Time Series Data in Surface Space - Revision history

Chris: /* Supplemental .annot Files */

2022-06-28T19:29:29Z

Supplemental .annot Files

← Older revision		Revision as of 15:29, 28 June 2022
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	=== Supplemental .annot Files ===		=== Supplemental .annot Files ===
	The stock set of .annot files in the default fsaverage/label folder may not include the parcellation that you're interested in. Look in the '''~~ubfs/~~openfmri/label''' folder for supplemental .annot files that have been generated for other projects. The lausanne parcellation is found in there.		The stock set of .annot files in the default fsaverage/label folder may not include the parcellation that you're interested in. Look in the UBFS '''openfmri/label''' folder for supplemental .annot files that have been generated for other projects. The lausanne parcellation is found in there.

	==== A Trick ====		==== A Trick ====

Chris: /* Running the Script */

2022-06-28T19:28:45Z

Running the Script

← Older revision		Revision as of 15:28, 28 June 2022
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	The script is run in a terminal by specifying the name of an annot file that can be found in the $SUBJECT_ID/label/ directory, a file pattern, followed by a list of subject IDs:		The script is run in a terminal by specifying the name of an annot file that can be found in the $SUBJECT_ID/label/ directory, a file pattern, followed by a list of subject IDs:
	surfacetimecourses.sh lausanne fmcpr.siemens.sm6 fsaverage FS_T1_501		surfacetimecourses.sh lausanne fmcpr.siemens.sm6 fsaverage FS_T1_501
	The above command would look in the /label directory ~~for subject ''FS_T1_501''~~ for ''lh.lausanne.annot'' and ''rh.lausanne.annot'', and extract the mean time series for each defined region within each .annot file for the bold data found in the file ''fmcpr.sm6.fsaverage.?h.~~mgz~~'' and ''fmcpr.sm6.mni305.2mm.mgh'' in the run folders indicated in the ''runs'' file.		The above command would look in the fsaverage/label directory for ''lh.lausanne.annot'' and ''rh.lausanne.annot'', and extract the mean time series for each defined region within each .annot file for the bold data found in the file ''fmcpr.sm6.fsaverage.?h.mgh'' and ''fmcpr.sm6.mni305.2mm.mgh'' in the run folders indicated in the ''runs'' file.
	A series of output files is produced in the subject's bold/ directory. The time series files are named ${sub}_?h_${annot}_${run}.${filepattern}.wav.txt		A series of output files is produced in the subject's bold/ directory. The time series files are named ${sub}_?h_${annot}_${run}.${filepattern}.wav.txt

			=== Supplemental .annot Files ===
			The stock set of .annot files in the default fsaverage/label folder may not include the parcellation that you're interested in. Look in the '''ubfs/openfmri/label''' folder for supplemental .annot files that have been generated for other projects. The lausanne parcellation is found in there.

	==== A Trick ====		==== A Trick ====

Chris: /* Running the Script */

2020-04-26T17:36:07Z

Running the Script

← Older revision		Revision as of 13:36, 26 April 2020
Line 73:		Line 73:

	The script is run in a terminal by specifying the name of an annot file that can be found in the $SUBJECT_ID/label/ directory, a file pattern, followed by a list of subject IDs:		The script is run in a terminal by specifying the name of an annot file that can be found in the $SUBJECT_ID/label/ directory, a file pattern, followed by a list of subject IDs:
	~~gettimecourses~~.sh lausanne fmcpr.siemens.sm6 fsaverage FS_T1_501		surfacetimecourses.sh lausanne fmcpr.siemens.sm6 fsaverage FS_T1_501
	The above command would look in the /label directory for subject ''FS_T1_501'' for ''lh.lausanne.annot'' and ''rh.lausanne.annot'', and extract the mean time series for each defined region within each .annot file for the bold data found in the file ''fmcpr.sm6.fsaverage.?h.mgz'' and ''fmcpr.sm6.mni305.2mm.mgh'' in the run folders indicated in the ''runs'' file.		The above command would look in the /label directory for subject ''FS_T1_501'' for ''lh.lausanne.annot'' and ''rh.lausanne.annot'', and extract the mean time series for each defined region within each .annot file for the bold data found in the file ''fmcpr.sm6.fsaverage.?h.mgz'' and ''fmcpr.sm6.mni305.2mm.mgh'' in the run folders indicated in the ''runs'' file.
	A series of output files is produced in the subject's bold/ directory. The time series files are named ${sub}_?h_${annot}_${run}.${filepattern}.wav.txt		A series of output files is produced in the subject's bold/ directory. The time series files are named ${sub}_?h_${annot}_${run}.${filepattern}.wav.txt
Line 83:		Line 83:
	ANNOT=lausanne		ANNOT=lausanne
	SURF=fsaverage		SURF=fsaverage
	~~gettimecourses~~.sh $ANNOT $FILEPATTERN $SURF `cat subjects`		surfacetimecourses.sh $ANNOT $FILEPATTERN $SURF `cat subjects`

	=== Decoding the Regions ===		=== Decoding the Regions ===

Chris: /* gettimecourses.sh */

2020-04-26T17:34:23Z

gettimecourses.sh

← Older revision		Revision as of 13:34, 26 April 2020
Line 1:		Line 1:
	After linear trends have been [[Detrending_FreeSurfer_Data \| removed]] from the data, we can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:		After linear trends have been [[Detrending_FreeSurfer_Data \| removed]] from the data, we can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:
	=== ~~gettimecourses~~.sh ===		=== surfacetimecourses.sh ===
	~~'''Note:''' If you have a gettimecourses.sh~~ in ~~your ~~~/~~bin~~/ ~~directory, it is likely obsolete. I have been copying updated versions of my shell~~ scripts ~~to /usr/local/sbin~~. If ~~this script exists in that folder~~, ~~you should consider deleting the version in your ~/bin/ directory so that~~ the ~~most up-to-date version is used instead~~. ~~As of June, 2019, this~~ script ~~now~~ extracts the subcortical ~~data in mni305~~ space.		This script should found in /opt/scripts and should be in your path. If not, let Chris know about the oversight. This script only extracts timecourses from the .lh and .rh surfaces. A separate script will extract timecourses from MNI305 subcortical space.

	#!/bin/bash		#!/bin/bash
	USAGE="Usage: ~~gettimecourses~~.sh annot filepattern surface sub1 ... subN"		USAGE="Usage: surfacetimecourses.sh annot filepattern surface sub1 ... subN"

	if [ "$#" == "0" ]; then		if [ "$#" == "0" ]; then
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	# first two parameters are is the annot files and filepatterns for the \		# first two parameters are is the annot files and filepatterns for the \
	# .nii.gz time series to be detrended, up to the hemisphere indicator		# .nii.gz time series to be detrended, up to the hemisphere indicator
	# e.g., fmcpr.up.sm4.~~self~~.?h.mgh would use fmcpr.up.sm4		# e.g., fmcpr.up.sm4.fsaverage.?h.mgh would use fmcpr.up.sm4
	# as the filepattern, and ~~self~~ as the surface		# as the filepattern, and fsaverage as the surface
	annot="$1"		annot="$1"
	shift		shift

Chris: /* Save Region Names */

2019-06-12T16:55:12Z

Save Region Names

← Older revision		Revision as of 12:55, 12 June 2019
Line 118:		Line 118:

	==== Save Region Names ====		==== Save Region Names ====
	To make a single cell array containing the names of all your regions, you'll need to concatenate your ~~lh and rh~~ region names, and prefix them to distinguish between regions with corresponding names in each hemisphere:		To make a single cell array containing the names of all your regions, you'll need to concatenate your region names, and prefix them to distinguish between regions with corresponding names in each hemisphere:
	%Concatenate the ~~499 + 501 element~~ cell arrays into a single ~~1000-element~~ cell array		%Concatenate the 3 cell arrays into a single cell array
	%Note that I am concatenating the lh label names followed by the rh label names because		%Note that I am concatenating the lh label names followed by the rh label names and then mni label names because
	%that is the order of the columns in the time series data (reading across columns left to right)		%that is the order of the columns in the time series data (reading across columns left to right)
	labels=vertcat(lh_region_names, rh_region_names);		labels=vertcat(lh_region_names, rh_region_names, mni_region_names);

	%Now make a list of 'lh.' and 'rh.' prefixes that I will prepend to each of the labels		%Now make a list of 'lh.' and 'rh.' prefixes that I will prepend to each of the labels
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	ells=repmat({'lh.'},length(lh_region_names),1);		ells=repmat({'lh.'},length(lh_region_names),1);
	arrs=repmat({'rh.'},length(rh_region_names),1);		arrs=repmat({'rh.'},length(rh_region_names),1);
	prefixes=vertcat(ells,arrs);		emms=repmat({'mni.'},length(mni_region_names),1);
			prefixes=vertcat(ells,arrs, emms);

	%Now use cellfun to prepend my prefixes to my labels		%Now use cellfun to prepend my prefixes to my labels

Chris: /* Decoding the Regions */

2019-06-12T16:52:10Z

Decoding the Regions

← Older revision		Revision as of 12:52, 12 June 2019
Line 93:		Line 93:
	%if called without providing filename will launch a dialog box to select the .sum.txt to parse		%if called without providing filename will launch a dialog box to select the .sum.txt to parse
	lh_summary=parseFSSegments();		lh_summary=parseFSSegments();
			rh_summary=parseFSSegments();
	%If you already know the filename, you can provide it as a parameter		%If you already know the filename, you can provide it as a parameter
	~~rh_summary~~=parseFSSegments('~~FS_T1_501_rh_myaparc_60_005~~.fmcpr.~~sm6~~.sum.txt');		mni_summary=parseFSSegments('FS_0183_subcort_lausanne_001.fmcpr.up.sm4.sum.txt');

	lh_region_names=lh_summary.segname;		lh_region_names=lh_summary.segname;
	rh_region_names=rh_summary.segname;		rh_region_names=rh_summary.segname;
			mni_region_names=mni_summary.segname;

	If there are regions that you have dropped from your time series data (e.g., Corpus Callosum), you will likely want to also drop those labels from your list of region names to maintain a correspondence between the region labels and your time series columns:		If there are regions that you have dropped from your time series data (e.g., Corpus Callosum), you will likely want to also drop those labels from your list of region names to maintain a correspondence between the region labels and your time series columns:
Line 104:		Line 106:
	lh_region_names=lh_region_names(2:end);		lh_region_names=lh_region_names(2:end);
	rh_region_names=rh_region_names(2:end);		rh_region_names=rh_region_names(2:end);
			If you have saved the vectors of dropped columns, this would be a handy place to recycle this information:
			lidx=1:length(lh_summary.segname);
			lidx(ldrop)=[];
			lh_region_names=lh_summary.segname(lidx);
			ridx=1:length(rh_summary.segname);
			ridx(rdrop)=[];
			rh_region_names=rh_summary.segname(ridx);
			mnidx=1:length(mni_summary.segname);
			mnidx(mnidrop)=[];
			mni_region_names=mni_summary.segname(mnidx);

	==== Save Region Names ====		==== Save Region Names ====

Chris at 03:12, 12 June 2019

2019-06-12T03:12:22Z

← Older revision		Revision as of 23:12, 11 June 2019
Line 1:		Line 1:
	After linear trends have been [[Detrending_FreeSurfer_Data removed]] from the data, we can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:		After linear trends have been [[Detrending_FreeSurfer_Data \| removed]] from the data, we can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:
	=== gettimecourses.sh ===		=== gettimecourses.sh ===
	'''Note:''' If you have a gettimecourses.sh in your ~/bin/ directory, it is likely obsolete. I have been copying updated versions of my shell scripts to /usr/local/sbin. If this script exists in that folder, you should consider deleting the version in your ~/bin/ directory so that the most up-to-date version is used instead. As of June, 2019, this script now extracts the subcortical data in mni305 space.		'''Note:''' If you have a gettimecourses.sh in your ~/bin/ directory, it is likely obsolete. I have been copying updated versions of my shell scripts to /usr/local/sbin. If this script exists in that folder, you should consider deleting the version in your ~/bin/ directory so that the most up-to-date version is used instead. As of June, 2019, this script now extracts the subcortical data in mni305 space.

Chris at 03:12, 12 June 2019

2019-06-12T03:12:11Z

← Older revision		Revision as of 23:12, 11 June 2019
Line 1:		Line 1:
	We can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:		After linear trends have been [[Detrending_FreeSurfer_Data removed]] from the data, we can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:
	=== gettimecourses.sh ===		=== gettimecourses.sh ===
	'''Note:''' If you have a gettimecourses.sh in your ~/bin/ directory, it is likely obsolete. I have been copying updated versions of my shell scripts to /usr/local/sbin. If this script exists in that folder, you should consider deleting the version in your ~/bin/ directory so that the most up-to-date version is used instead. As of June, 2019, this script now extracts the subcortical data in mni305 space.		'''Note:''' If you have a gettimecourses.sh in your ~/bin/ directory, it is likely obsolete. I have been copying updated versions of my shell scripts to /usr/local/sbin. If this script exists in that folder, you should consider deleting the version in your ~/bin/ directory so that the most up-to-date version is used instead. As of June, 2019, this script now extracts the subcortical data in mni305 space.

Chris: /* gettimecourses.sh */

2019-06-10T17:23:43Z

gettimecourses.sh

← Older revision		Revision as of 13:23, 10 June 2019
Line 4:		Line 4:

	#!/bin/bash		#!/bin/bash
	USAGE="Usage: gettimecourses.sh annot surface ~~filepattern~~ sub1 ... subN"		USAGE="Usage: gettimecourses.sh annot filepattern surface sub1 ... subN"

	if [ "$#" == "0" ]; then		if [ "$#" == "0" ]; then
Line 13:		Line 13:
	# first two parameters are is the annot files and filepatterns for the \		# first two parameters are is the annot files and filepatterns for the \
	# .nii.gz time series to be detrended, up to the hemisphere indicator		# .nii.gz time series to be detrended, up to the hemisphere indicator
	# e.g., ~~detrend~~.~~fmcpr~~.~~sm6~~.self.?h.~~nii.gz~~ would use ~~detrend~~.~~fmcpr~~.~~sm6~~		# e.g., fmcpr.up.sm4.self.?h.mgh would use fmcpr.up.sm4
	# as the filepattern, and self as the surface		# as the filepattern, and self as the surface
	annot="$1"		annot="$1"

Chris at 19:21, 7 June 2019

2019-06-07T19:21:11Z

← Older revision		Revision as of 15:21, 7 June 2019
Line 1:		Line 1:
	We can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:		We can calculate mean time course vectors calculated across all voxels within regions defined in a FreeSurfer annotation (.annot) file (we can also extract the time courses on a vertex-by-vertex basis -- [[#Voxelwise_Time_Series \| see below]]). Though any annotation file can be used for this purpose, we have been working at the scale of the Lausanne parcellation. A script exists, <code>gettimecourses.sh</code>, that will handle this:
	=== gettimecourses.sh ===		=== gettimecourses.sh ===
	'''Note: If you have a gettimecourses.sh in your ~/bin/ directory, it is likely obsolete. I have been copying updated versions of my shell scripts to /usr/local/sbin. If this script exists in that folder, you should consider deleting the version in your ~/bin/ directory so that the most up-to-date version is used instead. As of June, 2019, this script now extracts the subcortical data in mni305 space.		'''Note:''' If you have a gettimecourses.sh in your ~/bin/ directory, it is likely obsolete. I have been copying updated versions of my shell scripts to /usr/local/sbin. If this script exists in that folder, you should consider deleting the version in your ~/bin/ directory so that the most up-to-date version is used instead. As of June, 2019, this script now extracts the subcortical data in mni305 space.

	#!/bin/bash		#!/bin/bash