Re: [HCP-Users] MELODIC denoising vs. released ICA-FIX datasets

[Ely, Benjamin]

Hi Michael,

My initial interest was in comparing aggressive vs. soft regression of the 
noise ICs. When I tried simply regressing them out using fsl_regfilt with and 
without the -a flag, though, I realized that my soft denoised file didn't match 
the HCP denoised file, so this became more of an exercise to figure out why. 
Now that I have the FIX scripts running and can replicate the expected 
behavior, I can go back to comparing apples to aggressively denoised apples :)

Thanks again,
-Ely

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Re: [HCP-Users] MELODIC denoising vs. released ICA-FIX datasets

[Ely, Benjamin]

Thanks Steve, that's good to keep in mind. Our acquisition is a single 
"HCP-like" 15 minute run at MB6, 2.1mm isotropic resolution, TR=1s, AP phase 
encoding, 32-channel head coil on a 3T Skyra; hopefully that gives us a similar 
temporal profile. Sounds like I should compare our temporal stability against 
the HCP's - is there a measure you recommend?

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Re: [HCP-Users] Glasser nature atlas

[Timothy Coalson]

Making a "nifti version" isn't a simple conversion, as some lengthy
conversations on this list have elaborated on.

The good news: if you want a single-subject-specific version, that is
possible, using -cifti-separate and -label-to-volume-mapping, using the
subject's own surfaces.

The bad news: if you want it to be a group atlas, the currently popular
volume registration methods aren't good enough at cortical area alignment
to make our parcellation meaningful in a group-average volume space, and
thus we do not recommend making a volumetric version in such a space.  In
the meantime, surface registration avoids several difficulties inherent to
volume registration, and was instrumental in being able to create the
parcellation at all (via alignment of areal features).

For more detailed discussion:

https://www.mail-archive.com/hcp-users@humanconnectome.org/msg03078.html

Tim


On Tue, Oct 4, 2016 at 9:47 AM, Elam, Jennifer  wrote:

> Hi Peter,
>
> The data for the new parcellation are available in the BALSA database, see
> this thread: http://www.mail-archive.com/hcp-users@humanconnectome.org/
> msg02876.html
>
>
> We recommend using the included Connectome Workbench scene file to
> conveniently view the data in the wb_view part of the software platform.
> Get Connectome Workbench here: https://www.humanconnectome.
> org/software/get-connectome-workbench.html
>
>
> Many of the files are in the CIFTI data format
> ,
> which has a lot of advantages, but if you need NIFTI versions, you can use
> wb_command 
> (distributed as part of Connectome Workbench) to do the conversion.
>
>
> There's lots of help for using wb_command in the HCP-Users mail archive,
> and/or you could post more questions to the list.
>
>
> 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  humanconnectome.org> on behalf of Peter McColgan 
> *Sent:* Tuesday, October 4, 2016 9:29:30 AM
> *To:* hcp-users@humanconnectome.org
> *Subject:* [HCP-Users] Glasser nature atlas
>
>
>
> Dear HCP team,
>
>
> Is it possible to download a nifti version of the Glasser atlas featured
> recently in nature?
>
>
> Thanks
>
>
> bw
>
>
> Peter
>
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Re: [HCP-Users] Custom parcellation for HCP data

[Timothy Coalson]

You might want to compare them to the T1 in an example subject (and perhaps
also volume surface outline for pial), to see what is really going on.  The
pial surfaces of the two hemispheres shouldn't overlap outside the medial
wall.

Are you mapping to the structural volume resolution, or to the fMRI
resolution?  Larger voxels makes this problem harder to resolve.  The label
to volume mapping is also a bit greedy, as partial-volumed voxels are
labeled unconditionally (the "unlabeled" value can win out, if the data
from the label file suggests more volume in a voxel for "unlabeled", but
the partial volume that lies outside the surfaces is ignored, even if it
dominates).  As such, if it is only a few voxels, and only the very edges,
it may not really matter how you resolve the overlap.

Tim


On Tue, Oct 4, 2016 at 12:44 AM, Simon Baker  wrote:

> Hi Tim,
>
> Thanks for your email. To clarify, when I said voxels on the medial wall,
> I was referring to voxels near the sagittal midline of the brain (i.e., I
> wasn't referring to voxels that have been labelled as medial wall). As
> such, these voxels have been labelled as part of a structure in the left
> hemisphere and also labelled as part of a structure in the right
> hemisphere. In which case, how do you suggest we deal with the overlap
> issue?
>
> Thanks again,
>
> Simon
>
>
> On 4 October 2016 at 13:02, Timothy Coalson  wrote:
>
>> Note that this command just makes the left hemisphere take precedence -
>> you said the overlap was in medial wall, which is more of an artifact of
>> processing than a structure of importance, so this should be fine.  The
>> surfaces shouldn't have any overlap on real structures, so ribbon
>> constrained surface to volume mapping should prevent any other serious
>> overlaps.
>>
>> Tim
>>
>>
>> On Mon, Oct 3, 2016 at 8:56 PM, Timothy Coalson  wrote:
>>
>>> Assuming that your label values don't have any overlap (each integer
>>> uniquely identifies not only the area but also the hemisphere), you can do
>>> the math part with wb_command -volume-math:
>>>
>>> wb_command -volume-math 'L + (L == 0) * R'  ${Subject}.custom_raw.nii.gz
>>> -var L ${Subject}.L.custom.nii -var R ${Subject}.R.custom.nii
>>>
>>> You can then make the combined label names text file and use it to
>>> reimport the label file:
>>>
>>> wb_command -volume-label-export-table ${Subject}.L.custom.nii 1
>>> ${Subject}.L.custom.txt
>>> wb_command -volume-label-export-table ${Subject}.R.custom.nii 1
>>> ${Subject}.R.custom.txt
>>> cat ${Subject}.L.custom.txt ${Subject}.R.custom.txt >
>>> ${Subject}.custom.txt
>>> wb_command -volume-label-import ${Subject}.custom_raw.nii.gz ${Subject}
>>> .custom.txt ${Subject}.custom.nii.gz
>>>
>>> I also suggest using .nii.gz, label files compress very well.  Unlike
>>> FSL, workbench doesn't have an environment variable controlling the output
>>> format, you must specify full filenames.
>>>
>>> Tim
>>>
>>>
>>> On Mon, Oct 3, 2016 at 8:29 PM, Simon Baker 
>>> wrote:
>>>
 Hi all,

 We want to use our own custom parcellation with the connectome project
 data. Ultimately the parcellation needs to be a NIFTI volume.

 We mapped our own custom parcellation from fsaverage (.annot file in
 FreeSurfer space) onto an HCP subject (GIFTI surface .label.gii file in
 ${Subject}/MNINonLinear space) separately for each hemisphere, and we would
 like to convert the GIFTI surface files into a NIFTI volume file. Although
 this can be done with the following commands . . .

 [MNINonLinear]$ wb_command -label-to-volume-mapping
 ${Subject}.L.custom.164k_fs_LR.label.gii 
 ${Subject}.L.sphere.164k_fs_LR.surf.gii
 T1w.nii.gz ${Subject}.L.custom.nii -ribbon-constrained
 ${Subject}.L.white.164k_fs_LR.surf.gii ${Subject}.L.pial.164k_fs_LR.s
 urf.gii

 [MNINonLinear]$ wb_command -label-to-volume-mapping
 ${Subject}.R.custom.164k_fs_LR.label.gii 
 ${Subject}.R.sphere.164k_fs_LR.surf.gii
 T1w.nii.gz ${Subject}.R.custom.nii -ribbon-constrained
 ${Subject}.R.white.164k_fs_LR.surf.gii ${Subject}.R.pial.164k_fs_LR.s
 urf.gii

 [MNINonLinear]$ fslmaths ${Subject}.L.custom.nii -add
 ${Subject}.R.custom.nii ${Subject}.custom.nii

 . . . we have found that some voxels on the medial wall are labelled as
 both a left hemisphere region and a right hemisphere region.

 Using wb_command, how can we generate a NIFTI volume file containing
 both the left hemisphere parcellation and the right hemisphere parcellation
 and ensure that each voxel is labelled as only one region?

 Kind regards,

 Simon Baker
 Brain & Mental Health Laboratory
 Monash Institute of Cognitive & Clinical Neurosciences
 Monash University

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Re: [HCP-Users] Glasser nature atlas

[Elam, Jennifer]

Hi Peter,

The data for the new parcellation are available in the BALSA database, see this 
thread: http://www.mail-archive.com/hcp-users@humanconnectome.org/msg02876.html


We recommend using the included Connectome Workbench scene file to conveniently 
view the data in the wb_view part of the software platform. Get Connectome 
Workbench here: 
https://www.humanconnectome.org/software/get-connectome-workbench.html


Many of the files are in the CIFTI data 
format,
 which has a lot of advantages, but if you need NIFTI versions, you can use 
wb_command 
(distributed as part of Connectome Workbench) to do the conversion.


There's lots of help for using wb_command in the HCP-Users mail archive, and/or 
you could post more questions to the list.


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 Peter McColgan 

Sent: Tuesday, October 4, 2016 9:29:30 AM
To: hcp-users@humanconnectome.org
Subject: [HCP-Users] Glasser nature atlas



Dear HCP team,


Is it possible to download a nifti version of the Glasser atlas featured 
recently in nature?


Thanks


bw


Peter

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[HCP-Users] Glasser nature atlas

[Peter McColgan]

Dear HCP team,


Is it possible to download a nifti version of the Glasser atlas featured 
recently in nature?


Thanks


bw


Peter

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Re: [HCP-Users] MELODIC denoising vs. released ICA-FIX datasets

[Harms, Michael]

Glad you got it all figured out.  I’m curious though:  was this exercise just 
to understand the various steps internal to FIX (and hcp_fix) or is there some 
more fundamental reason that you can't use those actual scripts for your 
processing?

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: 
>
 on behalf of "Ely, Benjamin" 
>
Date: Tuesday, October 4, 2016 at 1:07 AM
To: "Burgess, Gregory" >, 
"HCP-Users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] MELODIC denoising vs. released ICA-FIX datasets

Hi Greg,

Thank you very much for your detailed response!

I have now generated a Movement_Regressors.mat file with the correct 24 
parameters (6 rigid body, 6 derivatives, 6 rigid body squared, 6 derivatives 
squared). As I didn't have code handy to run highpass filtering on the movement 
regressors, I based this file on the detrended movement regressors; the HCP's 
highpass filtering is described as "detrending-like", so this should 
(hopefully) give approximately the same result. I also noticed an error in my 
original highpass filtering of the fMRI data that accounts for part of the 
difference I was seeing; the correct sigma is 1389, not 1000 (cutoff = 
2*TR*sigma, so a TR of 0.72s and cutoff of 2000s requires a sigma of 1389; I 
forgot to account for the TR in my original filtering). Between the two, I am 
able to get much closer to matching the HCP's denoised data.

Regarding your second point, I think my setup is removing all of the variance 
in the movement parameters, but not in the noise ICs. fsl_glm, which is how I 
remove the movement parameters, appears to always do full regression. By 
default, though, fsl_regfilt performs partial regression of the specified noise 
ICs. But the difference regarding movement regression would definitely make a 
difference and seems to be one thing that can't be matched using FSL only.

So yes, this was very informative! Thank you again for your help.
-Ely

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Re: [HCP-Users] Converting legacy data to CIFTI format

[Glasser, Matthew]

The lack of a field map is the larger issue, as it makes accurately registering 
the EPI to the T1w image problematic.  Some folks have made use of an "average 
field map" to attempt to improve upon this situation, but I don't have this 
code or personal experience with this.

Lacking the T2w image need not necessarily prevent mapping fMRI data to CIFTI 
as one can take the non T2w parts of PreFreeSurfer (basically everything except 
the T2w to T1w registration and the bias correction), do things like specifying 
the T1w image as the T2w image (e.g. for the MNI registration), run FreeSurfer 
on its own, and then specify the T1w image as the T2w image for PostFreeSurfer. 
 A script that does all of this exists, but is not an "official pipeline."

Peace,

Matt.

From: Kelli Cannon >
Date: Monday, October 3, 2016 at 5:32 PM
To: "hcp-users@humanconnectome.org" 
>
Cc: Matt Glasser >
Subject: Converting legacy data to CIFTI format

Hello,

I would like to convert a legacy dataset into CIFTI format. The legacy dataset 
lacks T2-weighted images or field maps. How does the HCP advise that 
investigators go about converting legacy data into CIFTI format? I assume this 
must be possible, as we can use FreeSurfer to convert to flat maps with such 
data...

For the structural data, the PreFreeSurfer pipeline requires both a T1w and a 
T2w image, so it can not be used on the legacy dataset. One can run the T1w 
images through FreeSurfer's recon-all to approximate the HCP FreeSurfer 
pipeline for lower-resolution data. However, the PostFreeSurfer pipeline 
requires additional input files (e.g., T1w_acpc_dc_restore.nii.gz) that are not 
included in the FreeSurfer output.

Is there a reasonably straightforward way of converting legacy data without T2w 
images into CIFTI format? If not, does HCP plan to create a pipeline for 
preprocessing legacy data?


Best regards,

Kelli Cannon
Graduate Student
UAB Department of Vision Science
kecan...@uab.edu



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Re: [HCP-Users] MELODIC denoising vs. released ICA-FIX datasets

[Ely, Benjamin]

Hi Greg,

Thank you very much for your detailed response!

I have now generated a Movement_Regressors.mat file with the correct 24 
parameters (6 rigid body, 6 derivatives, 6 rigid body squared, 6 derivatives 
squared). As I didn't have code handy to run highpass filtering on the movement 
regressors, I based this file on the detrended movement regressors; the HCP's 
highpass filtering is described as "detrending-like", so this should 
(hopefully) give approximately the same result. I also noticed an error in my 
original highpass filtering of the fMRI data that accounts for part of the 
difference I was seeing; the correct sigma is 1389, not 1000 (cutoff = 
2*TR*sigma, so a TR of 0.72s and cutoff of 2000s requires a sigma of 1389; I 
forgot to account for the TR in my original filtering). Between the two, I am 
able to get much closer to matching the HCP's denoised data.

Regarding your second point, I think my setup is removing all of the variance 
in the movement parameters, but not in the noise ICs. fsl_glm, which is how I 
remove the movement parameters, appears to always do full regression. By 
default, though, fsl_regfilt performs partial regression of the specified noise 
ICs. But the difference regarding movement regression would definitely make a 
difference and seems to be one thing that can't be matched using FSL only.

So yes, this was very informative! Thank you again for your help.
-Ely

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