Hi Pilar,
You can update freesurfer to 7.1 by installing the rpm (on linux), running the
mac installer, or by extracting the tar archive. The rpm and mac installers
will download freesurfer distributions to appropriate version subdirectories,
so they will not overwrite an earlier version. If you extract the archive, it's
up to you to decide where to install freesurfer.
Best,
Andrew
On 9/7/20, 4:26 AM, "freesurfer-boun...@nmr.mgh.harvard.edu on behalf of
Ferraro, Pilar" <freesurfer-boun...@nmr.mgh.harvard.edu on behalf of
ferra...@pennmedicine.upenn.edu> wrote:
External Email - Use Caution
Hi Freesurfer experts,
I’m trying to update my current Freesurfer version (from 6.0 to 7.1).
I’d like to know whether the installation procedure for 7.1 would overwrite
the current one (6.0).
Many thanks,
Pilar
> Il giorno 22 ago 2020, alle ore 7:48 AM,
freesurfer-requ...@nmr.mgh.harvard.edu ha scritto:
>
> Send Freesurfer mailing list submissions to
> freesurfer@nmr.mgh.harvard.edu
>
> To subscribe or unsubscribe via the World Wide Web, visit
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> or, via email, send a message with subject or body 'help' to
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>
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>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of Freesurfer digest..."
>
>
> Today's Topics:
>
> 1. Global surface area and global thickness values (Palin, Tara)
> 2. How to run recon-all for different subjects and each subject
> with different sessions ? (Camargo, Aldo)
> 3. Postdoctoral scientist positions at UCSF (Tosun, Duygu)
> 4. Re: extracting beta coefficients and p-values (Douglas N. Greve)
> 5. Re: How to run recon-all for different subjects and each
> subject with different sessions ? (Douglas N. Greve)
> 6. Re: Global surface area and global thickness values
> (Douglas N. Greve)
> 7. Re: ROI-wide cortical thickness for each network ! (Martin Juneja)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 21 Aug 2020 18:55:16 +0000
> From: "Palin, Tara" <pa...@kennedykrieger.org>
> Subject: [Freesurfer] Global surface area and global thickness values
> To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
> Cc: "Plotkin, Micah" <plotkin...@kennedykrieger.org>, "Crocetti,
> Deana" <croce...@kennedykrieger.org>
> Message-ID: <162876d303f44e7d987ab303c0a11...@kennedykrieger.org>
> Content-Type: text/plain; charset="iso-8859-1"
>
> External Email - Use Caution
>
> Hello freesurfer experts,
>
>
> I am looking to extract the global surface and global thickness
measurements and am curious if my understanding of the output on the
aparc.stats (for lh and rh) are correct. When looking at these files, at the
very top you will find the Pial Surface Total Area and a Mean Thickness value
which I am assuming are the global measures. Below you have all of the ROIs
listed (this was not changed by us or generated by us this is what comes out of
freesurfer to my understanding) that includes everything EXCEPT the corpus
callosum and the unknown. However, I summed all the surface areas and took and
average of all the thickness measurements and they are very close to the values
printed at the top of the sheet. Another observation I had made was that the
total surface area measurement has a decimal and the surface area for the ROIs
appears to be rounded and if they were not rounded the values would be the
same. For the thickness values, the value I calculated and the one on the sheet
are within the average of the standard deviation each other.
>
>
> If my understanding is incorrect please let me know and provide insight
into how we may calculate these global measurements correctly.
>
>
> Thank you in advance for your time,
>
> Tara Palin
>
>
> ----------------------------------------------------------------------
> Disclaimer:
>
>
> The materials in this e-mail are private and may contain Protected
Information. Please note that e-mail communication is not encrypted by default.
You have the right to request further emails be encrypted by notifying the
sender. Your continued use of e-mail constitutes your acknowledgment of these
confidentiality and security limitations. If you are not the intended
recipient, be advised that any unauthorized use, disclosure, copying,
distribution, or the taking of any action in reliance on the contents of this
information is strictly prohibited. If you have received this e-mail in error,
please immediately notify the sender via telephone or return e-mail.
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> ------------------------------
>
> Message: 2
> Date: Fri, 21 Aug 2020 19:35:00 +0000
> From: "Camargo, Aldo" <acama...@som.umaryland.edu>
> Subject: [Freesurfer] How to run recon-all for different subjects and
> each subject with different sessions ?
> To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
> Message-ID:
>
<mn2pr03mb494412c0bcf4beaf0c94da679f...@mn2pr03mb4944.namprd03.prod.outlook.com>
>
> Content-Type: text/plain; charset="iso-8859-1"
>
> External Email - Use Caution
>
> Hi Freesurfer experts,
>
> I have a question: How to run recon-all for different subjects and each
subject with different sessions ?
>
> Thanks a lot in advance and have a nice day,
>
> Aldo
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> ------------------------------
>
> Message: 3
> Date: Fri, 21 Aug 2020 19:37:55 +0000
> From: "Tosun, Duygu" <duygu.to...@ucsf.edu>
> Subject: [Freesurfer] Postdoctoral scientist positions at UCSF
> To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
> Message-ID:
>
<dm6pr05mb4713ada49571c83cb10af22384...@dm6pr05mb4713.namprd05.prod.outlook.com>
>
> Content-Type: text/plain; charset="iso-8859-1"
>
> External Email - Use Caution
>
> We are seeking highly talented and self-motivated postdoctoral scientists
with experience in quantitative medical imaging and biomarker research. The
research will be conducted at the University of California San Francisco (UCSF)
and San Francisco Veterans Affair Medical Center. The successful candidate will
interact with multidisciplinary project teams focused on identifying and
characterizing neuronal substrates of cognitive and clinical decline and
optimizing and streamlining biomarker discovery in neurodegenerative diseases
and psychiatric disorders.
>
> Qualified candidate will have Ph.D. with practical experience in medical
imaging, fluid biomarkers, and neurobiology along with a solid understanding of
biostatistics. Training or experience in the areas of neuroinformatics,
computational neuroscience, and machine learning is preferred. Candidate is
expected to learn new techniques as necessary.
>
> The successful candidate will have demonstrated a track record of success
as evidenced by relevant publications in leading peer-reviewed journals.
Candidate must demonstrate outstanding communication skills, the ability to
operate within multidisciplinary environments, creativity, and a high degree of
intellectual independence. Attention to detail and enthusiasm for science are a
must.
>
> This postdoctoral research fellowship program is designed to prepare the
fellow for independent academic career in reproductive research. Focus will be
scientific productivity demonstrated by innovative research, publication in
high impact journals and successful extramural funding. Fellow is expected to
transition in few years into an independent faculty role.
>
> Applications will be accepted until the positions are filled. Funding is
available for the selected applicants to start immediately. When electronically
applying for this position (duygu.to...@ucsf.edu), please include (in one PDF
document) a cover letter, curriculum vitae, publication list and a brief
description of research interests.
>
>
>
> Duygu Tosun-Turgut, PhD
> Director, Medical Imaging Informatics + Artificial Intelligence
> Associate Professor, UCSF Department of Radiology and Biomedical Imaging
> https://profiles.ucsf.edu/duygu.tosun-turgut
> <https://profiles.ucsf.edu/duygu.tosun-turgut>
> University of California, San Francisco
> Center for Imaging of Neurodegenerative Diseases (CIND)
> 4150 Clement Street - 114M
> San Francisco, CA 94121
> Phone: (415) 221-4810 ext. 23650
> duygu.to...@ucsf.edu
>
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> ------------------------------
>
> Message: 4
> Date: Fri, 21 Aug 2020 16:02:05 -0400
> From: "Douglas N. Greve" <dgr...@mgh.harvard.edu>
> Subject: Re: [Freesurfer] extracting beta coefficients and p-values
> To: <freesurfer@nmr.mgh.harvard.edu>
> Message-ID: <e0eb1604-c392-6f1e-030c-8bf84cf04...@mgh.harvard.edu>
> Content-Type: text/plain; charset="windows-1252"
>
> You have 4 classes with 3 variables, so there are 4*(1+3)=16? items in
> your beta vector. In general, the betas are arranged
> Class1offset Class2offset ... Class1Var1Slope Class2Var1Slope ...
> Class1Var2Slope Class2Var2Slope ...
>
> If you want? the mean of the APOE Status No, then you would average
> beta1 and beta3 (ie, contrast [0.5 0 0.5 0 ... 0]). If you want the
> difference between the Yes and the No, then you would subtract the avg
> of beta1 and beta3 from beta2 and beta 4 ([0.5 -0.5 0.5 -0.5 0 0 ...]).
>
> If you want just the effect of age, then you would average betas 9-12.
>
> If you are trying to interpret the offsets, then I would make sure that
> there is not an interaction between group and covariate. If that is the
> case, then you can use DOSS instead of DODS in which case the covariate
> betas just reduce down to a single slope, which is easier to interpret.
>
>
> On 8/20/2020 2:11 PM, Rizvi, Batool wrote:
>>
>> ????????External Email - Use Caution
>>
>> Dear FreeSurfer experts,
>>
>> After running the longitudinal pipeline with QDEC, I needed
>> beta-values of the associations between my main continuous variable
>> and the significant cortical cluster, as well as of all the nuisance
>> factors/categorical variables. Since the summary file doesn?t list
>> beta values, I attempted running this command to find beta coefficients:
>>
>> mri_segstats --i beta.mgh --annot fsaverage lh
>> lh-Avg-long.thickness-spc-Parietal_WMH-Cor/mc-z.abs.th20.sig.ocn.annot
>> --excludeid 0 --avgwf avgwf.dat
>>
>> From this, I got this output file listing beta values for 1 cluster
>> (entorhinal cortex) that was related to my main continuous IV
>> (parietal WMH). I?m struggling to understand how to still compute 1
>> beta-coefficient per variable from this list. I understand that this
>> list, based on my fsgd file, can be split into 4 sets of numbers, but
>> I?m not sure what do next.
>>
>> avgwf.dat:
>>
>> ??? 0.93879
>>
>> ???-8.80748
>>
>> ????0.38452
>>
>> ????3.13252
>>
>> ???-0.09493
>>
>> ???-0.13107
>>
>> ???-0.09960
>>
>> ???-0.31087
>>
>> ???-0.02291
>>
>> ????0.03127
>>
>> ???-0.02417
>>
>> ???-0.07858
>>
>> ????0.13087
>>
>> ????1.80749
>>
>> ????0.32535
>>
>> ????0.64947
>>
>> Below is how my fsgd file was structured:
>>
>> GroupDescriptorFile 1
>>
>> Title ParietalWMH_age_gender_APOe_EC_left
>>
>> MeasurementName long.thickness-spc
>>
>> Class APOE_statusNO-genderF
>>
>> Class APOE_statusYES-genderF
>>
>> Class APOE_statusNO-genderM
>>
>> Class APOE_statusYES-genderM
>>
>> Variables Parietal_WMH age_years Avg_ECthickness
>>
>> Input W38284_base APOE_statusNO-genderF 0.732010 85.830000 3.125000
>>
>> .. (only listing the 1^st Input line here)
>>
>> My contrast file (C.dat) looks like below:
>>
>> 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0
>>
>> So basically, I would like to get beta coefficients for the below
>> variables:
>>
>> * Parietal WMH
>> * Age
>> * Gender
>> * APOE status
>> * Average EC thickness (baseline values)
>>
>> Thank you so much in advance for your help!
>>
>> Batool
>>
>>
>> _______________________________________________
>> Freesurfer mailing list
>> Freesurfer@nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
>
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> ------------------------------
>
> Message: 5
> Date: Fri, 21 Aug 2020 18:38:15 -0400
> From: "Douglas N. Greve" <dgr...@mgh.harvard.edu>
> Subject: Re: [Freesurfer] How to run recon-all for different subjects
> and each subject with different sessions ?
> To: <freesurfer@nmr.mgh.harvard.edu>
> Message-ID: <1fa92efe-9788-ba36-7b79-ceb394ae6...@mgh.harvard.edu>
> Content-Type: text/plain; charset="windows-1252"
>
> not sure what you mean by different session. Do you mean multiple time
> points (a longitudinal study)? Or do you mean that you acquired several
> images during a single scanning session?
>
> On 8/21/2020 3:35 PM, Camargo, Aldo wrote:
>>
>> ????????External Email - Use Caution
>>
>> Hi Freesurfer experts,
>>
>> I have a question: ?How to run recon-all for different subjects and
>> each subject with different sessions ?
>>
>> Thanks a lot in advance and have a nice day,
>>
>> Aldo
>>
>> _______________________________________________
>> Freesurfer mailing list
>> Freesurfer@nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
>
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> ------------------------------
>
> Message: 6
> Date: Fri, 21 Aug 2020 18:42:38 -0400
> From: "Douglas N. Greve" <dgr...@mgh.harvard.edu>
> Subject: Re: [Freesurfer] Global surface area and global thickness
> values
> To: <freesurfer@nmr.mgh.harvard.edu>
> Message-ID: <3f43df01-2efd-6c93-1f59-b72891f55...@mgh.harvard.edu>
> Content-Type: text/plain; charset="windows-1252"
>
> I'm not sure what your question is. You can't just average the ROI
> values to get the global value unless you weight by the ROI size.
>
> On 8/21/2020 2:55 PM, Palin, Tara wrote:
>>
>> ????????External Email - Use Caution
>>
>> Hello freesurfer experts,
>>
>>
>> I am looking to extract the global surface and global thickness
>> measurements and am curious if my understanding of the output on the
>> aparc.stats (for lh and rh) are correct. When looking at these files,
>> at the very top you will find the Pial?Surface Total Area and a Mean
>> Thickness value which I am assuming are the global measures. Below you
>> have all of the ROIs listed (this was not changed by us or generated
>> by us this is what comes out of freesurfer to my understanding) that
>> includes everything EXCEPT the corpus callosum and the unknown.
>> However, I summed all the surface areas and took and average of all
>> the thickness measurements and they are very close to the values
>> printed at the top of the sheet. Another observation I had made was
>> that the total surface area measurement has a decimal and the surface
>> area for the ROIs appears to be rounded and if they were not rounded
>> the values would be the same. For the thickness values, the value I
>> calculated and the one on the sheet are within the average of the
>> standard deviation each other.
>>
>>
>> If my understanding is incorrect please let me know and provide
>> insight into how we may calculate these global measurements correctly.
>>
>>
>> Thank you in advance for your time,
>>
>> Tara Palin
>>
>>
>>
>> ------------------------------------------------------------------------
>> Disclaimer:
>>
>>
>> The materials in this e-mail are private and may contain Protected
>> Information. Please note that e-mail communication is not encrypted by
>> default. You have the right to request further emails be encrypted by
>> notifying the sender. Your continued use of e-mail constitutes your
>> acknowledgment of these confidentiality and security limitations. If
>> you are not the intended recipient, be advised that any unauthorized
>> use, disclosure, copying, distribution, or the taking of any action in
>> reliance on the contents of this information is strictly prohibited.
>> If you have received this e-mail in error, please immediately notify
>> the sender via telephone or return e-mail.
>>
>> _______________________________________________
>> Freesurfer mailing list
>> Freesurfer@nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
>
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> ------------------------------
>
> Message: 7
> Date: Sat, 22 Aug 2020 00:48:33 -0500
> From: Martin Juneja <mj70...@gmail.com>
> Subject: Re: [Freesurfer] ROI-wide cortical thickness for each network
> !
> To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
> Message-ID:
> <caaf3gesxumaic-m8uuog0smkavbspqpvrwcvegfmtndjva5...@mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> External Email - Use Caution
>
> Thank you so much, Doug! Yes, it's working fine now.
>
> On Thu, Aug 20, 2020 at 8:49 AM Douglas N. Greve <dgr...@mgh.harvard.edu>
> wrote:
>
>> Try adding --accumulate to the command line. By default, mri_segstats
will
>> compute the mean of the input, but you want the sum. Also, ignore the
>> "Volume" column and look at the "Mean" column
>>
>> On 8/19/2020 2:27 PM, Martin Juneja wrote:
>>
>> External Email - Use Caution
>> Hi Doug,
>>
>> I used following mri_segstats:
>> mri_segstats --excludeid 0 --seg 2080/label/OCN5_LH.mgz --i
>> 2080/surf/lh.volume --sum 2080/stats/sum.LH_N5_CV.dat
>>
>> And here is the output it gives:
>>
>> ...............
>> # NRows 10
>> # NTableCols 10
>> # ColHeaders Index SegId NVoxels Volume_mm3 StructName Mean StdDev Min
>> Max Range
>> 1 1 6543 6543.0 Seg0001 2.7945 2.4609 0.0000
>> 26.6245 26.6245
>> 2 2 5897 5897.0 Seg0002 2.1155 1.5223 0.0000
>> 13.3562 13.3562
>> ...............
>>
>>
>> On Wed, Aug 19, 2020 at 10:13 AM Douglas N. Greve
<dgr...@mgh.harvard.edu>
>> wrote:
>>
>>> But this is happening at the individual level, right? Rather than look
at
>>> group data, let's debug an individual subject as it will be easier. Can
you
>>> replicate using a single subject's mri_segstats output? Can you send the
>>> mri_segstats command you used?
>>>
>>> On 8/17/2020 3:05 PM, Martin Juneja wrote:
>>>
>>> External Email - Use Caution
>>> Hi Doug,
>>>
>>> I used asegstats command as following:
>>> asegstats2table --meas volume --subjectsfile Subjects.txt --statsfile
>>> sum.LH_Volume.dat --tablefile CV_LH_N5_Regions.txt --common-segs
>>>
>>> On Mon, Aug 17, 2020 at 10:13 AM Douglas N. Greve
<dgr...@mgh.harvard.edu>
>>> wrote:
>>>
>>>> what is your mri_segstats command line?
>>>>
>>>> On 8/14/2020 2:57 AM, Martin Juneja wrote:
>>>>
>>>> External Email - Use Caution
>>>> Hi Doug,
>>>>
>>>> I calculated region-specific cortical volume values (e.g. for both the
>>>> clusters - the orbitofrontal cortex and the temporal pole of the
>>>> limbic network) using all the above steps. Just to verify everything,
when
>>>> I summed up the volume values of these two clusters, it doesn't come
out to
>>>> be equal to volume of the entire network (i.e. limbic network in this
case
>>>> which I used to get cluster-wise values). Volume of the entire network
from
>>>> Yeo atlas is much greater than the total sum of cluster-wise volume
values
>>>> (e.g., 5776+5271 below).
>>>>
>>>> Also, my clusterwise output looks like below, and I am not sure why I
>>>> always get number of voxels equal to the volume of the cluster. It
seems to
>>>> me that the Volume_mm3 values below are not the entire cluster values.
It
>>>> seems entire cluster volume value should be the product of Mean and
NVoxels
>>>> below i.e., for Seg0001: 5776*2.4890 and Seg0002: 5271*1.9801. Here
>>>> (5776*2.4890)+(5271*1.9801) comes out to be closer (although still not
>>>> exactly the same!) to the entire network volume.
>>>> .....
>>>> # ColHeaders Index SegId NVoxels Volume_mm3 StructName Mean StdDev Min
>>>> Max Range
>>>> 1 1 5776 5776.0 Seg0001 2.4890 2.1513 0.0000
>>>> 30.6004 30.6004
>>>> 2 2 5271 5271.0 Seg0002 1.9801 1.4990 0.0255
>>>> 15.5331 15.5077
>>>>
>>>> Any help would be really appreciated.
>>>>
>>>> On Tue, Jul 28, 2020 at 2:35 AM Martin Juneja <mj70...@gmail.com>
wrote:
>>>>
>>>>> Thank you so much, Dough. Using --dilate 1 gives me the correct number
>>>>> of clusters.
>>>>>
>>>>> Thanks again for all your help.
>>>>>
>>>>> On Mon, Jul 27, 2020 at 10:48 PM Douglas N. Greve <
>>>>> dgr...@mgh.harvard.edu> wrote:
>>>>>
>>>>>> Load ocn.mgz as an overlay on the surface (set the lower threshold to
>>>>>> 0.5 to see all the clusters). When you click on a cluster, the value
of the
>>>>>> overlay will be the cluster number. You can also use this to figure
out why
>>>>>> you have 25 clusters instead of 5. Don't worry about the Volume_mm3,
that's
>>>>>> just what mri_segstats prints out (this is a fairly non-standard use
of it)
>>>>>>
>>>>>> On 7/27/2020 7:30 PM, Martin Juneja wrote:
>>>>>>
>>>>>> External Email - Use Caution
>>>>>> Dear Doug,
>>>>>>
>>>>>> Thanks a lot for providing the instructions. It seems it's working
>>>>>> fine now. I have following three follow-up concerns:
>>>>>>
>>>>>> *1. The text file for the DMN (which has 5 regions) I get at the end
>>>>>> looks like the following. I am not sure why I get values of 25
clusters as
>>>>>> follows. Although, it seems the first 5 clusters belong to the 5
regions of
>>>>>> the DMN. The rest of the clusters from 6th to 25th show 1 voxel
only.*
>>>>>> *Could you please confirm if my interpretation is correct that the
>>>>>> first five belong to 5 ROIs of the DMN? And, what do the rest
represent?
>>>>>> Can I just ignore those?*
>>>>>> (The same is the case for other networks e.g. for the limbic network
>>>>>> (which has 2 regions), I get text file of 12 regions with regions
3rd to
>>>>>> 12th only 1 voxel, and there it seems the first two ROIs belong to
the
>>>>>> limbic network.)
>>>>>> .....
>>>>>> # ColHeaders Index SegId NVoxels Volume_mm3 StructName Mean StdDev
>>>>>> Min Max Range
>>>>>> 1 1 15712 15712.0 Seg0001 2.7525 0.6145 0.9909
>>>>>> 4.9323 3.9414
>>>>>> 2 2 7026 7026.0 Seg0002 3.1722 0.6279 1.6064
>>>>>> 5.0000 3.3936
>>>>>> 3 3 6268 6268.0 Seg0003 2.7608 0.5594 0.5501
>>>>>> 4.8145 4.2644
>>>>>> 4 4 5424 5424.0 Seg0004 2.6581 0.5140 0.7961
>>>>>> 4.3648 3.5687
>>>>>> 5 5 533 533.0 Seg0005 2.8529 0.7931 0.0000
>>>>>> 4.9511 4.9511
>>>>>> 6 6 1 1.0 Seg0006 2.9284 0.0000 2.9284
>>>>>> 2.9284 0.0000
>>>>>> 7 7 1 1.0 Seg0007 2.7651 0.0000 2.7651
>>>>>> 2.7651 0.0000
>>>>>> 8 8 1 1.0 Seg0008 4.5695 0.0000 4.5695
>>>>>> 4.5695 0.0000
>>>>>> 9 9 1 1.0 Seg0009 3.3998 0.0000 3.3998
>>>>>> 3.3998 0.0000
>>>>>> 10 10 1 1.0 Seg0010 2.4130 0.0000 2.4130
>>>>>> 2.4130 0.0000
>>>>>> 11 11 1 1.0 Seg0011 2.3768 0.0000 2.3768
>>>>>> 2.3768 0.0000
>>>>>> 12 12 1 1.0 Seg0012 2.0668 0.0000 2.0668
>>>>>> 2.0668 0.0000
>>>>>> 13 13 1 1.0 Seg0013 2.1846 0.0000 2.1846
>>>>>> 2.1846 0.0000
>>>>>> 14 14 1 1.0 Seg0014 4.4352 0.0000 4.4352
>>>>>> 4.4352 0.0000
>>>>>> 15 15 1 1.0 Seg0015 2.1129 0.0000 2.1129
>>>>>> 2.1129 0.0000
>>>>>> 16 16 1 1.0 Seg0016 1.8682 0.0000 1.8682
>>>>>> 1.8682 0.0000
>>>>>> 17 17 1 1.0 Seg0017 2.4406 0.0000 2.4406
>>>>>> 2.4406 0.0000
>>>>>> 18 18 1 1.0 Seg0018 2.3616 0.0000 2.3616
>>>>>> 2.3616 0.0000
>>>>>> 19 19 1 1.0 Seg0019 2.9061 0.0000 2.9061
>>>>>> 2.9061 0.0000
>>>>>> 20 20 1 1.0 Seg0020 4.0454 0.0000 4.0454
>>>>>> 4.0454 0.0000
>>>>>> 21 21 1 1.0 Seg0021 3.3300 0.0000 3.3300
>>>>>> 3.3300 0.0000
>>>>>> 22 22 1 1.0 Seg0022 2.9533 0.0000 2.9533
>>>>>> 2.9533 0.0000
>>>>>> 23 23 1 1.0 Seg0023 2.4742 0.0000 2.4742
>>>>>> 2.4742 0.0000
>>>>>> 24 24 1 1.0 Seg0024 3.4334 0.0000 3.4334
>>>>>> 3.4334 0.0000
>>>>>> 25 25 1 1.0 Seg0025 4.0615 0.0000 4.0615
>>>>>> 4.0615 0.0000
>>>>>>
>>>>>> *2. Although I extracted thickness measures as we discussed in the
>>>>>> previous email (command # 4), somehow the output text file above
shows
>>>>>> "Volume_mm3"? Could you please help with this as well whether it's
>>>>>> thickness or volume as the output above because I never mentioned
volume in
>>>>>> the command line?*
>>>>>>
>>>>>> *3. As there are 5 ROIs in the DMN, how can I interpret which of the
>>>>>> values from the above text file belong to which ROI? For example, in
the
>>>>>> above text, I am not sure to which ROI "Seg0001" belongs to? When I
open
>>>>>> ocn.mgz file in FreeView, there I get segmentation numbers at the
bottom
>>>>>> right corner, maybe those are the same as described in the text
file? Could
>>>>>> you confirm if that's correct?*
>>>>>>
>>>>>> *Thanks!*
>>>>>>
>>>>>> On Mon, Jul 27, 2020 at 3:25 PM Douglas N. Greve <
>>>>>> dgr...@mgh.harvard.edu> wrote:
>>>>>>
>>>>>>> You would have to divide them yourself. You can do this by:
>>>>>>> 1. Creating a label of that network (mri_annotation2label)
>>>>>>> 2. Creating a binary mask of that network by converting the label
>>>>>>> into a mask (mri_label2label with --outmask option and --regmethod
surface)
>>>>>>> 3. Divide into individual "clusters" using mri_surfcluster --in
>>>>>>> mask.mgz --thmin 0.5 --ocn ocn.mgz
>>>>>>> 4. Get measures for each of the clusters, eg, mri_segstats
>>>>>>> --excludeid 0 --seg ocn.mgz --i lh.thickness --sum
>>>>>>> sum.network7.thickness.dat
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On 7/27/2020 4:08 PM, Martin Juneja wrote:
>>>>>>>
>>>>>>> External Email - Use Caution
>>>>>>> Hi Doug,
>>>>>>>
>>>>>>> By each region I mean cortical measures of *every individual region
>>>>>>> that is part of a network*. For example, for the network 7 i.e.,
>>>>>>> DMN, I am interested in getting cortical measures of 4 regions
shown in the
>>>>>>> following screenshot in red color:
>>>>>>> (and similarly I am interested in getting cortical measures for
every
>>>>>>> individual region of all other 6 networks as well)
>>>>>>>
>>>>>>> [image: DMN_Regions.png]
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Mon, Jul 27, 2020 at 9:33 AM Douglas N. Greve <
>>>>>>> dgr...@mgh.harvard.edu> wrote:
>>>>>>>
>>>>>>>> What do you mean "each region"? Do you mean each vertex?
>>>>>>>>
>>>>>>>> On 7/27/2020 2:19 AM, Martin Juneja wrote:
>>>>>>>>
>>>>>>>> External Email - Use Caution
>>>>>>>>
>>>>>>>> Dear Doug,
>>>>>>>>
>>>>>>>>
>>>>>>>> I ran the following command, but it still gives me network-wise
>>>>>>>> cortical measures. But I am actually looking for cortical measures
of *each
>>>>>>>> region* within each network:
>>>>>>>>
>>>>>>>>
>>>>>>>> *mris_anatomical_stats -th3 -mgz -cortex
2500a/label/lh.cortex.label
>>>>>>>> -f 2500a/stats/lh.aparc.Yeo7.stats -b -a
**Yeo2011_7Networks_N1000.annot
>>>>>>>> -c 2500a/label/aparc.annot.Yeo7.ctab 2500a lh white*
>>>>>>>>
>>>>>>>>
>>>>>>>> INFO: using TH3 volume calc
>>>>>>>>
>>>>>>>> INFO: assuming MGZ format for volumes.
>>>>>>>>
>>>>>>>> INFO: using 2500a/label/lh.cortex.label as mask to calc cortex
>>>>>>>> NumVert, SurfArea and MeanThickness.
>>>>>>>>
>>>>>>>> computing statistics for each annotation in
>>>>>>>> Yeo2011_7Networks_N1000.annot.
>>>>>>>>
>>>>>>>> reading volume
>>>>>>>> /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/mri/wm.mgz...
>>>>>>>>
>>>>>>>> reading input surface
>>>>>>>> /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/surf/lh.white...
>>>>>>>>
>>>>>>>> Using TH3 vertex volume calc
>>>>>>>>
>>>>>>>> Total face volume 284736
>>>>>>>>
>>>>>>>> Total vertex volume 281405 (mask=0)
>>>>>>>>
>>>>>>>> reading input pial surface
>>>>>>>> /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/surf/lh.pial...
>>>>>>>>
>>>>>>>> reading input white surface
>>>>>>>> /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/surf/lh.white...
>>>>>>>>
>>>>>>>> reading colortable from annotation file...
>>>>>>>>
>>>>>>>> colortable with 8 entries read (originally MyColorLUT)
>>>>>>>>
>>>>>>>> Saving annotation colortable 2500a/label/aparc.annot.Yeo7.ctab
>>>>>>>>
>>>>>>>>
>>>>>>>> table columns are:
>>>>>>>>
>>>>>>>> number of vertices
>>>>>>>>
>>>>>>>> total surface area (mm^2)
>>>>>>>>
>>>>>>>> total gray matter volume (mm^3)
>>>>>>>>
>>>>>>>> average cortical thickness +- standard deviation (mm)
>>>>>>>>
>>>>>>>> integrated rectified mean curvature
>>>>>>>>
>>>>>>>> integrated rectified Gaussian curvature
>>>>>>>>
>>>>>>>> folding index
>>>>>>>>
>>>>>>>> intrinsic curvature index
>>>>>>>>
>>>>>>>> structure name
>>>>>>>>
>>>>>>>>
>>>>>>>> atlas_icv (eTIV) = 1393613 mm^3 (det: 1.397882 )
>>>>>>>>
>>>>>>>> lhCtxGM: 279618.799 278859.000 diff= 759.8 pctdiff= 0.272
>>>>>>>>
>>>>>>>> rhCtxGM: 283065.244 282415.000 diff= 650.2 pctdiff= 0.230
>>>>>>>>
>>>>>>>> lhCtxWM: 221173.591 221952.500 diff= -778.9 pctdiff=-0.352
>>>>>>>>
>>>>>>>> rhCtxWM: 221330.870 222469.500 diff=-1138.6 pctdiff=-0.514
>>>>>>>>
>>>>>>>> SubCortGMVol 57065.000
>>>>>>>>
>>>>>>>> SupraTentVol 1072714.504 (1069458.000) diff=3256.504 pctdiff=0.304
>>>>>>>>
>>>>>>>> SupraTentVolNotVent 1066020.504 (1062764.000) diff=3256.504
>>>>>>>> pctdiff=0.305
>>>>>>>>
>>>>>>>> BrainSegVol 1210413.000 (1208649.000) diff=1764.000 pctdiff=0.146
>>>>>>>>
>>>>>>>> BrainSegVolNotVent 1201232.000 (1200612.504) diff=619.496
>>>>>>>> pctdiff=0.052
>>>>>>>>
>>>>>>>> BrainSegVolNotVent 1201232.000
>>>>>>>>
>>>>>>>> CerebellumVol 138356.000
>>>>>>>>
>>>>>>>> VentChorVol 6694.000
>>>>>>>>
>>>>>>>> 3rd4th5thCSF 2487.000
>>>>>>>>
>>>>>>>> CSFVol 723.000, OptChiasmVol 112.000
>>>>>>>>
>>>>>>>> MaskVol 1616427.000
>>>>>>>>
>>>>>>>> 8855 5731 2791 0.977 1.446 0.081 0.035 129
>>>>>>>> 14.2 FreeSurfer_Defined_Medial_Wall
>>>>>>>>
>>>>>>>> 28199 18195 45320 2.362 0.630 0.131 0.031 374
>>>>>>>> 34.3 7Networks_1
>>>>>>>>
>>>>>>>> 21313 13976 38622 2.484 0.632 0.119 0.027 239
>>>>>>>> 22.6 7Networks_2
>>>>>>>>
>>>>>>>> 16377 10929 29338 2.522 0.521 0.116 0.024 192
>>>>>>>> 15.7 7Networks_3
>>>>>>>>
>>>>>>>> 12076 8058 25013 2.797 0.650 0.119 0.028 146
>>>>>>>> 13.2 7Networks_4
>>>>>>>>
>>>>>>>> 11151 7626 29041 3.067 0.733 0.127 0.033 167
>>>>>>>> 14.5 7Networks_5
>>>>>>>>
>>>>>>>> 16205 10841 32923 2.607 0.631 0.123 0.028 228
>>>>>>>> 17.6 7Networks_6
>>>>>>>>
>>>>>>>> 34755 23626 78358 2.825 0.621 0.125 0.029 494
>>>>>>>> 39.7 7Networks_7
>>>>>>>>
>>>>>>>> On Fri, Jul 17, 2020 at 9:35 AM Douglas N. Greve <
>>>>>>>> dgr...@mgh.harvard.edu> wrote:
>>>>>>>>
>>>>>>>>>
>>>>>>>>> Try something like
>>>>>>>>> mris_anatomical_stats -th3 -mgz -cortex ../label/lh.cortex.label
-f
>>>>>>>>> ../stats/lh.yeo.stats -b -a ../label/lh.yeo.annot -c
>>>>>>>>> ../label/yeo.annot.ctab 1040 lh white
>>>>>>>>>
>>>>>>>>> Assuming that your yeo atlas is in
>>>>>>>>> $SUBJECTS_DIR/$subject/label/lh.yeo.annot
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 7/15/2020 2:05 PM, Martin Juneja wrote:
>>>>>>>>>
>>>>>>>>> External Email - Use Caution
>>>>>>>>> Dear Doug,
>>>>>>>>>
>>>>>>>>> Thanks for your response !
>>>>>>>>>
>>>>>>>>> Yes, I have Yeo atlas in the individual space, and recon-all.log
>>>>>>>>> has the following command:
>>>>>>>>>
>>>>>>>>> mris_anatomical_stats -th3 -mgz -cortex ../label/lh.cortex.label
-f
>>>>>>>>> ../stats/lh.aparc.stats -b -a ../label/lh.aparc.annot -c
>>>>>>>>> ../label/aparc.annot.ctab 1040 lh white \n
>>>>>>>>> computing statistics for each annotation in
../label/lh.aparc.annot.
>>>>>>>>>
>>>>>>>>> Could you please help me in customizing this because it seems it
>>>>>>>>> gives me stats for each annotation e.g. stats for 34 areas (for
Desikan
>>>>>>>>> atlas) and 7 networks (for Yeo 7 network, I think this is
averaged over
>>>>>>>>> each network, correct?), but I am looking for stats of the
regions which
>>>>>>>>> constitute those networks (e.g. stats for the areas which are
part of the
>>>>>>>>> default mode network i.e., 4 individual stats of 4 individual red
colored
>>>>>>>>> regions in the following figure).
>>>>>>>>>
>>>>>>>>> [image: DMN.png]
>>>>>>>>>
>>>>>>>>> On Wed, Jul 15, 2020 at 8:54 AM Douglas N. Greve <
>>>>>>>>> dgr...@mgh.harvard.edu> wrote:
>>>>>>>>>
>>>>>>>>>> If you have the Yeo atlas in the individual space, you can use
>>>>>>>>>> mris_anatomical_stats to compute stats the same as in the
Desikan atlas.
>>>>>>>>>> Look in recon-all.log for the command line and customize it as
needed
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 7/14/2020 5:00 PM, Martin Juneja wrote:
>>>>>>>>>>
>>>>>>>>>> External Email - Use Caution
>>>>>>>>>> Hi experts,
>>>>>>>>>>
>>>>>>>>>> I extracted network-wise cortical measures (i.e., 7 cortical
>>>>>>>>>> thickness values for 7 networks for Yeo atlas).
>>>>>>>>>>
>>>>>>>>>> I was wondering if there is a way to get the cortical thickness
of
>>>>>>>>>> each ROI within each of these networks e.g., cortical thickness
values of
>>>>>>>>>> all the ROIs which constitute default-mode network of Yeo's 7
network
>>>>>>>>>> parcellation, and then cortical thickness values of all the ROIs
which
>>>>>>>>>> constitute limbic network of Yeo's 7 network parcellation, and
so on.
>>>>>>>>>>
>>>>>>>>>> I know Desikan atlas can be used to get morphometry measures of
34
>>>>>>>>>> ROIs per hemisphere. But the problem is that e.g., default-mode
ROIs
>>>>>>>>>> from Desikan atlas do not completely overlap with the DMN of
7-network
>>>>>>>>>> parcellation from Yeo atlas. In other words, superior frontal
cortex from
>>>>>>>>>> default-mode network of Yeo 7 network parcellation is a big
chunk compared
>>>>>>>>>> to several small ROIs (some partial and some full) in Desikan
atlas, so I
>>>>>>>>>> do not see any way how to find ROIs which just match with that
superior
>>>>>>>>>> frontal cortex of default-mode of Yeo's 7 network.
>>>>>>>>>>
>>>>>>>>>> Any help would be much appreciated !
>>>>>>>>>>
>>>>>>>>>> _______________________________________________
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