[HCP-Users] Fwd: how to map cortical thickness data to 180 parcellation (Glasser et al, Nature)

[YC Yao]

> 下面是被转发的邮件：
> 
> 发件人: YC Yao 
> 主题: 回复： [HCP-Users] how to map cortical thickness data to 180 parcellation 
> (Glasser et al, Nature)
> 日期: 2016年10月18日 GMT+8 18:57:22
> 收件人: Timothy Coalson , mha...@wustl.edu
> 
> Dear Tim and Michael Harms,
> 
> Thank your for your reply!
> I have already mapped the Freesurfer native individual data to fs_LR as 
> described in 
> https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ#HCPUsersF 
> AQ-9.HowdoImapdatabetweenFreeSurferandHCP?
> 
> As suggested, I am supposed to use -cifti-create-dense-from-template to make 
> the cifti file containing thickness data, and then use -cifti-parcellate. But 
> I don’t know which subcommand and file to use after checking the 
> corresponding info:
> 
> CREATE CIFTI WITH MATCHING DENSE MAP
>wb_command -cifti-create-dense-from-template
>- file to match brainordinates of
>- output - the output cifti file
> 
>   [-series] - make a dtseries file instead of a dscalar
>   - increment between series points
>   - start value of the series
> 
>  [-unit] - select unit for series (default SECOND)
>  - unit identifier
> 
>   [-volume-all] - specify an input volume file for all voxel data
>   - the input volume file
> 
>  [-from-cropped] - the input is cropped to the size of the voxel data
> in the template file
> 
>   [-cifti] - repeatable - use input data from a cifti file
>   - cifti file containing input data
> 
>   [-metric] - repeatable - use input data from a metric file
>   - which structure to put the metric file into
>   - input metric file
> 
>   [-label] - repeatable - use input data from surface label files
>   - which structure to put the label file into
>   - input label file
> 
>   [-volume] - repeatable - use a volume file for a single volume
>  structure's data
>   - which structure to put the volume file into
>   - the input volume file
> 
>  [-from-cropped] - the input is cropped to the size of the volume
> structure
> 
>   This command helps you make a new dscalar, dtseries, or dlabel cifti 
> file
>   that matches the brainordinate space used in another cifti file.  The
>   template file must have the desired brainordinate space in the mapping
>   along the column direction (for dtseries, dscalar, dlabel, and symmetric
>   dconn this is always the case).  All input cifti files must have a brain
>   models mapping along column and use the same volume space and/or surface
>   vertex count as the template for structures that they contain.  If any
>   input files contain label data, then input files with non-label data are
>   not allowed, and the -series option may not be used.
> 
>   Any structure that isn't covered by an input is filled with zeros or the
>   unlabeled key.
> 
>   The  argument of -metric, -label or -volume must be one of 
> the
>   following:
> 
>   CORTEX_LEFT
>   CORTEX_RIGHT
>   CEREBELLUM
>   ACCUMBENS_LEFT
>   ACCUMBENS_RIGHT
>   ALL_GREY_MATTER
>   ALL_WHITE_MATTER
>   AMYGDALA_LEFT
>   AMYGDALA_RIGHT
>   BRAIN_STEM
>   CAUDATE_LEFT
>   CAUDATE_RIGHT
>   CEREBELLAR_WHITE_MATTER_LEFT
>   CEREBELLAR_WHITE_MATTER_RIGHT
>   CEREBELLUM_LEFT
>   CEREBELLUM_RIGHT
>   CEREBRAL_WHITE_MATTER_LEFT
>   CEREBRAL_WHITE_MATTER_RIGHT
>   CORTEX
>   DIENCEPHALON_VENTRAL_LEFT
>   DIENCEPHALON_VENTRAL_RIGHT
>   HIPPOCAMPUS_LEFT
>   HIPPOCAMPUS_RIGHT
>   INVALID
>   OTHER
>   OTHER_GREY_MATTER
>   OTHER_WHITE_MATTER
>   PALLIDUM_LEFT
>   PALLIDUM_RIGHT
>   PUTAMEN_LEFT
>   PUTAMEN_RIGHT
>   THALAMUS_LEFT
>   THALAMUS_RIGHT
> 
>   The argument to -unit must be one of the following:
> 
>   SECOND
>   HERTZ
>   METER
>   RADIAN
> 
> 
> PARCELLATE A CIFTI FILE
>wb_command -cifti-parcellate
>- the cifti file to parcellate
>- a cifti label file to use for the parcellation
>- which mapping to parcellate (integer, ROW, or COLUMN)
>- output - output cifti file
> 
>   [-spatial-weights] - use voxel volume and either vertex areas or metric
>  files as weights
> 
>  [-left-area-surf] - use a surface for left vertex areas
>  - the left surface to use, areas are in mm^2
> 
>  [-right-area-surf] - use a surface for right vertex areas
>  - the right surface to use, areas are in mm^2
> 
>  [-cerebellum-area-surf] - use a surface for cerebellum vertex areas
>  - the cerebellum surface to use, areas are in
>mm^2
> 
>  [-left-area-metric] - use a metric file for left vertex weights
>  

Re: [HCP-Users] Extracting beta values from analyzed task-data

[Vadim Axelrod]

Hi Micahel and all,

I am sorry to bother you again, but as a follow-up of you last remark, I
feel that
I am confused about the way the baseline is treated:

1. How contrast and beta map can be the same thing? For example, let's take
Social cognition exp. At the first (or second) level there should be 3
regressors: RANDOM, TOM and fixation. So, each one of them should have
separate beta map. So, the contrast between RANDOM and fixation is the
difference between the beta maps of RANDOM and fixation. But what I see is
that the resultant difference map  is equivalent to beta map of RANDOM.  To
best of my understanding, fixation is a condition as any other condition.

2. Is the baseline reflects fixation blocks? If so, in  Language exp. there
is no Fixation block and in Emotional exp. there is only one fixation block
at the end. So, what does the contrast Story > baseline reflect?

3. I wanted to find beta estimate for the fixation in  Social cognition
exp. The only possible pe file for the fixation is the pe file
in cope3.feat (RANDOM-TOM), because two others are the betas of the RANDOM
and TOM. However, its map almost perfectly correlates with the contrast map
of RANDOM-TOM. So, is there beta map for fixation blocks?

Thanks a lot,
Vadim



On Mon, Oct 17, 2016 at 10:30 PM, Harms, Michael  wrote:

>
> And, to answer the Q in your 1st paragraph: yes.
>
> BTW: Since you want the “betas", you should use the pe1.dtseries.nii
> files.  But, as an aside, note that these pe’s/cope’s are the results of
> the Level2 analysis, essentially averaging the Level 1 (individual runs).
> Because all the contrasts were defined in Level 1, and then essentially
> averaged for Level 2, I’m pretty sure that within each
> GrayordinateStats/cope{i}.feat directory, the actual grayordinate-values of
> the pe1.dtseries.nii and cope1.dtseries.nii files are identical. (i.e., we
> didn’t define any “new” contrasts specifically at Level 2, which means that
> the pe1 and cope1 will be the same within each contrast).
>
> cheers,
> -MH
>
> --
> Michael Harms, Ph.D.
> ---
> Conte Center for the Neuroscience of Mental Disorders
> Washington University School of Medicine
> Department of Psychiatry, Box 8134
> 660 South Euclid Ave. Tel: 314-747-6173
> St. Louis, MO  63110 Email: mha...@wustl.edu
>
> From: "Glasser, Matthew" 
> Date: Monday, October 17, 2016 at 2:05 PM
> To: Vadim Axelrod , Michael Harms 
> Cc: "hcp-users@humanconnectome.org" 
> Subject: Re: [HCP-Users] Extracting beta values from analyzed task-data
>
> Grand mean 1.  Find out what SPM uses and adjust your internal scale
> accordingly...
>
>
> Peace,
>
>
> Matt.
> --
> *From:* Vadim Axelrod 
> *Sent:* Monday, October 17, 2016 1:41:35 PM
> *To:* Harms, Michael
> *Cc:* Glasser, Matthew; hcp-users@humanconnectome.org
> *Subject:* Re: [HCP-Users] Extracting beta values from analyzed task-data
>
> Thank you for the help! So, if I want to get a  beta estimate for the
> condition, I should take pe image in the cope#.feat folder, where this
> condition is compared vs. fixation. For example, in the Language task, for
> the Math beta I should take pe image in cope1.feat (MATH-fixation) and the
> Story betas are stored in cope2.feat (STORY-fixation contrast). Correct?
>
> One more question: I noticed that betas might have high values (e.g., 50).
> I am used to work with SPM, and their the beta estimates are order of
> magnitude smaller. Is there some scaling here?
>
> Harms, Michael  wrote:
>
>>
>> No, in the updated pipeline, we simply have some code to merge the
>> copes/betas from the different contrasts into a single file for each task
>> (as we did for the zstat maps, which perhaps somewhat confusingly, are the
>> files named as _tfMRI__level2_hp200_s?.dscalar.nii or
>> _tfMRI__level2_hp200_s?_MSMAll.dscalar.nii —
>> i.e., “zstat” is not part of the file name).
>>
>> There is no need to re-run the pipelines.  You can just use the
>> appropriate “merge” command in wb_shortcuts to merge the copes/betas into a
>> similar single file. As Matt pointed out, those copes/betas are in the
>> GrayordinateStats/cope{i}.feat/{pe1,cope1}.dtseries.nii files — “pe1”
>> for the betas; “cope1” for the contrasts).
>>
>> cheers,
>> -MH
>>
>> --
>> Michael Harms, Ph.D.
>> ---
>> Conte Center for the Neuroscience of Mental Disorders
>> Washington University School of Medicine
>> Department of Psychiatry, Box 8134
>> 660 South Euclid Ave.Tel: 314-747-6173
>> St. Louis, MO  63110Email: mha...@wustl.edu
>>
>> From:  on behalf of Vadim Axelrod
>> 
>> Date: Monday, October 17, 2016 at 8:28 AM
>> To: "Glasser, Matthew" 
>>
>> Cc: 

Re: [HCP-Users] Extracting beta values from analyzed task-data

[Harms, Michael]

Hi,
The fixation blocks establish the implicit baseline.  There is no beta map for 
fixation.

cheers,
-MH

--
Michael Harms, Ph.D.
---
Conte Center for the Neuroscience of Mental Disorders
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave. Tel: 314-747-6173
St. Louis, MO  63110 Email: mha...@wustl.edu

From: Vadim Axelrod >
Date: Tuesday, October 18, 2016 at 8:12 AM
To: Michael Harms >
Cc: "Glasser, Matthew" >, 
"hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] Extracting beta values from analyzed task-data

Hi Micahel and all,

I am sorry to bother you again, but as a follow-up of you last remark, I feel 
that
I am confused about the way the baseline is treated:

1. How contrast and beta map can be the same thing? For example, let's take 
Social cognition exp. At the first (or second) level there should be 3 
regressors: RANDOM, TOM and fixation. So, each one of them should have separate 
beta map. So, the contrast between RANDOM and fixation is the difference 
between the beta maps of RANDOM and fixation. But what I see is that the 
resultant difference map  is equivalent to beta map of RANDOM.  To best of my 
understanding, fixation is a condition as any other condition.

2. Is the baseline reflects fixation blocks? If so, in  Language exp. there is 
no Fixation block and in Emotional exp. there is only one fixation block at the 
end. So, what does the contrast Story > baseline reflect?

3. I wanted to find beta estimate for the fixation in  Social cognition exp. 
The only possible pe file for the fixation is the pe file in cope3.feat 
(RANDOM-TOM), because two others are the betas of the RANDOM and TOM. However, 
its map almost perfectly correlates with the contrast map of RANDOM-TOM. So, is 
there beta map for fixation blocks?

Thanks a lot,
Vadim



On Mon, Oct 17, 2016 at 10:30 PM, Harms, Michael 
> wrote:

And, to answer the Q in your 1st paragraph: yes.

BTW: Since you want the “betas", you should use the pe1.dtseries.nii files.  
But, as an aside, note that these pe’s/cope’s are the results of the Level2 
analysis, essentially averaging the Level 1 (individual runs).  Because all the 
contrasts were defined in Level 1, and then essentially averaged for Level 2, 
I’m pretty sure that within each GrayordinateStats/cope{i}.feat directory, the 
actual grayordinate-values of the pe1.dtseries.nii and cope1.dtseries.nii files 
are identical. (i.e., we didn’t define any “new” contrasts specifically at 
Level 2, which means that the pe1 and cope1 will be the same within each 
contrast).

cheers,
-MH

--
Michael Harms, Ph.D.
---
Conte Center for the Neuroscience of Mental Disorders
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110Email: mha...@wustl.edu

From: "Glasser, Matthew" >
Date: Monday, October 17, 2016 at 2:05 PM
To: Vadim Axelrod >, Michael 
Harms >
Cc: "hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] Extracting beta values from analyzed task-data


Grand mean 1.  Find out what SPM uses and adjust your internal scale 
accordingly...

Peace,

Matt.


From: Vadim Axelrod >
Sent: Monday, October 17, 2016 1:41:35 PM
To: Harms, Michael
Cc: Glasser, Matthew; 
hcp-users@humanconnectome.org
Subject: Re: [HCP-Users] Extracting beta values from analyzed task-data

Thank you for the help! So, if I want to get a  beta estimate for the 
condition, I should take pe image in the cope#.feat folder, where this 
condition is compared vs. fixation. For example, in the Language task, for the 
Math beta I should take pe image in cope1.feat (MATH-fixation) and the Story 
betas are stored in cope2.feat (STORY-fixation contrast). Correct?

One more question: I noticed that betas might have high values (e.g., 50). I am 
used to work with SPM, and their the beta estimates are order of magnitude 
smaller. Is there some scaling here?

Harms, Michael > wrote:

No, in the updated pipeline, we simply have some code to merge the copes/betas 
from the different contrasts into a single file for each task (as we did for 
the zstat maps, which perhaps somewhat 

[HCP-Users] mounting the HCP data on an ec2 isntance instead of s3 access

[Denis-Alexander Engemann]

Dear HCPers,

I recently had a conversation with Robert who suggested to me that it
should be possible to directly mount the HCP data like an EBS volume
instead of using the s3 tools for copying the data file by file.
Any hint would be appreciated.

Cheers,
Denis

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Re: [HCP-Users] Fwd: how to map cortical thickness data to 180 parcellation (Glasser et al, Nature)

[Timothy Coalson]

The options to these commands aren't subcommands, they just provide input
data or modify the details of how the command works.

For -cifti-create-dense-from-template, you can use the dlabel file as the
template (since you only plan to parcellate the data, it isn't critical to
match them to the 91282 grayordinate space), then specify -metric twice,
for left and right thickness files.

For -cifti-parcellate, use the file you just created with
-cifti-create-dense-from-template, use COLUMN for the direction, and it
would be good to use -spatial-weights and the group average vertex area
metrics (these happen to exist in the resampling files,
fs_LR.L.midthickness_va_avg.32k_fs_LR.shape.gii
and fs_LR.R.midthickness_va_avg.32k_fs_LR.shape.gii).  If you run into
trouble with them, you can do it without -spatial-weights and get a similar
answer.

Tim


On Tue, Oct 18, 2016 at 5:59 AM, YC Yao  wrote:

>
>
> 下面是被转发的邮件：
>
> *发件人: *YC Yao 
> *主题: **回复： [HCP-Users] how to map cortical thickness data to 180
> parcellation (Glasser et al, Nature)*
> *日期: *2016年10月18日 GMT+8 18:57:22
> *收件人: *Timothy Coalson , mha...@wustl.edu
>
> Dear Tim and Michael Harms,
>
> Thank your for your reply!
> I have already mapped the Freesurfer native individual data to fs_LR as
> described in https://wiki.humanconnectome.org/display/PublicData/HCP+
> Users+FAQ#HCPUsersF
> AQ-9.HowdoImapdatabetweenFreeSurferandHCP?
>
> As suggested, I am supposed to use -cifti-create-dense-from-template to
> make the cifti file containing thickness data, and then use
> -cifti-parcellate. But I don’t know which subcommand and file to use after
> checking the corresponding info:
>
> CREATE CIFTI WITH MATCHING DENSE MAP
> *   wb_command -cifti-create-dense-from-template*
>- file to match brainordinates of
>- output - the output cifti file
>
>   [-series] - make a dtseries file instead of a dscalar
>   - increment between series points
>   - start value of the series
>
>  [-unit] - select unit for series (default SECOND)
>  - unit identifier
>
>   [-volume-all] - specify an input volume file for all voxel data
>   - the input volume file
>
>  [-from-cropped] - the input is cropped to the size of the voxel
> data
> in the template file
>
>   [-cifti] - repeatable - use input data from a cifti file
>   - cifti file containing input data
>
>   [-metric] - repeatable - use input data from a metric file
>   - which structure to put the metric file into
>   - input metric file
>
>   [-label] - repeatable - use input data from surface label files
>   - which structure to put the label file into
>   - input label file
>
>   [-volume] - repeatable - use a volume file for a single volume
>  structure's data
>   - which structure to put the volume file into
>   - the input volume file
>
>  [-from-cropped] - the input is cropped to the size of the volume
> structure
>
>   This command helps you make a new dscalar, dtseries, or dlabel cifti
> file
>   that matches the brainordinate space used in another cifti file.  The
>   template file must have the desired brainordinate space in the
> mapping
>   along the column direction (for dtseries, dscalar, dlabel, and
> symmetric
>   dconn this is always the case).  All input cifti files must have a
> brain
>   models mapping along column and use the same volume space and/or
> surface
>   vertex count as the template for structures that they contain.  If
> any
>   input files contain label data, then input files with non-label data
> are
>   not allowed, and the -series option may not be used.
>
>   Any structure that isn't covered by an input is filled with zeros or
> the
>   unlabeled key.
>
>   The  argument of -metric, -label or -volume must be one
> of the
>   following:
>
>   CORTEX_LEFT
>   CORTEX_RIGHT
>   CEREBELLUM
>   ACCUMBENS_LEFT
>   ACCUMBENS_RIGHT
>   ALL_GREY_MATTER
>   ALL_WHITE_MATTER
>   AMYGDALA_LEFT
>   AMYGDALA_RIGHT
>   BRAIN_STEM
>   CAUDATE_LEFT
>   CAUDATE_RIGHT
>   CEREBELLAR_WHITE_MATTER_LEFT
>   CEREBELLAR_WHITE_MATTER_RIGHT
>   CEREBELLUM_LEFT
>   CEREBELLUM_RIGHT
>   CEREBRAL_WHITE_MATTER_LEFT
>   CEREBRAL_WHITE_MATTER_RIGHT
>   CORTEX
>   DIENCEPHALON_VENTRAL_LEFT
>   DIENCEPHALON_VENTRAL_RIGHT
>   HIPPOCAMPUS_LEFT
>   HIPPOCAMPUS_RIGHT
>   INVALID
>   OTHER
>   OTHER_GREY_MATTER
>   OTHER_WHITE_MATTER
>   PALLIDUM_LEFT
>   PALLIDUM_RIGHT
>   PUTAMEN_LEFT
>   PUTAMEN_RIGHT
>   THALAMUS_LEFT
>   THALAMUS_RIGHT
>
>   The argument to -unit must be one of the following:
>
>   SECOND
>   HERTZ
>   METER
>   RADIAN
>
>
> PARCELLATE A CIFTI FILE
> *   

[HCP-Users] HCP pipelines with FreeSurfer longitudinal stream?

[Antonin Skoch]

Dear experts,

I am considering the best option how to process the structural MRI data of our 
longitudinal study. We have patients and controls, each subject was scanned 
twice.

Our data meet the requirements of HCP pipelines which I would prefer to use. On 
the other hand, in FreeSurfer there is a special designed "longitudinal stream" 
for this purposes:

https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing

Since the FreeSurferPipeline.sh, FreeSurferHiResWhite.sh and 
FreeSurferHiResPial.sh mostly invoke FreeSurfer routines, there should be an 
option to modify these scripts to do the "longitudinal reconstruction". In most 
of recon-all invocations there should suffice to add directives for 
longitudinal stream, but the modification of FreeSurferHiResPial.sh and 
FreeSurferHiResWhite.sh is more tricky.  I am not sure how to properly set all 
the parameters of mris_make_surfaces there. 
Do you have someone the modified scripts available or can provide guidance for 
proper modification of standard scripts?

Regards,

Antonin Skoch
Institute for Clinical and Experimental Medicine
National Institute of Mental Health
Czech Republic

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Re: [HCP-Users] A question about 164k data

[Glasser, Matthew]

You could use wb_command -cifti-resample with the native mesh non-BC myelin 
maps and the MSMAll deformed spheres.  It will be a single, but long command.

Peace,

Matt.

From: 
>
 on behalf of Aaron C >
Date: Tuesday, October 18, 2016 at 12:21 AM
To: "hcp-users@humanconnectome.org" 
>
Subject: [HCP-Users] A question about 164k data


Dear HCP experts,


I have a question about 164k data. Is there a computational less expensive way 
to calculate MSMAll-registered 164k individual myelin maps from the existing 
data? There were only MSMAll-registered 164k myelin maps with bias field 
corrected there in the existing data, but I am looking for MSMAll-registered 
individual maps without this correction.


Also, could I find S900 group-average 164k data somewhere or are there any 
existing scripts for this purpose? Thank you.

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