You probably need to merge the files using something like paste.

Peace,

Matt.

From: 
<[email protected]<mailto:[email protected]>>
 on behalf of "Michael F.W. Dreyfuss" 
<[email protected]<mailto:[email protected]>>
Date: Thursday, August 4, 2016 at 6:15 PM
To: "Burgess, Gregory" <[email protected]<mailto:[email protected]>>
Cc: "[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Subject: Re: [HCP-Users] Movement Confounds and Censoring


Thank you. On a practical note, if I want to include both motion regressors and 
time points censoring with a censor file I produce for each run how can I do 
that? I am refering specifically to the following chunk of code:


#Create design files, model confounds if desired

DIR=`pwd`

cd ${FEATDir}

if [ $Confound= "NONE"] ;then

  feat_model ${FEATDir}/design

else

  feat_model ${FEATDir}/design ${ResultsFolder}/${LevelOnefMRIName}/${Confound}

fi

cd $DIR


When I change that to the code below including another argument for thecensor 
file, the result is the same as if I added no confound (it ignores both 
arguments):


feat_model ${FEATDir}/design ${ResultsFolder}/${LevelOnefMRIName}/${Confound} 
${ResultsFolder}/${LevelOnefMRIName}/censor.txt


How can I include both motion regression and censoring files in the design 
files using feat_model or another command?


Thank you,

Michael

________________________________
From: Burgess, Gregory <[email protected]<mailto:[email protected]>>
Sent: Thursday, August 4, 2016 10:35:33 AM
To: Michael F.W. Dreyfuss
Cc: [email protected]<mailto:[email protected]>
Subject: Re: [HCP-Users] Movement Confounds and Censoring

> On Aug 4, 2016, at 8:52 AM, Michael F.W. Dreyfuss 
> <[email protected]<mailto:[email protected]>> wrote:
>
> Thank you,
>
> Is it recommended to use the motion regressors with or without detrending on 
> data that has been processed with the HCP preprocessing scripts?

You should perform the same filtering on your confound regressors (and task 
model) that you are performing on your data. So, I’d start with the original 
Movement_Regressors.txt.


> Is there a recommended cutoff for censoring time points or a recommended 
> threshold of mean relative displacement for excluding a subject or run?

One of the interesting difficulties with HCP data (and occasionally other MB 
fMRI data from other groups) is that motion parameters from MB data pick up on 
some source of variability that is not typically visible in motion parameters 
estimated from fMRI data with lower spatial and temporal resolution. So, 
censoring thresholds that are typical in lower resolution fMRI might result in 
censoring a considerable proportion of your data. You might want to look at 
DVARS to see if it can detect apparent motion artifact more cleanly.

Either way, the best strategy is to look at your data to set your own 
threshold. (Look at strategies from Power, J. D., Mitra, A., Laumann, T. O., 
Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, 
characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 
320–341. 
https://urldefense.proofpoint.com/v2/url?u=http-3A__doi.org_10.1016_j.neuroimage.2013.08.048&d=DQIF-g&c=lb62iw4YL4RFalcE2hQUQealT9-RXrryqt9KZX2qu2s&r=rPclmYysc_z1plf99IoNsmxWf1JolkKMmL6bXnYFSwg&m=rA5E466xAGA4eFveDcDcOazqcXIKVYINMcieo_qUh1o&s=W83s5PHORheYaI8ov_xC8VSiKFk3nw9qHj8Q2IvgnNg&e=
 )


> I installed FIX and can implement it on my data, but I can only have it 
> trained to data from your scanners at Wash U since I do not have enough 
> additional data from our scanner here to train it on, so I do not feel 
> comfortable using it on my data.

You could consider hand categorizing the ICA components, if you feel 
comfortable making those judgments and you don’t have tons of data. There are 
also some other automated approaches such as AROMA that have become popular 
with folks. I have no experience with AROMA yet, but I’d imagine that it would 
rate spike-like ICA components as noise.


--Greg

____________________________________________________________________
Greg Burgess, Ph.D.
Staff Scientist, Human Connectome Project
Washington University School of Medicine
Department of Psychiatry
Phone: 314-362-7864
Email: [email protected]<mailto:[email protected]>

>
> Thank you,
> Michael
> From: Burgess, Gregory <[email protected]<mailto:[email protected]>>
> Sent: Tuesday, August 2, 2016 2:30:10 PM
> To: Michael F.W. Dreyfuss
> Cc: [email protected]<mailto:[email protected]>
> Subject: Re: [HCP-Users] Movement Confounds and Censoring
>
> > On Aug 2, 2016, at 10:17 AM, Michael F.W. Dreyfuss 
> > <[email protected]<mailto:[email protected]>> wrote:
>
> >
>
> > Hi, I have some basic questions about the movement parameters that are put 
> > out by the HCP preprocessing scripts.
>
> >
>
> > 1) What is the difference between Movement_Regressors_dt.txt and 
> > Movement_Regressors.txt? I would like to use one as a confound.
>
>
>
> The Movement_Regressors.txt are the parameters computed from the motion 
> correction algorithm. The Movement_Regressors_dt.txt is the same file put 
> through a detrend command to remove the linear trend and mean.
>
>
>
> >
>
> > 2) What does each column in these represent? I'm assuming 6 directions of 
> > displacement, and 3 for rotation?
>
>
>
> The 12 columns represent Displacement {x, y, z}, Rotation {x, y, z}, Backward 
> Derivative of Displacement {x, y, z}, and Backward Derivative of Rotation {x, 
> y, z}
>
>
>
> >
>
> > 3) What are the units in these and the other movement files in the output? 
> > mm? voxels?
>
>
>
> The units are mm for displacements and degrees for rotations.
>
>
>
> >
>
> > 4) I would like to censor time points with too much motion in 
> > Movement_AbsoluteRMS.txt. Is there a specific movement cutoff you 
> > recommend? I have made a script that produces a censor file in the fsl 
> > format (i.e. columns of all 0s with a 1 at the time point that is 
> > censored). Is there a simple way to add that censor file into the existing 
> > analysis scripts along with the motion as a confound?
>
>
>
> First, I would suggest using the Movement_RelativeRMS.txt file for censoring 
> instead. The AbsoluteRMS reflects the total movement from the motion 
> correlation registration target, which is the SBRef volume collected at the 
> beginning of the scan. In contrast, the RelativeRMS reflects the amount of 
> motion from the previous time point, which should be considered a more direct 
> measurement of movement on that specific time point.
>
>
>
> I’m not sure to which existing analysis scripts you are referring. We don’t 
> release analyses of individual scans. So, you should probably include these 
> censoring confounds as covariates in whatever preprocessing you’re doing for 
> the individual scans.
>
>
>
> After saying all of that, it is worth mentioning that FIX denoising:
>
> - Regresses a set of 24 motion parameters (six rigid body parameters, their 
> backward derivatives, and the squares of those 12 columns)
>
> - Regresses noise component variance (orthogonal to signal components) from 
> the scan-level ICA.
>
>
>
> In practice, the behavior of FIX is similar to regressing movement paremeters 
> and voxel-specific censoring (plus other artifact cleanup). If you utilize 
> FIX preprocessed data, you don’t need to regress motion parameters, and you 
> might feel comfortable skipping censoring as well.
>
>
>
> --Greg
>
>
>
> ____________________________________________________________________
>
> Greg Burgess, Ph.D.
>
> Staff Scientist, Human Connectome Project
>
> Washington University School of Medicine
>
> Department of Psychiatry
>
> Phone: 314-362-7864
>
> Email: [email protected]<mailto:[email protected]>
>
>
>
>
>
> >
>
> > Thank you,
>
> > Michael
>
> > _______________________________________________
>
> > HCP-Users mailing list
>
> > [email protected]<mailto:[email protected]>
>
> > https://urldefense.proofpoint.com/v2/url?u=http-3A__lists.humanconnectome.org_mailman_listinfo_hcp-2Dusers&d=DQIGaQ&c=lb62iw4YL4RFalcE2hQUQealT9-RXrryqt9KZX2qu2s&r=rPclmYysc_z1plf99IoNsmxWf1JolkKMmL6bXnYFSwg&m=FMg6DAhRQHwDPdwDze2CIg4j1-kMkVc9h-XJ0PXmDWQ&s=ck-KAIP80cxbdHtG0fMXtiT8T_-4QzuQ-lNVQlPrDs8&e=
>
>
>
>
>
>
>
>
>
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