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Dear AFNI users-
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
https://discuss.afni.nimh.nih.gov
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely,
AFNI HQ
History of AFNI updates
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Hi Paul,
thanks for the elaborated explanation.
QuoteThe values in the errts do reflect BOLD % signal change. I might just state it as "BOLD % signal change." I guess you could state it as "0.34% BOLD signal change from estimated baseline", but I would not state "0.34% BOLD signal change from errts baseline of 0". Maybe that is splitting hairs, but the latter s
by
Philipp
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AFNI Message Board
Hi Paul,
to clarify
Quote... and so it creates a dataset that has the interpretation of "local BOLD % change" at each time series; the residuals have these units.
does this mean that a value of for example + 0.34 (in the preprocessed .errts file) is a 0.34 % increase in BOLD signal compared to the baseline of 0?
by
Philipp
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AFNI Message Board
Hi,
I have a question regarding scaling in the preprocessing using AFNI proc.
When preprocessing a run with a resting-state script using the following blocks
-blocks despike tshift blur mask scale regress \
the .errts output file does not have a mean of 100 and it lacks the range from 0 to 200 (as the "scale" preprocessing block).
It seems that the .errts mean is 0, but how
by
Philipp
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AFNI Message Board
Rick,
one more question came up. When exactly does AFNI proc compute the global signal? If I have the following blocks in AFNI proc including the anaticor fast method
-blocks despike tshift blur mask scale regress \
is the global signal computed after despiking, tshift, blurring, anaticor etc. or does AFNI compute the global signal based on the raw functional run (or somewhere in betwee
by
Philipp
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AFNI Message Board
Hi Rick,
the problem is that they did not supply any head motion estimations for the 7T data at all.
However, after checking the data more, it looks as if their so-called minimally preprocessed data already included a perfect motion correction.
When I run through the single volumes, the brain looks almost perfectly static, i.e., there is almost zero motion.
This is what they state in thei
by
Philipp
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AFNI Message Board
Hi and thank you.
Example 11b applies for my case, because I do not use freesurfer data, but a ROI that I created in AFNI (a NIFTI file). This ROI file is based on the anatomical MNI152 template (but only using the cerebral cortex, not the whole brain) and hence the same for every subject.
Based on my understanding, the code should then look as follows, correct?
-mask_import cc cerebra
by
Philipp
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AFNI Message Board
Dear all,
I am interested in comparing ROI-based results with vs. without the application of global signal regression (GSR).
In other words, I am more interested in the signal component of the global signal, and not so much in the noise.
AFNI proc allows to run GSR via the option
-regress_roi brain
I assume that this option computes the global signal by taking all voxels of the brai
by
Philipp
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AFNI Message Board
Dear all,
I have a quick question regarding the combined use of
-regress_bandpass x y
-regress_polort x
in AFNI proc.
By default, AFNI proc automatically adjusts the option "regress_polort" depending on the run’s length. I often read the recommendation here on the AFNI board that IF one really wants to bandpass the data, say from 0.01-0.1 Hz, one should additionally set &q
by
Philipp
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AFNI Message Board
Dear all,
I am currently working with the HCP 7T minimally preprocessed dataset. Initially, I tried to preprocess the raw data (which the HCP also provides). However, I face too many problems in preprocessing their raw data with AFNI. The HCP information also states that their raw data is a bit special and requires more elaborated preprocessing methods. Since my knowledge is not sufficient to
by
Philipp
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AFNI Message Board
Paul,
thanks you for the detailed answer, this is much appreciated. A final question.
Say I am interested in extracting AFNI functional data that already has 3x3x3 mm voxels using 3dmaskdump in the neurological convention.
In this case, I would add the option
-nbox 3 3 3
to the 3dmaskdump script, correct? The extracted data would then correspond to the neurological or LPI-SPM conventi
by
Philipp
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AFNI Message Board
Paul,
I think that I maybe solved the problem now. The problem was that I extracted the networks’ coordinates with the 3dmaskdump option
-xyz
enabled.
By "networks coordinates" I mean the coordinates of the 7 AFNI Schaefer-Yeo networks that I initially created, plus a combination of all 7 networks together, where I added the 7 single networks together with 3dcalc because I also
by
Philipp
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AFNI Message Board
Ok, I think that I found the problem.
What a mess, but let me explain, I now remember another detail that I did here.
I extracted the voxel-based time series with 3dmaskdump using the option "-no ijk" so that the resulting .1D files do NOT include the voxel coordinates.
Why? Because I wanted to run interpolation in Python, and it was easier for me to script it this way.
Subsequ
by
Philipp
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AFNI Message Board
Hi Paul,
here is the output:
1 1 1 1 1 ACW_Awake_Global.nii
1 1 1 1 1 Resample_Yeo7N_1000ribbon.nii
1 1 1 1 1 errts.Subject1_Awake.anaticor
And you are right. I guess that I can skip using the "Resample_Yeo7N_1000ribbon.nii" here, and directly use the coordinates from the .errts file.
However, now I tell you something interesting.
When I don'
by
Philipp
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AFNI Message Board
Hi Paul,
I will try to specifiy what my aim is. I compute the temporal autocorrelation (AC) for each voxel. How do I do that?
1. I extract the voxel-based time-series from the preprocessed AFNI errts. file (aligned to MNI152) using 3dmaskdump into one .1D file per subject.
2. Next, I load this .1D file into Python. I compute the AC for every voxel and save those voxel values in a big list.
by
Philipp
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AFNI Message Board
Paul, sorry, it was a long day for me. I indeed made a typo, hence AFNI could not find one file.
Here is the new output:
Resample_Yeo7N_1000ribbon.nii
1 1 1 1 1 ACW_Awake_Global.nii
1 1 1 1 1 Resample_Yeo7N_1000ribbon.nii
Note that this output (the ACW_Awake_Global.nii file) was generated after I multiplied the y-coordinates with -1 in Python, leaving the x and z coordinates un
by
Philipp
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AFNI Message Board
Paul,
the two .nii datasets that I used are:
"Extracted_ACW_Awake.nii" and "Resample_Yeo7N_1000ribbon.nii". Hence I use the code, where both files are in the same folder:
3dinfo \
-same_all_grid \
-prefix \
Extracted_ACW_Awake.nii \
Resample_Yeo7N_1000ribbon.nii
which prints
NO-DSET NO-DSET NO-DSET NO-DSET NO-DSET NO-DSET
0 0 0 0 0 Resample_Yeo7N_1000ribbo
by
Philipp
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AFNI Message Board
I think the whole problems stems from the radiological vs. neurological coordinates.
My goal is to display AFNI preprocessed data on the MNI 152 template.
To make it short: would the correct solution to the problem be to simply extract voxel-based data using 3dmaskdump via adding the option
-nbox 3 3 3
to 3dmaskdump?
As I understand, -nbox 3 3 3 would extract the voxel-based data in the
by
Philipp
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AFNI Message Board
Dear Paul,
thank you.
I read on Andy’s Brain Blog (https://www.andysbrainblog.com/andysbrainblog/tag/3dUndump) that AFNI’s xyz coordinates correspond to MNI152’s x -y z.
More precisely, the positive y value in AFNI’s xyz coordinates corresponds to a negative y value in MNI152 (and vice versa).
So I went back to my Python script that initially loads the output of 3dmaskdump. In Python, I m
by
Philipp
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AFNI Message Board
Here is a picture between the 3dundump output (above, first row) and the resampled anatomical template (second row).
The mismatch is clear between those files, hence the mismatch occurs within 3dundump, because in all previous processing steps, the voxel coordinates are exaclty the same.
I wonder what needs to be changed in the 3dundump script?
by
Philipp
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AFNI Message Board
Hi,
this is going to be a bit complicated, but I try to describe my problem as simple as possible.
I extracted voxel-based time-series from AFNI functional scans (3x3x3 mm voxels) using 3dmaskdump. Subsequently, I loaded the extracted time-series into Python, and computed one measurement for each voxel (meaning I get one value per voxel, such as 7.54 or 2.13).
Furthermore, I also extract
by
Philipp
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AFNI Message Board
Thank you, Paul.
This is exactly what I wanted (or needed). Perfect!
Philipp
by
Philipp
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AFNI Message Board
Dear all,
I would like to extract all regions of the auditory cortex from the HCP Glasser 360 atlas that is already contained in AFNI, that is, from the file "MNI_Glasser_HCP_v1.0.nii.gz" in AFNI’s abin folder. I am refering to all red colored regions in the Figure on the right here:
In their paper, Glasser et al. included the following regions for the early auditory cortex: &quo
by
Philipp
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AFNI Message Board
Refering to this question again:
QuoteAs I understand it now, the general approach is as follows:
-regress_polort DEGREE: generally the better approach to better preserve DOF. It also takes care of slow scanner drift impacts etc.
-regress_bandpass x y: might be required in certain cases, like in my case when computing the autocorrelation.
I am still interested in your opinion, especiall
by
Philipp
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AFNI Message Board
Hi Paul,
the thing is that I get much shorter autocorrelation values when running "-regress_bandpass 0.01 0.23" (0.23 Hz is the Nyquist frequency in my case) compared to the automatically computed "-regress_polort" option by AFNI. The latter option is automatically computed because I did not include this option with a specific value, such as "-regress_polort 2" in
by
Philipp
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AFNI Message Board
Dear all,
I have general questions regarding bandpassing via "-regress_bandpass x y" in AFNI proc. I know that AFNI recommends to avoid bandpassing, but isn't it required in some cases (see below)?
1. Does the polort option "-regress_polort x" automatically take care of noise sources like slow scanner drift/noise etc.? Does this option function a bit like a high-pas
by
Philipp
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AFNI Message Board
QuoteSo given the choice between no censoring and interpolated censoring, it is difficult. High motion has drawbacks for each. If no censoring is done, that will probably drive down the AC values
Good points, thats what I thought too. Since some subjects showed kinda heavy head motion now and then, I wanted to censore and interpolate instead of "losing" or reducing the autocorrelatio
by
Philipp
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AFNI Message Board
I am interested in the autocorrelation itself (as an end product so to speak). For example, I am interested at what TR (or seconds) the autocorrelation reaches or drops below 0 (first zero crossing).
How would you measure that, generally speaking? Options:
1. No motion censoring in AFNI, hence no interpolation.
2. Motion censoring in AFNI, then some kind of interpolation.
3. Motion censorin
by
Philipp
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AFNI Message Board
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