Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)

[Glasser, Matthew]

Also you shouldn’t be using the HCP 7T fMRI data at this time.  See the message 
about this on the list.

Peace,

Matt.

From: 
>
 on behalf of Timothy Coalson >
Date: Friday, July 14, 2017 at 2:57 PM
To: David Hartman >
Cc: "hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] mapping HCP data into 7 functional networks (using 
Thomas Yeo parcellation)

As for the 59k vertices you mentioned, I'm guessing you were looking at a cifti 
file that contains both hemispheres, and excludes the medial wall - if so, this 
actually uses surfaces with 32k vertices.  Unfortunately, our 1.6mm 7T data was 
processed with a mesh that happens to use 59k-vertex surfaces for each 
hemisphere, so there is some potential for confusion here.  You may want to 
read this for an explanation of the cifti format:

http://www.humanconnectome.org/software/workbench-command/-cifti-help

The file containing the 17-network version on balsa also contains the 7-network 
version as the first map:

https://balsa.wustl.edu/file/show/Q2xn

Tim


On Fri, Jul 14, 2017 at 2:42 PM, Timothy Coalson 
> wrote:
We have a version of the 17-network Yeo parcellation here:

https://balsa.wustl.edu/W8wK

163842 sounds like a freesurfer resolution.  If the version you have is on the 
freesurfer atlas, then you can resample it to ours (or resample our 
parcellation to freesurfer's atlas) following these instructions:

https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ#HCPUsersFAQ-9.HowdoImapdatabetweenFreeSurferandHCP?

FAQ 9, "How do I map data between FreeSurfer and HCP?"

Note that if you want the left and right portions of each network separated, or 
want each contiguous piece to be a separate entity, more work is required.  We 
have a script somewhere that does these things...

Tim


On Fri, Jul 14, 2017 at 1:27 PM, David Hartman 
> wrote:

Hi,



Background:

Regarding the parcellation of the cortex into functional networks (“The 
organization of the human cerebral cortex estimated by intrinsic functional 
connectivity,” Yeo et al.) Yeo breaks up the cortex into 7 networks. However, 
his cortical data has 163842 vertices, while the HCP data only has 59412 
vertices.



Question:

I am looking to map the HCP data into these 7 networks, but I don’t see a way 
to get the data into the same format as Yeo’s data (ie. 163842 vertices) to use 
his mapping.

1.  Does anyone know of a way to convert HCP data into the same format as Yeo’s 
data to use his mapping or a direct way to map the HCP data to 7 networks?



Any help would be much appreciated.



Thank you,

David Hartman


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Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)

[Glasser, Matthew]

Most of the released HCP data are on a 32k mesh.  You can find the Yeo 
parcellation on the 32k mesh here: https://balsa.wustl.edu/study/show/WG33

Peace,

Matt.

From: 
>
 on behalf of David Hartman 
>
Date: Friday, July 14, 2017 at 1:27 PM
To: "hcp-users@humanconnectome.org" 
>
Subject: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas 
Yeo parcellation)


Hi,



Background:

Regarding the parcellation of the cortex into functional networks (“The 
organization of the human cerebral cortex estimated by intrinsic functional 
connectivity,” Yeo et al.) Yeo breaks up the cortex into 7 networks. However, 
his cortical data has 163842 vertices, while the HCP data only has 59412 
vertices.



Question:

I am looking to map the HCP data into these 7 networks, but I don’t see a way 
to get the data into the same format as Yeo’s data (ie. 163842 vertices) to use 
his mapping.

1.  Does anyone know of a way to convert HCP data into the same format as Yeo’s 
data to use his mapping or a direct way to map the HCP data to 7 networks?



Any help would be much appreciated.



Thank you,

David Hartman


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Re: [HCP-Users] HCP-style infant protocol: acquisition & parcellation questions

[Glasser, Matthew]

I would recommend that both images are 0.8mm isotropic, though there is no 
matrix size requirement (they just both need to cover the whole brain).  Why do 
you want to use differing resolutions?

You can download my percolation here:  https://balsa.wustl.edu/file/show/3VLx

Peace,

Matt.

From: "Harms, Michael" >
Date: Friday, July 14, 2017 at 8:24 PM
To: Megan Norr >, 
"ugurb...@umn.edu" 
>, 
"weili_...@med.unc.edu" 
>, Matt Glasser 
>
Subject: Re: HCP-style infant protocol: acquisition & parcellation questions


Hi Megan,
Please direct questions to the HCP-Users list, so that others can benefit from 
the responses.  Thx.

I suspect that the HCP Pipelines expect the T1 and T2 to have the same 
resolution and matrix, and likely won’t work if that isn’t the case.  Matt can 
confirm.

See Matt’s Nature paper for a parcellation in young-adults, based specifically 
on HCP data.  You can find the files for the parcellation in Balsa.  (You can 
search the HCP-User list for the exact link).

It is an open question the degree to which parcellations and networks may 
change as a function of age.

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: Megan Norr >
Date: Friday, July 14, 2017 at 2:13 PM
To: Michael Harms >, 
"ugurb...@umn.edu" 
>, 
"weili_...@med.unc.edu" 
>
Subject: HCP-style infant protocol: acquisition & parcellation questions

Hello,

I am a graduate student at UC Berkeley collaborating with Moriah Thomason 
(Wayne State, Berkeley) on an infant neuroimaging study. I attended the HCP 
course last month--it was fantastic!--and I am writing because I have a few 
questions about running HCP-style protocols in infants (< 12 mos).

At the workshop, Michael presented on scan acquisition requirements, and I saw 
that Drs. Ugurbil and Lin are the PIs on the "Baby Development" arm of the HCP 
Lifespan project. So, hopefully you are the right folks to contact! Please 
forward my note along if not.

My first question is whether T1-weighted and T2-weighted structural scans need 
to have the same spatial resolution. Throughout the documentation it seems 
these scans simply are the same resolution (e.g., 0.8mm), but it isn't clear 
whether this is a requirement. Would it be possible to resample one of the 
structural scans to the same resolution as the other, and still run HCP 
pre-processing pipelines (e.g., generate myelin maps, etc.)?

Next, I am wondering what the options are for brain parcellation with infant 
data. I think we can get a sort of "parcellation" by doing ICA with our resting 
state data, but I am curious whether there is a common parcellation in the 
works using HCP-style infant data. I'm also wondering whether there is a 
current parcellation or atlas that is preferred by people who are working on 
the HCP?

Thank you very much for your time, and I look forward to hearing your thoughts!

Best regards,
Megan

--
Megan Norr
Doctoral Student, University of California, Berkeley
Clinical Science, Department of Psychology
Head Graduate Student Instructor
Graduate Assembly of Students in Psychology (GASP)
Berkeley Adult Brain Study, Hinshaw Lab
megan.n...@berkeley.edu

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Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)

[Timothy Coalson]

As for the 59k vertices you mentioned, I'm guessing you were looking at a
cifti file that contains both hemispheres, and excludes the medial wall -
if so, this actually uses surfaces with 32k vertices.  Unfortunately, our
1.6mm 7T data was processed with a mesh that happens to use 59k-vertex
surfaces for each hemisphere, so there is some potential for confusion
here.  You may want to read this for an explanation of the cifti format:

http://www.humanconnectome.org/software/workbench-command/-cifti-help

The file containing the 17-network version on balsa also contains the
7-network version as the first map:

https://balsa.wustl.edu/file/show/Q2xn

Tim


On Fri, Jul 14, 2017 at 2:42 PM, Timothy Coalson  wrote:

> We have a version of the 17-network Yeo parcellation here:
>
> https://balsa.wustl.edu/W8wK
>
> 163842 sounds like a freesurfer resolution.  If the version you have is on
> the freesurfer atlas, then you can resample it to ours (or resample our
> parcellation to freesurfer's atlas) following these instructions:
>
> https://wiki.humanconnectome.org/display/PublicData/HCP+
> Users+FAQ#HCPUsersFAQ-9.HowdoImapdatabetweenFreeSurferandHCP?
>
> FAQ 9, "How do I map data between FreeSurfer and HCP?"
>
> Note that if you want the left and right portions of each network
> separated, or want each contiguous piece to be a separate entity, more work
> is required.  We have a script somewhere that does these things...
>
> Tim
>
>
> On Fri, Jul 14, 2017 at 1:27 PM, David Hartman 
> wrote:
>
>> Hi,
>>
>>
>>
>> *Background:*
>>
>> Regarding the parcellation of the cortex into functional networks (“The
>> organization of the human cerebral cortex estimated by intrinsic functional
>> connectivity,” Yeo et al.) Yeo breaks up the cortex into 7 networks.
>> However, his cortical data has 163842 vertices, while the HCP data only has
>> 59412 vertices.
>>
>>
>>
>> *Question:*
>>
>> I am looking to map the HCP data into these 7 networks, but I don’t see a
>> way to get the data into the same format as Yeo’s data (ie. 163842
>> vertices) to use his mapping.
>>
>> 1.  Does anyone know of a way to convert HCP data into the same format
>> as Yeo’s data to use his mapping or a direct way to map the HCP data to 7
>> networks?
>>
>>
>>
>> Any help would be much appreciated.
>>
>>
>>
>> Thank you,
>>
>> David Hartman
>>
>> ___
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>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>
>
>

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Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)

[Elam, Jennifer]

Hi David,

With 59k vertices, it sounds like you are using the versions of the HCP 
individual data that was preprocessed at 1.6mm resolution for use with the 7T 
data contained in the "Structural Preprocessed for 7T (1.6mm/59k mesh)" package 
in ConnectomeDB. The 3T "Structural Preprocessed" package contains both 164k 
and 32k resolution Conte69-registered standard mesh cortical surfaces in MNI 
space in the {Subject_ID}/MNINonLinear/ and 
{Subject_ID}/MNINonLinear/fsaverage_32k directories, respectively.


In addition, 32k versions of the 7 and 17 Network versions of the Yeo et al. 
2011 parcellation are included in the RSN-networks.32k_fs_LR.dlabel.nii CIFTI 
file that is included in the 900 Subjects group average dataset available in 
ConnectomeDB: https://db.humanconnectome.org/data/projects/HCP_1200


Tim Coalson can give you more info on using wb_command to do surface resampling 
if you want to do that as well.


Best,

Jenn

Jennifer Elam, Ph.D.
Scientific Outreach, Human Connectome Project
Washington University School of Medicine
Department of Neuroscience, Box 8108
660 South Euclid Avenue
St. Louis, MO 63110
314-362-9387
e...@wustl.edu
www.humanconnectome.org



From: hcp-users-boun...@humanconnectome.org 
 on behalf of David Hartman 

Sent: Friday, July 14, 2017 1:27:09 PM
To: hcp-users@humanconnectome.org
Subject: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas 
Yeo parcellation)


Hi,



Background:

Regarding the parcellation of the cortex into functional networks (“The 
organization of the human cerebral cortex estimated by intrinsic functional 
connectivity,” Yeo et al.) Yeo breaks up the cortex into 7 networks. However, 
his cortical data has 163842 vertices, while the HCP data only has 59412 
vertices.



Question:

I am looking to map the HCP data into these 7 networks, but I don’t see a way 
to get the data into the same format as Yeo’s data (ie. 163842 vertices) to use 
his mapping.

1.  Does anyone know of a way to convert HCP data into the same format as Yeo’s 
data to use his mapping or a direct way to map the HCP data to 7 networks?



Any help would be much appreciated.



Thank you,

David Hartman


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[HCP-Users] Meaning of the MMP 180 parcellation names

[Yu (Andy) Huang]

Dear HCP users,

I'm trying to use the multi-model parcellation (MMP) data. I have the label
names for the 180 parcellations, and also their location map available at
https://balsa.wustl.edu/78X3. But cannot find out the full name or meaning
of each name (e.g., what does p32pr mean). Can someone provide a link to
the descriptions of the 180 names?

Thanks a lot!

-- 
Yu (Andy) Huang, Ph.D.
Postdoc fellow at Dept. of Biomedical Engineering, City College of New York
Center for Discovery and Innovation, Rm. 3.320,

85 St Nicholas Terrace, New York, NY 10027

Tel: 1-646-509-8798
Email: andypotat...@gmail.com
  *yhuan...@citymail.cuny.edu* 
http://www.parralab.org/people/yu-andy-huang/


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[HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)

[David Hartman]

Hi,



*Background:*

Regarding the parcellation of the cortex into functional networks (“The
organization of the human cerebral cortex estimated by intrinsic functional
connectivity,” Yeo et al.) Yeo breaks up the cortex into 7 networks.
However, his cortical data has 163842 vertices, while the HCP data only has
59412 vertices.



*Question:*

I am looking to map the HCP data into these 7 networks, but I don’t see a
way to get the data into the same format as Yeo’s data (ie. 163842
vertices) to use his mapping.

1.  Does anyone know of a way to convert HCP data into the same format as
Yeo’s data to use his mapping or a direct way to map the HCP data to 7
networks?



Any help would be much appreciated.



Thank you,

David Hartman

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Re: [HCP-Users] CMRR vs MGH multiband/SMS sequences

[Harms, Michael]

What banding artifact are you referring to?  Could you post a picture to a 
sharing site?

thx
--
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: 
>
 on behalf of A R >
Date: Friday, July 14, 2017 at 8:25 AM
To: "Juranek, Jenifer" 
>
Cc: "HCP-Users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] CMRR vs MGH multiband/SMS sequences

In my experience they both suffer from the same banding artifact affecting the 
middle 25% of slices.


On Jul 11, 2017, at 5:34 PM, Juranek, Jenifer 
> wrote:

Just curious if anyone is aware of head-to-head comparisons of CMRR and MGH 
MB/SMS sequences?
Someone recently mentioned to me that “the general consensus is that MGH 
outperforms CMRR”.
Is there a “general consensus” in the research community on this issue? Any 
differences between dMRI and fMRI applications?
I’m interested in using an HCP-style acquisition protocol for a 5yr study about 
to start…from what I can tell, CMRR MB sequences have been selected across the 
board for HCP-style studies currently funded by NIH.
Does anyone have any thoughts they can share?
Many Thanks,
Jenifer
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
Jenifer Juranek, PhD
Associate Professor
Department of Pediatrics
UTHealth
Houston, TX 77030
713.500.8233


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Re: [HCP-Users] Flat Maps

[Claude Bajada]

Dear David,

Thank you very much for your response. A colleague has forwarded the 
mail to me. I have no idea why I am not receiving your mails, I seem to 
be receiving all the other [HCP-Users] mail. I will check my settings.

Thanks once again and regards,

Claude


>
>
>  Forwarded Message 
> Subject:  Re: [HCP-Users] Flat Maps
> Date: Thu, 13 Jul 2017 16:36:12 -0500
> From: David Van Essen 
> To:   Claude Bajada , hcp-users
> 
>
>
>
> Hi Claude et al.,
>
> 1) The atlas template flatmaps surfaces are available
> at 
> https://github.com/Washington-University/Pipelines/tree/master/global/templates/standard_mesh_atlases
> 164k: colin.cerebral.L.flat.164k_fs_LR.surf.gii
> ;
>  colin.cerebral.R.flat.164k_fs_LR.surf.gii
> 
> 32k: colin.cerebral.L.flat.32k_fs_LR.surf.gii
> ;
>  colin.cerebral.R.flat.32k_fs_LR.surf.gi
> i
>
> 2) 32k versions of the same flatmap surfaces are available (but
> named S900.L.flat.32k_fs_LR.surf.gii
> and S900.R.flat.32k_fs_LR.surf.gii) along with the group average sulc
> and other modalities are in the 900 Subjects Group Average dataset users
> can download from our BALSA database:
> https://balsa.wustl.edu/study/show/WG33
> And also here: https://db.humanconnectome.org/data/projects/HCP_1200
> Upon download, the zipped data unpacks to a folder
> named HCP_S900_GroupAvg_v1
>
> 3) Flatmaps and associated datasets for individual subjects are
> available via ConnectomeDB or Connectome-in-a-Box datasets: The 164k flat 
> maps are in {SubjectID}/MNINonLinear
> and the 32k flat maps are in {SubjectID}/MNINonLinear/fsaverage_LR32k
>
> 4) Let us know if you need help finding other reference datasets.
>
> 5) My earlier response to Claude Bajada  > failed to transmit.  If someone knows a
> different email for Claude,  please forward this email to him separately.
>
> David
>
>> On Jul 10, 2017, at 3:08 AM, Claude Bajada > > wrote:
>>
>> Dear David,
>>
>> Thank you for your reply and apologies for the late response. It
>> appears that I did not receive the email and it was forwarded to me
>> today by a colleague of mine.
>>
>> I am assuming that I can find the atlas flatmap somewhere within the
>> HCP site?
>>
>> Kind regards,
>> Claude
>>>
>>>  Forwarded Message 
>>> Subject: Re: [HCP-Users] Flat Maps
>>> Date: Fri, 7 Jul 2017 10:16:13 -0700
>>> From: David Van Essen >
>>> To: Claude Bajada >> >, hcp-users
>>> >
>>>
>>> Claude et al.,
>>>
>>> The HCP pipelines include a process of registering individual-subject
>>> data to an atlas flat map (originally derived from the human PALS
>>> atlas).  This does not include a process for generating de novo flat
>>> maps.
>>> Other software (e.g., FreeSurfer and Caret) have methods for generating
>>> individual flat-maps. The Caret method is only semi-automated.
>>>
>>> David
>>>
 On Jul 7, 2017, at 5:37 AM, Claude Bajada > wrote:

 Dear all,

 Are there any available scripts (preferably in matlab or python) to
 generate flat maps?

 Regards,

 Claude



 
 
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Re: [HCP-Users] Calculation of myelin maps

[Glasser, Matthew]

It is better for finding brain areas, but that bias correction will also remove 
low spatial frequency differences across subjects that you might be interested 
in (see Glasser et al 2013 Neuroimage for how it works).

The protocol looks fine, though TI=1000 would be better from the perspective of 
evenly spacing CSF, grey matter, and white matter tissue peaks in the image 
intensity histogram.

Matt.

From: Lisa Kramarenko 
>
Date: Friday, July 14, 2017 at 2:34 AM
To: Matt Glasser >
Cc: "hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] Calculation of myelin maps

I'm using Myelin_BC as I thought it would be more accurate due to bias 
correction. I will recalculate with the normal myelin maps.

Can you please tell me why MyelinMaps are better for the stats than 
MyelinMaps_BC? And do I understand correctly that our acquisition protocol is 
okay?

Thanks a lot!


On 14 July 2017 at 02:52, Glasser, Matthew 
> wrote:
Are you using Myelin or MyelinMap_BC?  For finding brain areas use Myelin_BC, 
for statistics on the myelin itself you will want to use MyelinMap.

Peace,
Matt.

From: 
>
 on behalf of Lisa Kramarenko 
>
Date: Thursday, July 13, 2017 at 7:25 AM
To: "hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] Calculation of myelin maps

(just a correction: flip angle 9°, not 90 for MPRAGE)

On 13 July 2017 at 12:19, Lisa Kramarenko 
> wrote:
Dear Matt,

I completed all three structural pipelines and performed PALM comparison for 
myelin maps. However, the result I got was somewhat opposed to expectations 
(patients with early MS having significantly stronger myelination compared to 
healthy controls).
I wondered that maybe something in our acquisition technique could have caused 
an error in the calculation (as they are a bit different from the HCP 
protocol)? I used the following images:

3D MPRAGE (TR = 1900 ms, TE = 2.55 ms, TI = 900 ms, flip angle = 90 , FOV = 240 
x 240 mm2 , matrix size = 240 x 240, 176 slices, slice thickness = 1 mm), no 
fat suppression
T2 SPACE (same matrix, FOV, and number of slices as in the T1w, TR=5000ms, TE= 
393ms, flip angle = 120, slice thickness = 1mm), no fat suppression

Is there anything that would have cause imprecise calculation of myelin maps or 
is it just how our results are?

Thanks a lot!!

Lisa


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Re: [HCP-Users] Calculation of myelin maps

[Lisa Kramarenko]

I'm using Myelin_BC as I thought it would be more accurate due to bias
correction. I will recalculate with the normal myelin maps.

Can you please tell me why MyelinMaps are better for the stats than
MyelinMaps_BC? And do I understand correctly that our acquisition protocol
is okay?

Thanks a lot!


On 14 July 2017 at 02:52, Glasser, Matthew  wrote:

> Are you using Myelin or MyelinMap_BC?  For finding brain areas use
> Myelin_BC, for statistics on the myelin itself you will want to use
> MyelinMap.
>
> Peace,
> Matt.
>
> From:  on behalf of Lisa
> Kramarenko 
> Date: Thursday, July 13, 2017 at 7:25 AM
> To: "hcp-users@humanconnectome.org" 
> Subject: Re: [HCP-Users] Calculation of myelin maps
>
> (just a correction: flip angle 9°, not 90 for MPRAGE)
>
> On 13 July 2017 at 12:19, Lisa Kramarenko 
> wrote:
>
>> Dear Matt,
>>
>> I completed all three structural pipelines and performed PALM comparison
>> for myelin maps. However, the result I got was somewhat opposed to
>> expectations (patients with early MS having significantly stronger
>> myelination compared to healthy controls).
>> I wondered that maybe something in our acquisition technique could have
>> caused an error in the calculation (as they are a bit different from the
>> HCP protocol)? I used the following images:
>>
>> 3D MPRAGE (TR = 1900 ms, TE = 2.55 ms, TI = 900 ms, flip angle = 90 , FOV
>> = 240 x 240 mm2 , matrix size = 240 x 240, 176 slices, slice thickness = 1
>> mm), no fat suppression
>> T2 SPACE (same matrix, FOV, and number of slices as in the T1w,
>> TR=5000ms, TE= 393ms, flip angle = 120, slice thickness = 1mm), no fat
>> suppression
>>
>> Is there anything that would have cause imprecise calculation of myelin
>> maps or is it just how our results are?
>>
>> Thanks a lot!!
>>
>> Lisa
>>
>
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