Re: [Freesurfer] Pial surface does not follow the gray/csf boundary

[Greve, Douglas N.,Ph.D.]

It is hard to say. The 0.25mm may be throwing it off. You can try adding -r 
0.25. This will increase the force that repels the pial surface from the white 
by a factor of 10. That might be enough. But there are a lot of places where 
mris_make_surfaces assumes that the voxels are 1mm. Sometimes people will 
change the voxel size in the header to 1mm (so no change to the pixel data, 
just a change to the header) and then run that.

On 12/12/2019 7:07 AM, Ardesch, D.J. wrote:

External Email - Use Caution
Dear FreeSurfers,

I’m trying to create surface reconstructions for some small monkey scans (about 
half the size of a rhesus macaque). The white surfaces look fine, but I can’t 
seem to get the pial surfaces to extend properly to the edge of the gray 
matter. Is there a way to ‘force’ the mris_make_surfaces command to place the 
pial surface along a certain voxel intensity or intensity gradient?

As I understand it, mris_make_surfaces takes the ?h.orig surface and extends it 
into the gray matter until it finds the gray matter/csf boundary. I’ve 
therefore tried to edit the brain.finalsurfs.mgz and brainmask.mgz volumes such 
that all white matter voxels have an intensity of 110 and all other brain 
voxels have an intensity of 80, and added some smoothing to create an intensity 
gradient, but this did not make a difference. The gray matter surface is still 
placed about 1/5th of the way between the gray/white boundary and the gray/csf 
boundary. Adding a T2 scan did not help either.

My setup is the following:

  *   FreeSurfer v6.0.0
  *   MacOS 10.14.6
  *   Running FreeSurfer with the -hires flag (voxel size is 0.25 mm isotropic)

With some manual editing I’ve been able to complete all pipeline steps until 
the surface reconstruction, except the topology fixing because it created large 
inaccuracies in the occipital lobe (including the topology fixing step did not 
solve the problem either).

Any help would be greatly appreciated!

With kind regards,
Dirk Jan




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Re: [Freesurfer] Thickness problem

[Greve, Douglas N.,Ph.D.]

Can you try running the ROI results with mri_glmfit? ie, if you ran 
aparcstats2table to get the ROI results you input to SPSS, then run

mri_glmfit --table table.dat  --fsgd g1v4.fsgd --C group.diff.mtx  --glmdir 
table.g1v4.glmdir

where table.dat is the result of aparcstats2table

Then look at the sig.table.dat

Oftentimes, the SPSS analysis is not doing exactly the same thing as the 
mri_glmfit analysis.

On 12/6/2019 11:25 AM, Gwang-Won Kim wrote:

External Email - Use Caution

Hi there,

I obtained results for right thickness using aparcstats2table.
Then, ANCOVA was used to compared right thickness between two group (age, sex, 
edu, ICV as covariates)using SPSS.
A group showed higher right thickness in the superior frontal gyrus compared 
with B group (p=0.001).

To see voxel-wise map in same data, I tried to process "mris_preporc", 
"mri_surf2surf", and "mri_glmfit"

But the group showed higher right thickness in the middle frontal gyrus 
compared with B group (p<0.005).

I don't understand why data is different.

In ROI analysis, the group showed higher right thickness in the superior 
frontal gyrus compared with B group, but  the group showed higher right 
thickness in the middle frontal gyrus compared with B group in the voxel-wise 
map (p<0.005).

Please recommend me about it.



mris_preproc --fsgd g1v4.fsgd --target fsaverage --hemi rh --meas thickness 
--out rh.g1v4.thickness.00.mgh



mri_surf2surf --hemi rh --s fsaverage --sval rh.g1v4.thickness.00.mgh --fwhm 10 
--cortex --tval rh.g1v4.thickness.10B.mgh



itmri_glmfit --y lh.g1v4.thickness.10B.mgh --fsgd g1v4.fsgd --C group.diff.mtx 
--surf fsaverage rh --cortex --glmdir rh.g1v4.glmdir





g1g4.fsgd (subjects 50)

Class Group1

Class Group2

Variables age sex edu ICV

Input eewe Group1 34 1 13 1753213

Input ffds Group2 32 1 13 1753213

Input erfg Group2 33 1 13 1753213

Input gdds Group1 31 2 12 1753213





group.diff.mtx

1 -1 0 0 0 0 0 0 0 0






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Re: [Freesurfer] (no subject)

[Greve, Douglas N.,Ph.D.]

ok, this is very strange to me. Can you send the result of these two commands
  pwd
and
  ls -l BN_Atlas_subcotex.mgz


On 12/13/2019 2:22 AM, Boris Rauchmann wrote:

External Email - Use Caution

yes to both. I always get the error ERROR: cannot find aseg...

On Fri, Dec 13, 2019 at 12:58 AM Greve, Douglas N.,Ph.D. 
mailto:dgr...@mgh.harvard.edu>> wrote:
Are you running this from 1122/mri and is BN_Atlas_subcotex.mgz in that
folder?

On 12/12/19 12:57 PM, Boris Rauchmann wrote:
>
> External Email - Use Caution
>
> My-Computer:~ boris$ mri_aparc2aseg --s 1122 --volmask --aseg
> BN_Atlas_subcotex.mgz --o aparc+BN_Atlas_subcotex.mgz
> SUBJECTS_DIR /Users/boris/Desktop/mydir
> subject 1122
> outvol aparc+BN_Atlas_subcotex.mgz
> useribbon 0
> baseoffset 0
> RipUnknown 0
>
> Reading lh white surface
>  /Users/boris/Desktop/mydir/1122/surf/lh.white
>
> Reading lh pial surface
>  /Users/boris/Desktop/mydir/1122/surf/lh.pial
>
> Loading lh annotations from
> /Users/boris/Desktop/mydir/1122/label/lh.aparc.annot
> reading colortable from annotation file...
> colortable with 36 entries read (originally
> /autofs/space/tanha_002/users/greve/fsdev.build/average/colortable_desikan_killiany.txt)
>
> Reading rh white surface
>  /Users/boris/Desktop/mydir/1122/surf/rh.white
>
> Reading rh pial surface
>  /Users/boris/Desktop/mydir/1122/surf/rh.pial
>
> Loading rh annotations from
> /Users/boris/Desktop/mydir/1122/label/rh.aparc.annot
> reading colortable from annotation file...
> colortable with 36 entries read (originally
> /autofs/space/tanha_002/users/greve/fsdev.build/average/colortable_desikan_killiany.txt)
> Have color table for lh white annotation
> Have color table for rh white annotation
> Loading ribbon segmentation from
> /Users/boris/Desktop/mydir/1122/mri/ribbon.mgz
>
> Building hash of lh white
>
> Building hash of lh pial
>
> Building hash of rh white
>
> Building hash of rh pial
> ERROR: cannot find aseg
>
>
> I get the same result using aseg.mgz
>
> Thanks,
> Boris
>> Am 12.12.2019 um 17:37 schrieb Bruce Fischl
>> mailto:fis...@nmr.mgh.harvard.edu> 
>> >>:
>>
>> Hi Boris
>>
>> can you send us the full command line and screen output of the
>> commands that are failing?
>>
>> cheers
>> Bruce
>> On Thu, 12 Dec 2019, Boris Rauchmann wrote:
>>
>>> External Email - Use Caution
>>> Thanks. unfortunately I get an error message when I use the --aseg
>>> flag for BN_Atlas_subcotex.mgz but
>>> even, if I'm using the original aseg.mgz I get: ERROR: cannot find aseg
>>> .../fs_all_subjects/xyz/mri/aseg.mgz
>>> The file BN_Atlas_subcotex.mgz was created using:
>>> mri_ca_label $SUBJECTS_DIR/xyz/mri/brain.mgz
>>> $SUBJECTS_DIR/xyz/mri/transforms/talairach.m3z
>>> $SUBJECTS_DIR/BN_Atlas_subcortex.gca
>>> $SUBJECTS_DIR/xyz/mri/BN_Atlas_subcotex.mgz
>>> Best,
>>> Boris
>>> On Thu, Dec 12, 2019 at 12:30 AM Greve, Douglas N.,Ph.D.
>>> mailto:dgr...@mgh.harvard.edu> 
>>> >> wrote:
>>>  What is in BN_Atlas_subcotex.mgz ? Is it like aseg.mgz but with
>>> your
>>>  subcortical ROIs added? If so, you can try merging it with the
>>> aparc, eg,
>>>
>>>  mri_aparc2aseg --s subject --volmask --aseg
>>> BN_Atlas_subcotex.mgz --o
>>>  aparc+BN_Atlas_subcotex.mgz
>>>
>>>  Then use aparc+BN_Atlas_subcotex.mgz as input to xcerebralseg,
>>> and then
>>>  run gtmseg as you have done below.
>>>
>>>  Let me know if that works
>>>  doug
>>>
>>>  On 12/2/19 1:18 PM, Boris Rauchmann wrote:
>>>  >
>>>  > External Email - Use Caution
>>>  >
>>>  > In this example tried it with only the subcortical
>>> segmentations from
>>>  > my atlas. Please find the logfile attached. It gives me back:
>>> "tissue
>>>  > type is not set" but I set it to 2 in the LUT.txt
>>>  >
>>>  > In principle look the following commands right to you?
>>>  >
>>>  > xcerebralseg --s 0120test --o apas+head+subcort_BN.mgz --m
>>>  > BN_Atlas_subcotex.mgz --atlas BN_Atlas_subcortex.gca
>>>  >
>>>  > gtmseg --s 0120test --head apas+head+subcort_BN.mgz --ctab
>>>  > subcort_BN_LUT.txt --o gtmseg+subcort_BN.mgz
>>>  >
>>>  > Ideally I would have a gtmseg with both, the subcortical and the
>>>  > cortical structures, but only the subcortical would also be
>>> fine as
>>>  > long as I can get  mri_gtmpvc running on it.
>>>  >
>>>  > Thanks,
>>>  > Boris
>>>  >
>>>  > On Mon, Dec 2, 2019 at 5:31 PM Greve, Douglas N.,Ph.D.
>>>  > mailto:dgr...@mgh.harvard.edu> 
>>> >
>>> >> wrote:
>>>  >
>>>  > Can you send the log file for each of the gtmseg runs?
>>>  >
>>>  > On 11/26/2019 1:09 PM, Boris Rauchmann wrote:
>>>  >>
>>>  >> External Emai

Re: [Freesurfer] Study specific template

[Greve, Douglas N.,Ph.D.]

OK. Does that mean there is not a problem? I'm just not sure if you need 
anything from us.

On 12/12/2019 7:37 PM, Joshi, Nandita wrote:

External Email - Use Caution

Hi Dr. Greve,

I apologize, maybe I should have been more specific. What I meant to say was, 
for example, in the attached area and volume distribution graphs over regions, 
we have most outliers in the superior frontal, and inferior parietal regions. 
However, on examining these regions for the subjects that are outliers, in 
comparison to the other subjects that are not, there doesn't seem to be an 
increased inclusion of voxels in these regions which could possibly explain why 
these might be outliers compared to the rest of the group.

- Nandita




On 12/12/19, 7:02 PM, "freesurfer-boun...@nmr.mgh.harvard.edu on behalf of 
Greve, Douglas 
N.,Ph.D."
 
 wrote:

When you say that you looked at the labels/segmentations and that
nothing looked horribly wrong, would that mean that the labels are
accurate and that they are not really outliers?

On 12/5/19 7:26 PM, Joshi, Nandita wrote:
>  External Email - Use Caution
>
> Hi Dr. Greve,
>
> Thanks so much for your response. The primary reason for my wanting to do 
this is due to a large number of outliers in terms of area and volume. Our 
subject population is cognitively impaired older adults > 60 yrs. The custom 
surface based template might help in investigating the area outliers, but it 
wouldn't help in investigating the volume outliers. Since the DK atlas is 
constructed from a population 18-86 years including 10 people with Alzheimer's 
Disease, I am not even sure if making a new volume atlas would help.
>
> Attached are graphs showing the distribution of the region wise area, 
volume, and thickness of all subjects in lh (filled circles) and rh (open 
circles). I looked into the aparc and aseg labels in specific regions for the 
subjects that are outliers, but nothing seems to have gone horribly wrong in 
terms of parcellation. Any pointers on how I can investigate and if a custom 
volume template might help?
>
> Thanks,
> Nandita
>
>
>
> On 12/5/19, 1:36 PM, "freesurfer-boun...@nmr.mgh.harvard.edu on behalf of 
Greve, Douglas 
N.,Ph.D."
 
 wrote:
>
>  We currently do not have a mac build of mri_aparc2aseg, and we 
recommend
>  downloading the freesurfer dev release to resolve this issue.
>
>
>
>  On 12/5/19 1:00 PM, Joshi, Nandita wrote:
>  >  External Email - Use Caution
>  >
>  > Thank you so much for that! The README file instructs to download 
make_average_subject and platform specific mri_aparc2aseg. However, the mac 
version of mri_aparc2aseg is missing in the patch. Could it be possible to 
download it from elsewhere?
>  >
>  > - Nandita
>  >
>  >
>  >
>  > On 12/5/19, 12:49 PM, "freesurfer-boun...@nmr.mgh.harvard.edu on 
behalf of Greve, Douglas 
N.,Ph.D."
 
 wrote:
>  >
>  >  USE CAUTION: External Message.
>  >
>  >  See the REAME file here
>  >  
https://urldefense.proofpoint.com/v2/url?u=ftp-3A__surfer.nmr.mgh.harvard.edu_pub_dist_freesurfer_6.0.0-2Dpatch_&d=DwIGaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=Bk3PG21nu20V-TX_S982zJE-KkrgCuPol41Dhbe_0mI&m=HRcVNlmwdK9YHH6teQ4oW9oc6SDFzz79TS63mOlbWIY&s=6p4504imNdnEktx-WXKqAlxMvMCQ1EYnJW_deB2CAWI&e=
>  >
>  >
>  >  On 12/5/19 11:47 AM, Joshi, Nandita wrote:
>  >  >
>  >  > External Email - Use Caution
>  >  >
>  >  > Hello,
>  >  >
>  >  > I am trying to make a study specific template based on the
>  >  > instructions at:
>  >  > 
https://urldefense.proofpoint.com/v2/url?u=https-3A__surfer.nmr.mgh.harvard.edu_fswiki_SurfaceRegAndTemplates&d=DwIGaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=Bk3PG21nu20V-TX_S982zJE-KkrgCuPol41Dhbe_0mI&m=HRcVNlmwdK9YHH6teQ4oW9oc6SDFzz79TS63mOlbWIY&s=LmIaVqBLyDekwkZsc46m3nOdn25B7E96m_UuRzDu3Ew&e=
>  >  >
>  >  > However, on running the first step:
>  >  >
>  >  > make_average_subject --out newtemplate --subjects subj1 
subj2 subj3 ...
>  >  >
>  >  > I get an error while running the recon-all is trying to run 
the aparc
>  >  > to aseg step:
>  >  >
>  >  > #@# AParc-to-ASeg aparc

Re: [Freesurfer] Cortical segmentation error

[Bruce Fischl]

Hi Monica

hmmm, hard to say without seeing the dataset. You could try using expert 
options to change some of the intensity thresholds if this is something 
specific to your acquiisition. If you upload the gzipped subject dir we 
will take a look


cheers
Bruce


On Fri, 13 Dec 2019, Monica Bondy wrote:



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Hello FreeSurfer experts,

I am trying to figure out how to fix the segmentation so that the cortex is 
properly included. 
I have had this problem with several subjects, especially in the temporal lobe 
regions. 

I have tried tkmregister but it unfortunately didn't change anything. I am in 
the middle of using
control points to try to fix this problem but it hasn't been ideal as it is 
requiring a lot of control
points. Do you have any ideas on how to fix this?

I have attached an image of what the problem looks like below. Please let me 
know if you need anything
else.

Thank you!

1. FreeSurfer version: freesurfer-i386-apple-darwin11.4.2-stable5-20130514
2. Platform: macOS Mojave (version 10.14.6)
3. uname -a: Darwin D20190801 18.7.0 Darwin Kernel Version 18.7.0: Sat Oct 12 
00:02:19 PDT 2019;
root:xnu-4903.278.12~1/RELEASE_X86_64 x86_64

Screen Shot 2019-12-13 at 9.56.46 AM.png


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Re: [Freesurfer] Create translucent brain volume and save as matlab figure file

[Bruce Fischl]

Hi Sparsh

probably easiest to do with a surface, but I defer to Ruopeng

cheers
Bruce
On Fri, 13 Dec 
2019, Sparsh Jain wrote:




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Hi!
Can I create a brain volume with about 40% opacity and save it as a matlab 
figure? The volume may not be
very accurate.

Why I need this: I want to 3D plot the RAS coordinates of electrodes inside 
this brain volume in Matlab
and assign different colors corresponding to power values.

I have attached a sample brain volume that might work for me. Please ignore the 
electrode color (it was
obtained using a different pipeline).

Thanks!

Sparsh

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Re: [Freesurfer] Segmentation fault mri_em_register

[Bruce Fischl]

Hi Ricardo

hmmm, the fact that it says "MRImaskDifferentGeometry" suggests that your 
skull stripping didn't end up "conforming", which is probably the error. 
Try something like:


cp brainmask.mgz brainmask.bet.mgz
mri_convert -rl orig.mgz  brainmask.bet.mgz brainmask.mgz

then rerun and see if that fixes your problem

cheers
Bruce


On Fri, 13 Dec 2019, Loucao, Ricardo wrote:



External Email - Use Caution

Dear Freesurfer experts,
I’m trying to run recon-all on "T1-like” data (it is actually a quantitative 
MRI map with a contrast
very similar to T1). 
I managed to put it through recon-all’s autorecon1 stage without any major 
tricks or modifications
(watershed is failing but I managed to work around it using FSL’s BET combined 
with mri_convert to
create the brainmask.mgz file).

Now I’m running into a segmentation fault error in mri_em_register.  
Here’s the relevant portion of the log file:

#@# EM Registration Fri Dec 13 10:55:07 GMT 2019
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/mri

 mri_em_register 
-rusage/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_em_registe
r.dat -uns 3 -mask brainmask.mgz nu.mgz
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_2016-05-10.vc700.gca
transforms/talairach.lta 

setting unknown_nbr_spacing = 3
using MR volume brainmask.mgz to mask input volume...

== Number of threads available to mri_em_register for OpenMP = 1 == 
reading 1 input volumes...
logging results to talairach.log
reading'/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_2016-05-10.vc700.gca'...
average std = 7.3   using min determinant for regularization = 5.3
0 singular and 841 ill-conditioned covariance matrices regularized
reading 'nu.mgz'...
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
freeing gibbs priors...done.
accounting for voxel sizes in initial transform
bounding unknown intensity as < 6.3 or > 503.7 
total sample mean = 78.8 (1011 zeros)

spacing=8, using 2830 sample points, tol=1.00e-05...

register_mri: find_optimal_transform
find_optimal_transform: nsamples 2830, passno 0, spacing 8
Segmentation fault (core dumped)
Linux btupc09 4.4.0-154-generic #181-Ubuntu SMP Tue Jun 25 05:29:03 UTC 2019 
x86_64 x86_64 x86_64
GNU/Linux

recon-all -s PM1 exited with ERRORS at Fri Dec 13 10:55:35 GMT 2019


I’m also sending you the log from the skull striping step, in case the two 
errors might be connected:
 
#@# Skull Stripping Fri Dec 13 10:23:26 GMT 2019
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/mri

 mri_em_register 
-rusage/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_em_registe
r.skull.dat -skull 
nu.mgz/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc70
0.gca transforms/talairach_with_skull.lta 

aligning to atlas containing skull, setting unknown_nbr_spacing = 5

== Number of threads available to mri_em_register for OpenMP = 1 == 
reading 1 input volumes...
logging results to talairach_with_skull.log
reading'/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc7
00.gca'...
average std = 22.9   using min determinant for regularization = 52.6
0 singular and 9002 ill-conditioned covariance matrices regularized
reading 'nu.mgz'...
freeing gibbs priors...done.
accounting for voxel sizes in initial transform
bounding unknown intensity as < 8.7 or > 569.1 
total sample mean = 77.6 (1399 zeros)

spacing=8, using 3243 sample points, tol=1.00e-05...

register_mri: find_optimal_transform
find_optimal_transform: nsamples 3243, passno 0, spacing 8
resetting wm mean[0]: 100 --> 108
resetting gm mean[0]: 61 --> 61
input volume #1 is the most T1-like
using real data threshold=25.1
skull bounding box = (67, 34, 13) --> (199, 162, 201)
using (111, 77, 107) as brain centroid...
mean wm in atlas = 108, using box (95,61,84) --> (127, 92,130) to find MRI wm
before smoothing, mri peak at 106
robust fit to distribution - 107 +- 5.6
after smoothing, mri peak at 106, scaling input intensities by 1.019
scaling channel 0 by 1.01887
initial log_p = -4.775

First Search limited to translation only.

max log p =    -4.469154 @ (-9.091, 27.273, 9.091)
max log p =    -4.310065 @ (4.545, -4.545, -4.545)
max log p =    -4.294819 @ (-2.273, 2.273, -2.273)
max log p =    -4.269301 @ (1.136, -1.136, -1.136)
max log p = 

[Freesurfer] Segmentation fault mri_em_register

[Loucao, Ricardo]

External Email - Use Caution

Dear Freesurfer experts,

I’m trying to run recon-all on "T1-like” data (it is actually a quantitative 
MRI map with a contrast very similar to T1).
I managed to put it through recon-all’s autorecon1 stage without any major 
tricks or modifications (watershed is failing but I managed to work around it 
using FSL’s BET combined with mri_convert to create the brainmask.mgz file).

Now I’m running into a segmentation fault error in mri_em_register.
Here’s the relevant portion of the log file:

#@# EM Registration Fri Dec 13 10:55:07 GMT 2019
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/mri

 mri_em_register -rusage 
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_em_register.dat
 -uns 3 -mask brainmask.mgz nu.mgz 
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_2016-05-10.vc700.gca
 transforms/talairach.lta

setting unknown_nbr_spacing = 3
using MR volume brainmask.mgz to mask input volume...

== Number of threads available to mri_em_register for OpenMP = 1 ==
reading 1 input volumes...
logging results to talairach.log
reading 
'/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_2016-05-10.vc700.gca'...
average std = 7.3   using min determinant for regularization = 5.3
0 singular and 841 ill-conditioned covariance matrices regularized
reading 'nu.mgz'...
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
INFO: MRImask() using MRImaskDifferentGeometry()
freeing gibbs priors...done.
accounting for voxel sizes in initial transform
bounding unknown intensity as < 6.3 or > 503.7
total sample mean = 78.8 (1011 zeros)

spacing=8, using 2830 sample points, tol=1.00e-05...

register_mri: find_optimal_transform
find_optimal_transform: nsamples 2830, passno 0, spacing 8
Segmentation fault (core dumped)
Linux btupc09 4.4.0-154-generic #181-Ubuntu SMP Tue Jun 25 05:29:03 UTC 2019 
x86_64 x86_64 x86_64 GNU/Linux

recon-all -s PM1 exited with ERRORS at Fri Dec 13 10:55:35 GMT 2019


I’m also sending you the log from the skull striping step, in case the two 
errors might be connected:

#@# Skull Stripping Fri Dec 13 10:23:26 GMT 2019
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/mri

 mri_em_register -rusage 
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_em_register.skull.dat
 -skull nu.mgz 
/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc700.gca
 transforms/talairach_with_skull.lta

aligning to atlas containing skull, setting unknown_nbr_spacing = 5

== Number of threads available to mri_em_register for OpenMP = 1 ==
reading 1 input volumes...
logging results to talairach_with_skull.log
reading 
'/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc700.gca'...
average std = 22.9   using min determinant for regularization = 52.6
0 singular and 9002 ill-conditioned covariance matrices regularized
reading 'nu.mgz'...
freeing gibbs priors...done.
accounting for voxel sizes in initial transform
bounding unknown intensity as < 8.7 or > 569.1
total sample mean = 77.6 (1399 zeros)

spacing=8, using 3243 sample points, tol=1.00e-05...

register_mri: find_optimal_transform
find_optimal_transform: nsamples 3243, passno 0, spacing 8
resetting wm mean[0]: 100 --> 108
resetting gm mean[0]: 61 --> 61
input volume #1 is the most T1-like
using real data threshold=25.1
skull bounding box = (67, 34, 13) --> (199, 162, 201)
using (111, 77, 107) as brain centroid...
mean wm in atlas = 108, using box (95,61,84) --> (127, 92,130) to find MRI wm
before smoothing, mri peak at 106
robust fit to distribution - 107 +- 5.6
after smoothing, mri peak at 106, scaling input intensities by 1.019
scaling channel 0 by 1.01887
initial log_p = -4.775

First Search limited to translation only.

max log p =-4.469154 @ (-9.091, 27.273, 9.091)
max log p =-4.310065 @ (4.545, -4.545, -4.545)
max log p =-4.294819 @ (-2.273, 2.273, -2.273)
max log p =-4.269301 @ (1.136, -1.136, -1.136)
max log p =-4.255560 @ (-0.568, 0.568, 0.568)
max log p =-4.255560 @ (0.000, 0.000, 0.000)
Found translation: (-6.3, 24.4, 1.7): log p = -4.256

Nine parameter search.  iteration 0 nscales = 0 ...

Result so far: scale 1.000: max_log_p=-4.200, old_max_log_p =-4.256 
(thresh=-4.3)
 1.07500   0.0   0.0  -