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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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Results 1 - 30 of 367
After a vast amount of pointless wandering through the wastelands of cluster-size thresholding, the ETAC manuscript is online:
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Bob Cox
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AFNI Message Board
Are there any AFNI users who still want the support for MINC-1 datasets (.mnc files)? I'm thinking of removing support for these from the source code, since they are a relic from the ancient past -- 2001, to be accurate.
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Bob Cox
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AFNI Message Board
I have used 3dQwarp with the '-emask' option. Here, 'e' means "exclude" -- that is, the option provides a mask of voxels NOT to use in the alignment process. There are some details you have to understand to use this properly:
You have to learn to draw the exclusion mask over the subject's image dataset, which isn't hard but takes a little practice. This
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Bob Cox
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AFNI Message Board
The InstaCorr menu on the Define Overlay control panel has a new entry: 3dTstat.
This menu item will let you interactively run 3dTstat to compute a voxelwise statistic from a time series dataset. There are 43 statistics available.
One new such statistics is MSSD = Mean Square Successive Differences = a measurement of how much the dataset fluctuates between neighboring time points. Related to th
by
Bob Cox
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AFNI Message Board
You can use 3dQwarp with options "-maxlev 0 -noQ -allineate" to make it compute a warp with the affine part ("-allineate") plus a single 3D cubic polynomial ("-maxlev 0 -noQ"). These results should be similar to the mostly useless bilinear warp and will be available for use with other programs, such as 3dNwarpApply.
"-noQ" is a hidden option, which just
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Bob Cox
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AFNI Message Board
An easier way to sum up across columns is with
3dTstat -prefix stdout: -sum SPMout.xmat.1D > SPMvector.1D
This is simpler, since 1dsum sums down columns, not across columns, which is why the 1dtranspose program has to be used in the earlier example.
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Bob Cox
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AFNI Message Board
Not at this time. The only way to do that would be to use 3dExtractGroupInCorr to break individual datasets out of the .niml/.data files, then reassemble the subset you like with 3dSetupGroupInCorr.
I can consider adding this subset capability as an option, but I don't know when I will get around to it. I am glad to hear someone using 3dGroupInCorr, since it doesn't seem to get much
by
Bob Cox
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AFNI Message Board
I have some results for you, based on the datasets you provided. Please check your email.
Long story made short: use the @SSwarper script, not auto_warp.py, to take human brain images to MNI space. In particular, using the nonlinearly aligned MNI template gives more accurate results than the older linearly aligned template.
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Bob Cox
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AFNI Message Board
I'm looking at the data now. Will try to understand what's going on.
by
Bob Cox
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AFNI Message Board
I forgot to mention that the AFNI program timing_tool.py can manipulate stimulus timing files for you, including adding/subtracting values from a given file.
https://afni.nimh.nih.gov/pub/dist/doc/program_help/timing_tool.py.html
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Bob Cox
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AFNI Message Board
I've seen this happen a couple times, and don't know what it is. I have only seen it when using a networked filesystem, so it is possible that there is something that can happen there -- a timeout when the filesystem is busy?
The program can proceed because the minmax.1D files are a side-calculation to compute the single-voxel threshold that would give a 5% (say) false positive rate,
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Bob Cox
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AFNI Message Board
I see -- you are new to AFNI.
Always look at the data. Just because a data was put on a Web repository doesn't mean it is OK. We have seen things where images were oriented incorrectly, "skull stripped" images still had the skull on, and so forth. AFNI is good for looking at 3D datasets.
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Bob Cox
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AFNI Message Board
After you extract the 503 time series into a text .1D file (as described in my earlier post), you could correlate them with any other dataset, not just the one they were taken from. So you could downsample the original dataset. Which (unless it was already blurred) should be done with a little bit of blurring first (3dmerge -1blur_fwhm 4 ..., or 3dBlurInMask -FWHM 4 ...) followed by 3dresample.
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Bob Cox
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AFNI Message Board
The direct way to do this would be to
1) extract the 503 time series from the ROI using a command like
3dmaskdump -noijk -mask ROImask.nii -o ROIdump.1D timeseries.nii
2) transpose the output file so that each column is a voxel timeseries, rather than each row
1dtranspose ROIdump.1D > ROIts.1D
3) Produce a 503 sub-brick dataset of the correlation of each column with all voxels:
3dTco
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Bob Cox
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AFNI Message Board
I'm adding a feature to the AFNI GUI to make it easier to collate all the datasets from a single subject into a "session". The source code is in github now and the next binary build will have it incorporated.
As far as BIDS-ification of AFNI scripts, nothing to report on that battle front.
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Bob Cox
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AFNI Message Board
When you switch the threshold slider to be 'Pos only' (or 'Neg only'), the program "knows" that it is now a 1-sided test, so it adjusts the p-value of a t-statistic. That is what you are seeing.
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Bob Cox
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AFNI Message Board
Your simple sounding question has only complicated answers.
How did you align the subjects? We recommend 3dQwarp (or its use in the @SSwarper or auto_warp.py scripts). Unless activation regions are large and strong, affine alignment does not do a good job. 3dMean is *not* a statistical analysis program for groups. It is just a convenience program for averaging datasets together. In particula
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Bob Cox
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AFNI Message Board
From the help for 3dAllineate https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dAllineate.html:
* The option '-nwarp_save sss' lets you save a 3D dataset of the
the displacement field used to create the output dataset. This
dataset can be used in program 3dNwarpApply to warp other datasets.
++ If the warp is symbolized by x -> w(x) ,
then the '-nwarp_save&
by
Bob Cox
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AFNI Message Board
A possible solution is the Unix command line utility 'cut'. For example, if file fred.txt is
1 2 3 4 5 6 7
1 2 3
1 2 3 4 5
Then the command
cut -f 1,3,5,7,9 -d ' ' fred.txt
produces (to stdout) the results
1 3 5 7
1 3
1 3 5
That's the odd numbered columns, separated by the space ' ' character (up to column #9). By putting in at least as many column
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Bob Cox
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AFNI Message Board
It is possible that inclusion of both gcor and average motion as covariates would help adjust for differences in connectivity caused by artifacts. We have seen this work in our trial analyses. Is this method guaranteed to work? Of course not -- nothing in FMRI is known for sure. And different people (including paper reviewers) will have different opinions on this subject.
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Bob Cox
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AFNI Message Board
Not at this time. I could consider adding it, but that would basically require forcing all datasets (internally in the GUI) to be coded as having the same "view"
by
Bob Cox
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AFNI Message Board
These are the usual suspects to be rounded up. Be sure to note that '1' in the censor file means 'keep this time point', and '0' means 'ignore this time point'.
Some people think the file motion_SUBJID_enorm.1D is also useful -- it measures the size of the motion. However, I don't think it's particularly relevant for regression purposes.
Fin
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Bob Cox
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AFNI Message Board
The difficulty with such volumes is that the through-slice resolution is typically 5 mm, which makes the in-slice images look good (very high SNR), but makes it hard to match them to a template dataset at 1 mm resolution. It is possible to resample them to a 1 mm grid in all 3 directions ("isotropic"), and then try the standard alignment tools. However, segmentation will be inaccurate s
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Bob Cox
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AFNI Message Board
17 subjects with 6 covariates each means you will have only 10 degrees of freedom. An effect will have to be pretty large to be seen!
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Bob Cox
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AFNI Message Board
If you want to duplicate the work in the paper by Wall (et al.), then you have to find out the parameters that BrainVoyager uses. I cannot find that information after spending all of 2 minutes with Google.
You can choose the K and W (peak and width) parameters based on looking at data, or at the models Wall used. Probably the exact values won't matter much in the final results. The r val
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Bob Cox
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AFNI Message Board
The following changes are in the AFNI source code now, but will not be available in binaries until Tuesday morning 09 Jan 2018 (Tuesday evening in China).
I had planned to explain how to specify a two gamma variate model with the 'EXPR' model in 3dDeconvolve. But this is complicated and easy to use incorrectly. So instead, I modified the program to have a new model:
TWOGAM(p1,q1,r,p
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Bob Cox
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AFNI Message Board
Unfortunately, the article referenced does not give the formula for the HRF they used, they just say it is implemented in BrainVoyager. So it is a little difficult to fully address your question.
There are a couple ways you can proceed, from easy to hard.
FIRST method: GAM(p,q) is described in detail in the output of 3dDeconvolve -help:
https://afni.nimh.nih.gov/pub/dist/doc/program_help/
by
Bob Cox
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AFNI Message Board
You can also read in image files (.jpg and .png) as "datasets", as in
afni *.jpg
These can only be opened as Axial images, since they are only 1 "voxel" thick.
You can read directories recursively, as in
afni -R1
which will read datasets from the current directory and all its immediate (1 level deep) sub-directories. You can descend deeper with "-R2" and even de
by
Bob Cox
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AFNI Message Board
Page 1 of 13
Pages: 12345