creation time = 0.019 sec
DoMCFit = 1
ntptot = 2592, nX = 81, DOF = 2511
Saving X matrix to /home/chris/SEM/FS_0231/bold/FAM.self.sm4.down.rh/Xtmp.mat
Error using svd
Input to SVD must not contain NaN or Inf.
Error in cond (line 35)
s = svd(A);
Error in fast_selxavg3 (line 279)
XCond = cond(XtX);

The problem is that the Xn matrix generated from the .par files contains several columns of NaN. The root cause of this is when your par files are missing one or more of the conditions that are listed in the analysis. In the above error, the familiarity analysis includes 3 familiarity conditions: Familiar (1), Unfamiliar_Time1 (2) and Unfamiliar_Time2 (3). The ''FAM.par'' files that were produced by a Matlab script only differentiated between familiar and unfamiliar items, and consequently only contained conditions 1 and 2.
====Solution====
The solution was to find the ''FAM.par'' files associated with Time 2 (runs 007 to 012, when both time points are analyzed as a single 12-run session) and recode the 2s into 3s. After doing this, all was well with the world (aside from the geopolitical strife and environmental disasters).
This can be done fairly easily using <code>sed</code> with the -i "in place" switch:
sed -i 's/\t2/\t3/g' FAM.par

=== ERROR: fast_selxavg3() ===

<code>ERROR: fast_selxavg3() failed\n</code>

Leading up to the error message, we see a message <code>Subscript indices must either be real positive integers or logicals.</code>

When we see messages that refer to indices in the context of an error message, this is often symptomatic of a mismatch between the amount of data available, and the data access request. For example, if only 252 volumes of data are present (numbered 1, 2, ..., 252) but a script or function is asked to access volume 253 or volume 0, then this will likely generate an error.

If the same processing steps work fine with some data, but generate the above error for a particular dataset, check to see that there isn't something funny with the data. One potential culprit is the early termination of the BOLD sequence. For the LDT, which is self-paced, there were a few runs where the MRI acquisition stopped before the task ended. In this case, the timestamps for the runtime behavioral data will make reference to points in time outside of the scope of the MRI data. The scripts we use to generate the .par files from these runtime data have no way of knowing that these timestamps go past the end of the BOLD sequence.
==== Solution ====
Try manually editing the .par file by removing timestamps outside of the MRI acquisition. You will have to do some math (''TR'' × ''Number of Volumes'') to figure out what the last valid timestamp would be.
editfslhd f.nii
For FS_0092, we see
nt = '252'
...
dt = '2.047'
So the latest possible timepoint is 515.8. Any .par file events past this point are not in the data.

Also, while editing the .par file, ensure that any NaN values are set to a real number, which, now that I think of it, is another potential culprit (since NaN is neither a ''real positive integer or logical''). The FSTSExtractor.m script uses RT as the event duration, but this value is set to NaN if the participant didn't respond. Set the NaN value to 0, but also set the weight (the last column) to 0 so that these null events aren't modeled.

An update to the FSTSExtractor function will need to be made so that this problem doesn't pop up, but in the meantime, ensuring that the .par file timestamps are sensible and contain no NaN values fixes this problem. A fast way to find any NaN values:
grep "NaN" `find ./ -name "LDT.par"`

===ERROR: analysis.cfg does not exist ===

This error occurs if you run the selxavg-sess function associated with older FS versions (e.g. check your command: selxavg-sess vs. selxavg3-sess). Freesurfer 5.x no longer creates an analysis.cfg file with the newer selxavg3-sess.

== Check your results ==
You can look at the contrast results in the surface model in ''tksurfer''. First, load the surface you want to overlay your results on. All of our examples have been done using the '''self''' surface, but it is possible to instead map data to the ''fsaverage'' template, which facilitates comparisons between individuals.
tksurfer FS_T1_501 lh pial
Next, you will load the relevant statistical overlay. This file will be found in the subject folder in the '''bold''' subdirectory, which has an analysis folder for each analysis that you ran with selxavg3-sess. In this example, there are two folders:
*$SUBJECTS_DIR
**FS_T1_501
***bold
****booth500.sm6.lh
****booth500.sm6.rh
In each of these analysis folders are several compressed '''.nii.gz''' files. The statistical overlay you will want to overlay will depend on your interests, but most likely you will want to open up a diagnostic (i.e., easily interpretable) contrast map, which you will find in subdirectories within each of the analysis directories. In my case, I had run a word_vs_fix contrast which generated a t-statistic and f-statistic map. I chose to load the t-map for my left-hemisphere surface. To do so, I did the following:
#File>Load Overlay...
#Browse to $SUBJECTS_DIR/FS_T1_501/bold/booth500.sm6.lh/word_vs_fix/t.nii.gz
#Additional '''Registration''' information may be required to map each of the voxels on to your surface space.
#*When mapping voxels onto a self surface, it's safe to select the '''Find registration in data directory'''
#*When mapping voxels onto fsaverage, you can select the '''Compute Identity Matrix''' registration option.
#Click the '''OK''' button. In a moment you should see hot- and cold- colored blobs overlaid on the surface model. Depending on the nature of the contrast and your experience, you might be able to use this overlay to assess whether the data look reasonable. For example a contrast between button-press trials and rest should probably show a robust activation in left motor cortex (assuming right-handed participants).

[[Category: FreeSurfer]]
[[Category: First Level Analysis]]

Selxavg3-sess

2018-05-17T19:22:04Z

Greg: /* ERROR: analysis.cfg does not exist */

== Before you begin ==
Ensure that your BOLD data has the correct timing info. See [[Freesurfer_BOLD_files |Freesurfer BOLD files]] for information on changing the TR for your data. If your BOLD data files are not named 'f.nii', you likely have missed a step.

== Running selxavg3-sess ==
This command runs a GLM regression on the BOLD time series for each run separately. This creates a 3D file that stores a statistic for each voxel for each experimental run that indicates how well the activity for that voxel conformed with a predicted time series associated with each of your experimental conditions. Assuming you have run all the previous steps in the [[FreeSurfer#FS-FAST_Setup_Overview | FS-FAST overview]], there will be one, two or three analysis directories in your $SUBJECTS_DIR, generated by [[Configure_mkanalysis-sess|mkanalysis-sess]]. These directories will contain all the information to perform surface-space analyses in your left/right or mni305 (voxel-space, for subcortical regions which are not included in the surface models) models. If you have configured contrasts using [[Configure_mkcontrast-sess|mkcontrast-sess]], then ''selxavg3-sess'' will perform the statistical condition contrasts you had specified in that step.

=== Batch mode ===
If you want to run the same analysis on multiple participants in batch mode, you must first create a plain text file containing all the participant IDs (i.e., the folder names) for those you wish to analyze. If you ran [[Configure_preproc-sess|preproc-sess]] in batch mode, then you can simply reuse the sessid file you created for that purpose. See the preproc-sess instructions for creating that file. Batch mode uses the <code>-sf</code> switch, which is the same as the switch used for running ''preproc-sess'' in batch mode.
selxavg3-sess -sf <sessid_file> -analysis <analysis_dir>

=== Individual mode ===
Alternatively, you may run the analysis on one participant at the time using the <code>-s</code> switch, and providing a single participant ID name.
selxavg3-sess -s <sessid> -analysis <analysis_dir>

== Example ==
Here, we are going to run selxavg3-sess on a single participant (<code>FS_0001</code>) for the analysis configuration found in the folder <code>$SUBJECTS_DIR/LDT.sm6.lh</code>
selxavg3-sess -s FS_0001 -analysis LDT.sm6.lh
Note that the analysis folder indicated in this example is configured only for the left hemisphere. You would most likely want to run this command a second time, and specify the configuration for the right hemisphere. In this way, you will have run the analysis for both cortical hemispheres. A third execution of the command would be required to run analyses for subcortical regions found in MNI305.

== Troubleshooting ==
=== SVD input matrix contains NaN ===
I've encountered this problem twice, only documenting the solution the second time. When running selxavg3-sess, you may encounter the following error:
Creating Design Matrix
... creation time = 0.019 sec
DoMCFit = 1
ntptot = 2592, nX = 81, DOF = 2511
Saving X matrix to /home/chris/SEM/FS_0231/bold/FAM.self.sm4.down.rh/Xtmp.mat
Error using svd
Input to SVD must not contain NaN or Inf.
Error in cond (line 35)
s = svd(A);
Error in fast_selxavg3 (line 279)
XCond = cond(XtX);

The problem is that the Xn matrix generated from the .par files contains several columns of NaN. The root cause of this is when your par files are missing one or more of the conditions that are listed in the analysis. In the above error, the familiarity analysis includes 3 familiarity conditions: Familiar (1), Unfamiliar_Time1 (2) and Unfamiliar_Time2 (3). The ''FAM.par'' files that were produced by a Matlab script only differentiated between familiar and unfamiliar items, and consequently only contained conditions 1 and 2.
====Solution====
The solution was to find the ''FAM.par'' files associated with Time 2 (runs 007 to 012, when both time points are analyzed as a single 12-run session) and recode the 2s into 3s. After doing this, all was well with the world (aside from the geopolitical strife and environmental disasters).
This can be done fairly easily using <code>sed</code> with the -i "in place" switch:
sed -i 's/\t2/\t3/g' FAM.par

=== ERROR: fast_selxavg3() ===

<code>ERROR: fast_selxavg3() failed\n</code>

Leading up to the error message, we see a message <code>Subscript indices must either be real positive integers or logicals.</code>

When we see messages that refer to indices in the context of an error message, this is often symptomatic of a mismatch between the amount of data available, and the data access request. For example, if only 252 volumes of data are present (numbered 1, 2, ..., 252) but a script or function is asked to access volume 253 or volume 0, then this will likely generate an error.

If the same processing steps work fine with some data, but generate the above error for a particular dataset, check to see that there isn't something funny with the data. One potential culprit is the early termination of the BOLD sequence. For the LDT, which is self-paced, there were a few runs where the MRI acquisition stopped before the task ended. In this case, the timestamps for the runtime behavioral data will make reference to points in time outside of the scope of the MRI data. The scripts we use to generate the .par files from these runtime data have no way of knowing that these timestamps go past the end of the BOLD sequence.
==== Solution ====
Try manually editing the .par file by removing timestamps outside of the MRI acquisition. You will have to do some math (''TR'' × ''Number of Volumes'') to figure out what the last valid timestamp would be.
editfslhd f.nii
For FS_0092, we see
nt = '252'
...
dt = '2.047'
So the latest possible timepoint is 515.8. Any .par file events past this point are not in the data.

Also, while editing the .par file, ensure that any NaN values are set to a real number, which, now that I think of it, is another potential culprit (since NaN is neither a ''real positive integer or logical''). The FSTSExtractor.m script uses RT as the event duration, but this value is set to NaN if the participant didn't respond. Set the NaN value to 0, but also set the weight (the last column) to 0 so that these null events aren't modeled.

An update to the FSTSExtractor function will need to be made so that this problem doesn't pop up, but in the meantime, ensuring that the .par file timestamps are sensible and contain no NaN values fixes this problem. A fast way to find any NaN values:
grep "NaN" `find ./ -name "LDT.par"`

===ERROR: analysis.cfg does not exist ===

Solution pending
I found this error occurs if you run the selxavg-sess function associated with older FS versions (e.g. check your command: selxavg-sess vs. selxavg3-sess). Freesurfer 5.x no longer creates an analysis.cfg file. - Greg

== Check your results ==
You can look at the contrast results in the surface model in ''tksurfer''. First, load the surface you want to overlay your results on. All of our examples have been done using the '''self''' surface, but it is possible to instead map data to the ''fsaverage'' template, which facilitates comparisons between individuals.
tksurfer FS_T1_501 lh pial
Next, you will load the relevant statistical overlay. This file will be found in the subject folder in the '''bold''' subdirectory, which has an analysis folder for each analysis that you ran with selxavg3-sess. In this example, there are two folders:
*$SUBJECTS_DIR
**FS_T1_501
***bold
****booth500.sm6.lh
****booth500.sm6.rh
In each of these analysis folders are several compressed '''.nii.gz''' files. The statistical overlay you will want to overlay will depend on your interests, but most likely you will want to open up a diagnostic (i.e., easily interpretable) contrast map, which you will find in subdirectories within each of the analysis directories. In my case, I had run a word_vs_fix contrast which generated a t-statistic and f-statistic map. I chose to load the t-map for my left-hemisphere surface. To do so, I did the following:
#File>Load Overlay...
#Browse to $SUBJECTS_DIR/FS_T1_501/bold/booth500.sm6.lh/word_vs_fix/t.nii.gz
#Additional '''Registration''' information may be required to map each of the voxels on to your surface space.
#*When mapping voxels onto a self surface, it's safe to select the '''Find registration in data directory'''
#*When mapping voxels onto fsaverage, you can select the '''Compute Identity Matrix''' registration option.
#Click the '''OK''' button. In a moment you should see hot- and cold- colored blobs overlaid on the surface model. Depending on the nature of the contrast and your experience, you might be able to use this overlay to assess whether the data look reasonable. For example a contrast between button-press trials and rest should probably show a robust activation in left motor cortex (assuming right-handed participants).

[[Category: FreeSurfer]]
[[Category: First Level Analysis]]

Selxavg3-sess

2018-05-17T19:21:53Z

Greg: /* ERROR: analysis.cfg does not exist */

== Before you begin ==
Ensure that your BOLD data has the correct timing info. See [[Freesurfer_BOLD_files |Freesurfer BOLD files]] for information on changing the TR for your data. If your BOLD data files are not named 'f.nii', you likely have missed a step.

== Running selxavg3-sess ==
This command runs a GLM regression on the BOLD time series for each run separately. This creates a 3D file that stores a statistic for each voxel for each experimental run that indicates how well the activity for that voxel conformed with a predicted time series associated with each of your experimental conditions. Assuming you have run all the previous steps in the [[FreeSurfer#FS-FAST_Setup_Overview | FS-FAST overview]], there will be one, two or three analysis directories in your $SUBJECTS_DIR, generated by [[Configure_mkanalysis-sess|mkanalysis-sess]]. These directories will contain all the information to perform surface-space analyses in your left/right or mni305 (voxel-space, for subcortical regions which are not included in the surface models) models. If you have configured contrasts using [[Configure_mkcontrast-sess|mkcontrast-sess]], then ''selxavg3-sess'' will perform the statistical condition contrasts you had specified in that step.

=== Batch mode ===
If you want to run the same analysis on multiple participants in batch mode, you must first create a plain text file containing all the participant IDs (i.e., the folder names) for those you wish to analyze. If you ran [[Configure_preproc-sess|preproc-sess]] in batch mode, then you can simply reuse the sessid file you created for that purpose. See the preproc-sess instructions for creating that file. Batch mode uses the <code>-sf</code> switch, which is the same as the switch used for running ''preproc-sess'' in batch mode.
selxavg3-sess -sf <sessid_file> -analysis <analysis_dir>

=== Individual mode ===
Alternatively, you may run the analysis on one participant at the time using the <code>-s</code> switch, and providing a single participant ID name.
selxavg3-sess -s <sessid> -analysis <analysis_dir>

== Example ==
Here, we are going to run selxavg3-sess on a single participant (<code>FS_0001</code>) for the analysis configuration found in the folder <code>$SUBJECTS_DIR/LDT.sm6.lh</code>
selxavg3-sess -s FS_0001 -analysis LDT.sm6.lh
Note that the analysis folder indicated in this example is configured only for the left hemisphere. You would most likely want to run this command a second time, and specify the configuration for the right hemisphere. In this way, you will have run the analysis for both cortical hemispheres. A third execution of the command would be required to run analyses for subcortical regions found in MNI305.

== Troubleshooting ==
=== SVD input matrix contains NaN ===
I've encountered this problem twice, only documenting the solution the second time. When running selxavg3-sess, you may encounter the following error:
Creating Design Matrix
... creation time = 0.019 sec
DoMCFit = 1
ntptot = 2592, nX = 81, DOF = 2511
Saving X matrix to /home/chris/SEM/FS_0231/bold/FAM.self.sm4.down.rh/Xtmp.mat
Error using svd
Input to SVD must not contain NaN or Inf.
Error in cond (line 35)
s = svd(A);
Error in fast_selxavg3 (line 279)
XCond = cond(XtX);

The problem is that the Xn matrix generated from the .par files contains several columns of NaN. The root cause of this is when your par files are missing one or more of the conditions that are listed in the analysis. In the above error, the familiarity analysis includes 3 familiarity conditions: Familiar (1), Unfamiliar_Time1 (2) and Unfamiliar_Time2 (3). The ''FAM.par'' files that were produced by a Matlab script only differentiated between familiar and unfamiliar items, and consequently only contained conditions 1 and 2.
====Solution====
The solution was to find the ''FAM.par'' files associated with Time 2 (runs 007 to 012, when both time points are analyzed as a single 12-run session) and recode the 2s into 3s. After doing this, all was well with the world (aside from the geopolitical strife and environmental disasters).
This can be done fairly easily using <code>sed</code> with the -i "in place" switch:
sed -i 's/\t2/\t3/g' FAM.par

=== ERROR: fast_selxavg3() ===

<code>ERROR: fast_selxavg3() failed\n</code>

Leading up to the error message, we see a message <code>Subscript indices must either be real positive integers or logicals.</code>

When we see messages that refer to indices in the context of an error message, this is often symptomatic of a mismatch between the amount of data available, and the data access request. For example, if only 252 volumes of data are present (numbered 1, 2, ..., 252) but a script or function is asked to access volume 253 or volume 0, then this will likely generate an error.

If the same processing steps work fine with some data, but generate the above error for a particular dataset, check to see that there isn't something funny with the data. One potential culprit is the early termination of the BOLD sequence. For the LDT, which is self-paced, there were a few runs where the MRI acquisition stopped before the task ended. In this case, the timestamps for the runtime behavioral data will make reference to points in time outside of the scope of the MRI data. The scripts we use to generate the .par files from these runtime data have no way of knowing that these timestamps go past the end of the BOLD sequence.
==== Solution ====
Try manually editing the .par file by removing timestamps outside of the MRI acquisition. You will have to do some math (''TR'' × ''Number of Volumes'') to figure out what the last valid timestamp would be.
editfslhd f.nii
For FS_0092, we see
nt = '252'
...
dt = '2.047'
So the latest possible timepoint is 515.8. Any .par file events past this point are not in the data.

Also, while editing the .par file, ensure that any NaN values are set to a real number, which, now that I think of it, is another potential culprit (since NaN is neither a ''real positive integer or logical''). The FSTSExtractor.m script uses RT as the event duration, but this value is set to NaN if the participant didn't respond. Set the NaN value to 0, but also set the weight (the last column) to 0 so that these null events aren't modeled.

An update to the FSTSExtractor function will need to be made so that this problem doesn't pop up, but in the meantime, ensuring that the .par file timestamps are sensible and contain no NaN values fixes this problem. A fast way to find any NaN values:
grep "NaN" `find ./ -name "LDT.par"`

===ERROR: analysis.cfg does not exist ===

Solution pending
I found this error occurs if you run the selxavg-sess function associated with older FS versions (e.g. check your command: selxavg-sess vs. selxavg3-sess). Freesurfer 5.x no longer creates an analysis.cfg file.

== Check your results ==
You can look at the contrast results in the surface model in ''tksurfer''. First, load the surface you want to overlay your results on. All of our examples have been done using the '''self''' surface, but it is possible to instead map data to the ''fsaverage'' template, which facilitates comparisons between individuals.
tksurfer FS_T1_501 lh pial
Next, you will load the relevant statistical overlay. This file will be found in the subject folder in the '''bold''' subdirectory, which has an analysis folder for each analysis that you ran with selxavg3-sess. In this example, there are two folders:
*$SUBJECTS_DIR
**FS_T1_501
***bold
****booth500.sm6.lh
****booth500.sm6.rh
In each of these analysis folders are several compressed '''.nii.gz''' files. The statistical overlay you will want to overlay will depend on your interests, but most likely you will want to open up a diagnostic (i.e., easily interpretable) contrast map, which you will find in subdirectories within each of the analysis directories. In my case, I had run a word_vs_fix contrast which generated a t-statistic and f-statistic map. I chose to load the t-map for my left-hemisphere surface. To do so, I did the following:
#File>Load Overlay...
#Browse to $SUBJECTS_DIR/FS_T1_501/bold/booth500.sm6.lh/word_vs_fix/t.nii.gz
#Additional '''Registration''' information may be required to map each of the voxels on to your surface space.
#*When mapping voxels onto a self surface, it's safe to select the '''Find registration in data directory'''
#*When mapping voxels onto fsaverage, you can select the '''Compute Identity Matrix''' registration option.
#Click the '''OK''' button. In a moment you should see hot- and cold- colored blobs overlaid on the surface model. Depending on the nature of the contrast and your experience, you might be able to use this overlay to assess whether the data look reasonable. For example a contrast between button-press trials and rest should probably show a robust activation in left motor cortex (assuming right-handed participants).

[[Category: FreeSurfer]]
[[Category: First Level Analysis]]

Selxavg3-sess

2018-05-17T19:21:16Z

Greg: /* ERROR: analysis.cfg does not exist */

== Before you begin ==
Ensure that your BOLD data has the correct timing info. See [[Freesurfer_BOLD_files |Freesurfer BOLD files]] for information on changing the TR for your data. If your BOLD data files are not named 'f.nii', you likely have missed a step.

== Running selxavg3-sess ==
This command runs a GLM regression on the BOLD time series for each run separately. This creates a 3D file that stores a statistic for each voxel for each experimental run that indicates how well the activity for that voxel conformed with a predicted time series associated with each of your experimental conditions. Assuming you have run all the previous steps in the [[FreeSurfer#FS-FAST_Setup_Overview | FS-FAST overview]], there will be one, two or three analysis directories in your $SUBJECTS_DIR, generated by [[Configure_mkanalysis-sess|mkanalysis-sess]]. These directories will contain all the information to perform surface-space analyses in your left/right or mni305 (voxel-space, for subcortical regions which are not included in the surface models) models. If you have configured contrasts using [[Configure_mkcontrast-sess|mkcontrast-sess]], then ''selxavg3-sess'' will perform the statistical condition contrasts you had specified in that step.

=== Batch mode ===
If you want to run the same analysis on multiple participants in batch mode, you must first create a plain text file containing all the participant IDs (i.e., the folder names) for those you wish to analyze. If you ran [[Configure_preproc-sess|preproc-sess]] in batch mode, then you can simply reuse the sessid file you created for that purpose. See the preproc-sess instructions for creating that file. Batch mode uses the <code>-sf</code> switch, which is the same as the switch used for running ''preproc-sess'' in batch mode.
selxavg3-sess -sf <sessid_file> -analysis <analysis_dir>

=== Individual mode ===
Alternatively, you may run the analysis on one participant at the time using the <code>-s</code> switch, and providing a single participant ID name.
selxavg3-sess -s <sessid> -analysis <analysis_dir>

== Example ==
Here, we are going to run selxavg3-sess on a single participant (<code>FS_0001</code>) for the analysis configuration found in the folder <code>$SUBJECTS_DIR/LDT.sm6.lh</code>
selxavg3-sess -s FS_0001 -analysis LDT.sm6.lh
Note that the analysis folder indicated in this example is configured only for the left hemisphere. You would most likely want to run this command a second time, and specify the configuration for the right hemisphere. In this way, you will have run the analysis for both cortical hemispheres. A third execution of the command would be required to run analyses for subcortical regions found in MNI305.

== Troubleshooting ==
=== SVD input matrix contains NaN ===
I've encountered this problem twice, only documenting the solution the second time. When running selxavg3-sess, you may encounter the following error:
Creating Design Matrix
... creation time = 0.019 sec
DoMCFit = 1
ntptot = 2592, nX = 81, DOF = 2511
Saving X matrix to /home/chris/SEM/FS_0231/bold/FAM.self.sm4.down.rh/Xtmp.mat
Error using svd
Input to SVD must not contain NaN or Inf.
Error in cond (line 35)
s = svd(A);
Error in fast_selxavg3 (line 279)
XCond = cond(XtX);

The problem is that the Xn matrix generated from the .par files contains several columns of NaN. The root cause of this is when your par files are missing one or more of the conditions that are listed in the analysis. In the above error, the familiarity analysis includes 3 familiarity conditions: Familiar (1), Unfamiliar_Time1 (2) and Unfamiliar_Time2 (3). The ''FAM.par'' files that were produced by a Matlab script only differentiated between familiar and unfamiliar items, and consequently only contained conditions 1 and 2.
====Solution====
The solution was to find the ''FAM.par'' files associated with Time 2 (runs 007 to 012, when both time points are analyzed as a single 12-run session) and recode the 2s into 3s. After doing this, all was well with the world (aside from the geopolitical strife and environmental disasters).
This can be done fairly easily using <code>sed</code> with the -i "in place" switch:
sed -i 's/\t2/\t3/g' FAM.par

=== ERROR: fast_selxavg3() ===

<code>ERROR: fast_selxavg3() failed\n</code>

Leading up to the error message, we see a message <code>Subscript indices must either be real positive integers or logicals.</code>

When we see messages that refer to indices in the context of an error message, this is often symptomatic of a mismatch between the amount of data available, and the data access request. For example, if only 252 volumes of data are present (numbered 1, 2, ..., 252) but a script or function is asked to access volume 253 or volume 0, then this will likely generate an error.

If the same processing steps work fine with some data, but generate the above error for a particular dataset, check to see that there isn't something funny with the data. One potential culprit is the early termination of the BOLD sequence. For the LDT, which is self-paced, there were a few runs where the MRI acquisition stopped before the task ended. In this case, the timestamps for the runtime behavioral data will make reference to points in time outside of the scope of the MRI data. The scripts we use to generate the .par files from these runtime data have no way of knowing that these timestamps go past the end of the BOLD sequence.
==== Solution ====
Try manually editing the .par file by removing timestamps outside of the MRI acquisition. You will have to do some math (''TR'' × ''Number of Volumes'') to figure out what the last valid timestamp would be.
editfslhd f.nii
For FS_0092, we see
nt = '252'
...
dt = '2.047'
So the latest possible timepoint is 515.8. Any .par file events past this point are not in the data.

Also, while editing the .par file, ensure that any NaN values are set to a real number, which, now that I think of it, is another potential culprit (since NaN is neither a ''real positive integer or logical''). The FSTSExtractor.m script uses RT as the event duration, but this value is set to NaN if the participant didn't respond. Set the NaN value to 0, but also set the weight (the last column) to 0 so that these null events aren't modeled.

An update to the FSTSExtractor function will need to be made so that this problem doesn't pop up, but in the meantime, ensuring that the .par file timestamps are sensible and contain no NaN values fixes this problem. A fast way to find any NaN values:
grep "NaN" `find ./ -name "LDT.par"`

===ERROR: analysis.cfg does not exist ===

Solution pending
I found this error occurs if you run the selxavg-sess function associated with older FS versions (e.g. check your command: selxavg-sess vs. selxavg3-sess).

== Check your results ==
You can look at the contrast results in the surface model in ''tksurfer''. First, load the surface you want to overlay your results on. All of our examples have been done using the '''self''' surface, but it is possible to instead map data to the ''fsaverage'' template, which facilitates comparisons between individuals.
tksurfer FS_T1_501 lh pial
Next, you will load the relevant statistical overlay. This file will be found in the subject folder in the '''bold''' subdirectory, which has an analysis folder for each analysis that you ran with selxavg3-sess. In this example, there are two folders:
*$SUBJECTS_DIR
**FS_T1_501
***bold
****booth500.sm6.lh
****booth500.sm6.rh
In each of these analysis folders are several compressed '''.nii.gz''' files. The statistical overlay you will want to overlay will depend on your interests, but most likely you will want to open up a diagnostic (i.e., easily interpretable) contrast map, which you will find in subdirectories within each of the analysis directories. In my case, I had run a word_vs_fix contrast which generated a t-statistic and f-statistic map. I chose to load the t-map for my left-hemisphere surface. To do so, I did the following:
#File>Load Overlay...
#Browse to $SUBJECTS_DIR/FS_T1_501/bold/booth500.sm6.lh/word_vs_fix/t.nii.gz
#Additional '''Registration''' information may be required to map each of the voxels on to your surface space.
#*When mapping voxels onto a self surface, it's safe to select the '''Find registration in data directory'''
#*When mapping voxels onto fsaverage, you can select the '''Compute Identity Matrix''' registration option.
#Click the '''OK''' button. In a moment you should see hot- and cold- colored blobs overlaid on the surface model. Depending on the nature of the contrast and your experience, you might be able to use this overlay to assess whether the data look reasonable. For example a contrast between button-press trials and rest should probably show a robust activation in left motor cortex (assuming right-handed participants).

[[Category: FreeSurfer]]
[[Category: First Level Analysis]]

Developmental Neuroscience

2018-05-09T14:35:31Z

Greg: /* Overview */

=Overview=
[[File:DevNeuroConcepts.jpg]]

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
Formatting Info:
https://www.mediawiki.org/wiki/Help:Images

===Local Storage===

[[File:cpicard.jpg]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brodmann areas 3D.png|20px]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-05-09T14:30:41Z

Greg:

=Overview=
[[File:DevNeuro_concepts.jpg]]

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
Formatting Info:
https://www.mediawiki.org/wiki/Help:Images

===Local Storage===

[[File:cpicard.jpg]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brodmann areas 3D.png|20px]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

BASH Tricks

2018-03-12T15:46:40Z

Greg:

== Make a list of directory names ==
We often organize subject data so that each subject gets their own directory. Freesurfer uses a '''subjects''' file when batch processing. Rather than manually type out each folder name into a text file, it can be generated in one line of code:
ls -1 -d */ | sed 's\/\\g' > subjects
This lists in 1 column all the directories (-1 -d) and uses ''sed'' to snip off the trailing forward slashes in the directory names

== Restart Window Manager ==
This has happened a couple times before: you step away from the computer for awhile (maybe even overnight) and when you come back, you find it is locked up and completely unresponsive. The nuclear option is to reboot the whole machine:
sudo shutdown -r now #Sad for anyone running autorecon or a neural network
Unfortunately, that will stop anything that might be running in the background. A less severe solution might be to just restart the window manager. To do this you will need to ssh into the locked-up computer from a different computer, and then restart the lightdm process. This will require superuser privileges.
ssh ''hostname''
Then after you have connected to the frozen computer:
sudo restart lightdm
Any processes that were dependent on the window manager will be terminated (e.g., so if you had been in the middle of editing labels in tksurfer, you will find that tksurfer has been shutdown and you will need to start over), however anything that was running in the background (e.g., autorecon) should be unaffected.

==Renaming Multiple Files==
If you don't have access to the rename command (Mac OSX), you can fake it:
PREFIX=LO
for file in `find . -name "*.txt"`; do mv ${file##*/} ${PREFIX}_${file##*/}; done
Source: [https://stackoverflow.com/questions/2664740/extract-file-basename-without-path-and-extension-in-bash]

==Replacing Text in Multiple Files==
sed -i 's/oldtext/newtext/g' *.ext

Developmental Neuroscience

2018-03-03T19:09:09Z

Greg: /* = */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
Formatting Info:
https://www.mediawiki.org/wiki/Help:Images

===Local Storage===

[[File:cpicard.jpg]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T19:08:34Z

Greg: /* Images */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
Formatting Info:
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T19:08:06Z

Greg: /* = */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T19:07:07Z

Greg: /* = */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

[[media:picard-meets-xenomorphs-in-new-comic.jpg|link:http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T19:06:38Z

Greg: /* = */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

[[media:http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/picard-meets-xenomorphs-in-new-comic.jpg]]

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T19:05:55Z

Greg: /* Mediawiki Images */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/picard-meets-xenomorphs-in-new-comic.jpg

===Wikicommons Images===
https://commons.wikimedia.org/wiki/Main_Page

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T19:04:24Z

Greg: /* Mediawiki Images */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/picard-meets-xenomorphs-in-new-comic.jpg

===Mediawiki Images===

[[Image:Wiki.png|link=MediaWiki]]

[[Image:Brain_Brodmann_blend.gif]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T18:58:21Z

Greg: /* Mediawiki Images */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/picard-meets-xenomorphs-in-new-comic.jpg

===Mediawiki Images===

[[Image:Wiki.png|link=MediaWiki]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T18:58:04Z

Greg: /* = */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/picard-meets-xenomorphs-in-new-comic.jpg

===Mediawiki Images===

[[Image:Wiki.png|50px|link=MediaWiki]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T18:57:16Z

Greg: /* Images */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
https://www.mediawiki.org/wiki/Help:Images

===

[[File:cpicard.jpg]]

http://cdn2.darkhorizons.com/wp-content/uploads/2016/10/picard-meets-xenomorphs-in-new-comic.jpg

===Mediawiki Images===

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T18:51:18Z

Greg: /* Images */

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
[[File:cpicard.jpg]]

===Mediawiki Images===

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

Developmental Neuroscience

2018-03-03T18:50:08Z

Greg:

=Overview=

==Principles==

==Individual Differences==

==Disorders==

=Synapses=

==Epigenetics==

==Circuit Development==

=Plasticity=

==Homeostatic Plasticity==

==Functional Plasticity==

==Sensory Plasticity==

==Sensitive Periods==

==Environmental Enrichment==

=Behavior=

==Consciousness==

==Play==

==Language Learning==

=Intervention=

==Sensory Substitution==

=Formatting Guide=
Click [https://www.mediawiki.org/wiki/Help:Formatting here] for a guide to formatting using wiki markup.

=Demo=

== Accessing the Page ==
===Location===
===User and Password===

== Edits ==
====Micro====
====Macro====

===Fonts===
''italic''

'''bold'''

'''''bold & italic'''''

===Blocks===
this
is
a
block -- useful for displaying code/figures/etc.
this
is
also
a
block
with
some
more
lines

==Images==
[[File:CCNLOGO.jpg]]

== Levels - 2 ==
=== Level 3 ===
==== Level 4 ====
===== Level 5 =====
====== Level 6 ======

CCN Wiki:Terms of Service

2018-02-13T19:05:05Z

Greg: Created page with "Account requests are currently not functioning as intended. For now, ask Greg or Chris to set you up with an account, or send an email request to ubccnlab@gmail.com"

Account requests are currently not functioning as intended. For now, ask Greg or Chris to set you up with an account, or send an email request to ubccnlab@gmail.com