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If you are looking at a speaking vs nonspeaking condition, my guess is  that it is head movement. At the edge of the volume, you will get large signal changes that you would not normally see in a whole head acq. I think it is ok to just clip those slices. If you use the mri_convert command below, you will not need a different registration (I think). The question is where to do it. You could apply it to the MC'ed data, eg,
mri_convert fmc.nii.gz fmc.cutends.nii.gz --cutends 1
Then in mkanalysis-sess specify the input with --funcstem fmc.cutends
If you are doing STC, then do it after the STC stage.

On 8/21/2023 2:01 PM, Itzik Norman wrote:

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Hi Doug, 
The task is an auditory memory task (spoken sentences) and I get beautiful activations in the primary auditory cortex, STG, STS, and hippocampus. So except for these edge effects, everything else appears to be normal.
The motion correction was done using 'preproc-sess', either with per-run or per-session  (I get this problem with both methods), and my subjects are well-trained, showing only minimal movement.
I noticed this artifact in all participants so far (N=5), so I'm not sure head motion alone can explain this.
Could it be related to the way 'selxavg3-sess' handles edge voxels?

Thanks for the help,
Itzik

On Sun, Aug 20, 2023 at 2:45 PM Douglas N. Greve <dgreve@mgh.harvard.edu> wrote:

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I'm not sure how to remove those edge effects. My guess is that they are motion artifacts (eventhough you are using motion correction regressors). Does the activation otherwise look ok given your task? I suppose you could just remove those slides by hand with mri_convert --cutends ...

On 8/16/2023 1:41 AM, Itzik Norman wrote:

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Dear FreeSurfer team,

I'm using fsfast to analyze 1.2mm^3 7T BOLD data collected with a partial coverage over the hippocampus and superior temporal lobe.   

I ran preprocessing with the following flags: 

preproc-sess
-d /functional-data
-s SUB01
-fsd bold
-surface SUB01 lhrh
-fwhm 2
-init-header
-bbr-int /functional-data/SUB04/bold mT2_WB
-per-session

And then I ran the GLM analysis (stimulus-vs-baseline) using selxavg3-sess, keeping the data in the subject's native space (using the '-native' flag combined with '-per-session').

The resulting t-map shows a diagonal line of spurious activation along the edge of the functional volume. Is there a way to eliminate this artifact? (see figures attached)

Moreover, is there a way to use the -native flag together with -per-run? 

I want to simultaneously measure the hippocampal activation (in native space) and cortical activity smoothed on the surface; but I want all the data to be processed the same way (-per-run), and currently the volume analysis forces me to use -per-session.

I haven't encountered any previously documented issues similar to this one.

Please let me know if you have any thoughts. 

1) FreeSurfer build stamp: freesurfer-darwin-macOS-7.3.2-20220804-6354275
2) Platform: Mac Studio M1 (macOS)
3) selxavg3-sess log: attached
4) analysis config file: attached

Thanks,

Itzik

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