Selxavg3-sess - Revision history

Chris: /* Running selxavg3-sess */

2020-12-07T19:16:29Z

Running selxavg3-sess

← Older revision		Revision as of 15:16, 7 December 2020
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	== Running selxavg3-sess ==		== 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.		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.

			On workstation 3, selxavg3-sess takes 40 minutes to perform a GLM for 12 runs on a single surface.

	=== Batch mode ===		=== Batch mode ===

Chris: /* Before you begin */

2020-12-07T19:15:28Z

Before you begin

← Older revision		Revision as of 15:15, 7 December 2020
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	== Before you begin ==		== 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.		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. Also ensure that your subject folders contain a subjectname file.

	== Running selxavg3-sess ==		== Running selxavg3-sess ==

Chris: /* What Next? */

2020-05-23T14:59:48Z

What Next?

← Older revision		Revision as of 10:59, 23 May 2020
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	With the process safely running in the background, I am free to shut down my laptop, confident that when I check back later on, the first level GLM analysis of all my subjects will have been completed.		With the process safely running in the background, I am free to shut down my laptop, confident that when I check back later on, the first level GLM analysis of all my subjects will have been completed.
	== What Next? ==		== What Next? ==
	If all goes well, you can move on to the [[~~Freesurfer_Group-Level_Analysis_(mri_glmfit)~~ group level analysis]]		If all goes well, you can move on to the [[Mri_glmfit \| group level analysis]]

	== Troubleshooting ==		== Troubleshooting ==

Chris at 14:59, 23 May 2020

2020-05-23T14:59:00Z

← Older revision		Revision as of 10:59, 23 May 2020
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	cat nohup.out		cat nohup.out
	With the process safely running in the background, I am free to shut down my laptop, confident that when I check back later on, the first level GLM analysis of all my subjects will have been completed.		With the process safely running in the background, I am free to shut down my laptop, confident that when I check back later on, the first level GLM analysis of all my subjects will have been completed.
			== What Next? ==
			If all goes well, you can move on to the [[Freesurfer_Group-Level_Analysis_(mri_glmfit) group level analysis]]

	== Troubleshooting ==		== Troubleshooting ==

Chris: /* ERROR: fast_selxavg3() */

2020-05-21T18:14:58Z

ERROR: fast_selxavg3()

← Older revision		Revision as of 14:14, 21 May 2020
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	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.		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()~~ ===		=== ERROR: Subscript indices must either be real positive integers or logicals===

	<code>ERROR: fast_selxavg3() failed\n</code>		<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>		Leading up to the error message, we see a message <code>Subscript indices must either be real positive integers or logicals.</code>

			There are two plausible causes here, both related to your PAR files.

	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.		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.

			Another cause concerns the presence of NAN values in the event duration column in the par files when these files are generated from our MATLAB runtime files -- a NAN may be recorded for the participant response time if the participant failed to respond.

	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.		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 ====		==== 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.~~		I've written a script, '''sane_par.sh''', which you should find in your $PATH:
	~~editfslhd f.nii~~		sane_par.sh FS_0000
	~~For FS_0092~~, ~~we see~~		The script will go into all the run directories listed in the subject's bold/runs file, identify all the .par files, and truncate the .par file entries so that none of the events fall past the end of the scan, calculated my multiplying the TR by the number of volumes in f.nii.gz. It will also remove any NaN entries in the event duration column.
	~~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 ===		===ERROR: analysis.cfg does not exist ===

Chris: /* Example selxavg3-sess shell script */

2019-06-03T18:52:40Z

Example selxavg3-sess shell script

← Older revision		Revision as of 14:52, 3 June 2019
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	do_selx FAM sm4		do_selx FAM sm4

	This process takes a little while to run, especially when running a batch of several participants. When running it remotely (i.e., connected over the internet via ssh), I didn't want to have to worry about the process stopping part-way through because of a network disconnection~~, so I ran~~ it as a background process using the <code>nohup</code> command, which ensures that the process continues, even if the parent shell process terminates:		This process takes a little while to run, especially when running a batch of several participants. When running it remotely (i.e., connected over the internet via ssh), I didn't want to have to worry about the process stopping part-way through because of a network disconnection. My solution was to run it as a background process using the <code>nohup</code> command, which ensures that the process continues, even if the parent shell process terminates:
	nohup do_selx FAM sm4 &		<span style="color:red;">nohup</span> do_selx FAM sm4 <span style="color:red;">&</span>
	All the screen output will get redirected to nohup.out if you wish to track the progress of the background process:		All the screen output will get redirected to nohup.out if you wish to track the progress of the background process:
	cat nohup.out		cat nohup.out

Chris: /* Example selxavg3-sess shell script */

2019-06-03T18:51:05Z

Example selxavg3-sess shell script

← Older revision		Revision as of 14:51, 3 June 2019
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	do_selx FAM sm4		do_selx FAM sm4

			This process takes a little while to run, especially when running a batch of several participants. When running it remotely (i.e., connected over the internet via ssh), I didn't want to have to worry about the process stopping part-way through because of a network disconnection, so I ran it as a background process using the <code>nohup</code> command, which ensures that the process continues, even if the parent shell process terminates:
			nohup do_selx FAM sm4 &
			All the screen output will get redirected to nohup.out if you wish to track the progress of the background process:
			cat nohup.out
			With the process safely running in the background, I am free to shut down my laptop, confident that when I check back later on, the first level GLM analysis of all my subjects will have been completed.

	== Troubleshooting ==		== Troubleshooting ==

Chris: /* Example selxavg3-sess shell script */

2019-05-31T17:36:34Z

Example selxavg3-sess shell script

← Older revision		Revision as of 13:36, 31 May 2019
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	===Example selxavg3-sess shell script===		===Example selxavg3-sess shell script===
	#!/bin/bash		#!/bin/bash
	PARADIGM=$1		PARADIGM=$1
	shift		shift
	SMOOTHING=$1		SMOOTHING=$1
	shift		shift
	AROOT=${PARADIGM}.${SMOOTHING}		AROOT=${PARADIGM}.${SMOOTHING}
	HEMIS=( lh rh mni )		HEMIS=( lh rh mni )
	for hemi in "${HEMIS[@]}"		for hemi in "${HEMIS[@]}"
	do		do
	selxavg3-sess -sf subjects -analysis ${AROOT}.${hemi}		selxavg3-sess -sf subjects -analysis ${AROOT}.${hemi}
	done		done

	I saved the script to a file called '''do_selx''', and after I made it executable, I was able to automate running the script on all the subjects in the '''subjects''' session file:		I saved the script to a file called '''do_selx''', and after I made it executable, I was able to automate running the script on all the subjects in the '''subjects''' session file:

Chris at 17:36, 31 May 2019

2019-05-31T17:36:07Z

@@ Line 17: / Line 17: @@
   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 ==

Greg: /* Solution */

2018-05-17T20:05:03Z

Solution

← Older revision		Revision as of 16:05, 17 May 2018
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	This can be done fairly easily using <code>sed</code> with the -i "in place" switch:		This can be done fairly easily using <code>sed</code> with the -i "in place" switch:
	sed -i 's/\t2/\t3/g' FAM.par		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() ===		=== ERROR: fast_selxavg3() ===