Re: [HCP-Users] Conversion from 4D data to Cifti (.dtseries.nii)

[Erin W. E. Dickie]

Hi Amal,

Good to hear from somebody from the SIMEXP lab! I think the functions in
ciftify might work well for your use case. I presented to your lab a little
about this last year.

Let me know if there is anything I can do to help.

Thanks,
Erin

On Wed, Mar 28, 2018 at 4:56 PM, Glasser, Matthew 
wrote:

> I think you might need to contact the Petersen lab for support for their
> code.  If you have T1w, T2w, field map and fMRI you could use the HCP
> Pipelines to get CIFTI timeseries.  If you don’t, you might be able to use
> CIFTIFY, a beta tool for mapping legacy fMRI data to CIFTI by Erin Dickie:
>
> https://edickie.github.io/ciftify
>
> Peace,
>
> Matt.
>
> From: Boukhdhir Amal 
> Reply-To: Boukhdhir Amal 
> Date: Wednesday, March 28, 2018 at 3:51 PM
> To: Matt Glasser 
> Subject: Re: [HCP-Users] Conversion from 4D data to Cifti (.dtseries.nii)
>
> Hello,
>
> I have preprocessed volume data (4D Nifti) from the CoRR HNU data and I
> want to convert these volumes into surface data
> to be able to run a Matlab-based parcellation algorithm from Gordon et al,
> 2016 (Resources )
> which is based on Cifti time series.
>
> Resources
>
> 
>
> Your help is truly appreciated ! Thank you !
>
>
>
>
> Cordialement,
>
> *Amal Boukhdhir, étudiante Doctorat*
> Centre de recherche de l’Institut Universitaire de gériatrie de Montréal 
> (SIMEXP
> lab )
> CIUSSS du Centre-Est-de-l’Île-de-Montréal
> Bureau M6808
> 4565, Chemin Queen-Mary
> 
> Montréal (Québec)
> H3W 1W5
>
> Département d'informatique et de Recherche opérationnelle (Computer
> Vision & Geometric Modeling Lab 
> )
> Université de Montréal
> CP 6128 Succ centre-ville
> Montréal (Québec)
> H3C 3J7
> 
>
>
>
> Le mercredi 28 mars 2018 à 12:16:53 UTC-4, Glasser, Matthew <
> glass...@wustl.edu> a écrit :
>
>
> I would need to know more about your study to best assist you.
>
> Peace,
>
> Matt.
>
> From:  on behalf of Boukhdhir Amal
> 
> Reply-To: Boukhdhir Amal 
> Date: Wednesday, March 28, 2018 at 11:07 AM
> To: "hcp-users@humanconnectome.org" 
> Subject: [HCP-Users] Conversion from 4D data to Cifti (.dtseries.nii)
>
> Hello,
>
> I am trying to convert a nifti 4D volume to cifti format but this command
> does not provide a useful image:
> *./wb_command -cifti-convert -to-nifti  path_to_cifti path_to_nifti*
>
> Is it possible to have a CIFTI version inclusing cortical and subcortical
> areas ?  Any insights related to that please ?
>
>
>
>
> Best Regards,
>
> *Amal Boukhdhir, étudiante Doctorat*
> Centre de recherche de l’Institut Universitaire de gériatrie de Montréal 
> (SIMEXP
> lab )
> CIUSSS du Centre-Est-de-l’Île-de-Montréal
> Bureau M6808
> 4565, Chemin Queen-Mary
> 
> Montréal (Québec)
> H3W 1W5
>
> Département d'informatique et de Recherche opérationnelle (Computer
> Vision & Geometric Modeling Lab 
> )
> Université de Montréal
> CP 6128 Succ centre-ville
> Montréal (Québec)
> H3C 3J7
>
>
>
>
> Cordialement,
>
> *Amal Boukhdhir, étudiante Doctorat*
> Centre de recherche de l’Institut Universitaire de gériatrie de Montréal 
> (SIMEXP
> lab )
> CIUSSS du Centre-Est-de-l’Île-de-Montréal
> Bureau M6808
> 4565, Chemin Queen-Mary
> 
> Montréal (Québec)
> H3W 1W5
>
> Département d'informatique et de Recherche opérationnelle (Computer
> Vision & Geometric Modeling Lab 
> )
> Université de Montréal
> CP 6128 Succ centre-ville
> Montréal (Québec)
> H3C 3J7
> 
>
> ___
> HCP-Users mailing list
> HCP-Users@humanconnectome.org
> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>

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Re: [HCP-Users] HCP Pipeline without T2w

[Erin W. E. Dickie]

Thanks for the recommendation Matt!

Pedro, the ciftify toolbox link is here (https://edickie.github.io/ciftify).
The toolbox started with exactly with what you are suggesting (modifying
the .sh files to make them run without T2w images). Over time we ended up
translating the key parts to python and adding more detailed logging and
less intermediate output files.

The current state of the package is a little unstable as I'm currently
working in a few final changes. I'm aiming to publish a more stable release
within the next two weeks.

Let me know if you have any questions.

Thanks,
Erin



On Fri, Mar 16, 2018 at 10:28 AM, Glasser, Matthew 
wrote:

> I would recommend the ciftify toolbox if your data do not meet HCP
> Pipelines acquisition requirements (lacking highres T2w scan and or
> Fieldmap).  The ciftify toolbox is currently beta, but should be fully
> available in the very near future (Erin Dickie the author is CCed and can
> give more details).
>
> Peace,
>
> Matt.
>
> From:  on behalf of Pedro Costa
> Klein 
> Date: Friday, March 16, 2018 at 3:49 AM
> To: "hcp-users@humanconnectome.org" 
> Subject: [HCP-Users] HCP Pipeline without T2w
>
> Dear all,
>
> I have a dataset that I wish to use with the HCP pipeline scripts from the
> repository in https://github.com/Washington-University/Pipelines, but I
> don't have the T2w images for the subjects. Is it possible (perhaps with
> some modification on the .sh files) to use pipeline without the T2w? If so,
> what would be the impact on the outputs and what are the most "critical"
> steps that I must pay attention to while changing the scripts?
>
> Another idea would be to replace the T2w images by a synthetic image (e.g.
> a T2w atlas, like the MNI). Is this option acceptable? If so, what would be
> the best approach for using a atlas replacing the T2w?
>
> Thank you very much for your insights.
>
> --
> *Pedro Costa Klein **(*https://scholar.google.com.br/citations?user=
> ll3ukqMJ)
> *PhD Student*
> *Klinik und Poliklink für Psychiatrie und Psychotherapie
> - Ludwig-Maximilians Universität*
> pedrockl...@gmail.com
>
>
>
>
>
> ___
> HCP-Users mailing list
> HCP-Users@humanconnectome.org
> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>

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Re: [HCP-Users] Best Approach for using old volumetric data to pick parcels-of-interest

[Erin W. E. Dickie]

Hey Mike,

Here's the link to the tools Matt was referring to in the last email.
https://edickie.github.io/ciftify

I think I'm still a little confused about your exact problem. What do you
need to map? Do you already have surfaces for all your participants in
GIFTI format?

Thanks,
Erin

On Thu, Mar 1, 2018 at 2:22 PM, Stevens, Michael <
michael.stev...@hhchealth.org> wrote:

> Hi Tim,
>
>
>
> Thanks.  That’s clear and sounds like a really reasonable approach.
>
>
>
> Can you point me towards the exact files I’d need to reference and maybe
> suggest which function calls I’ll need to use to do the volume-to-surface
> mapping you describe?  I’ll whip up a quick script to loop through about
> 120 datasets from this R01 project and let you know how well it works.
>
>
>
> Mike
>
>
>
>
>
> *From:* Timothy Coalson [mailto:tsc...@mst.edu]
> *Sent:* Friday, February 23, 2018 6:49 PM
> *To:* Glasser, Matthew
> *Cc:* Stevens, Michael; Erin W. E. Dickie; hcp-users@humanconnectome.org
> *Subject:* Re: [HCP-Users] Best Approach for using old volumetric data to
> pick parcels-of-interest
>
>
>
> This is an email from Outside HHC. USE CAUTION opening attachments or
> links from unknown senders.
>
> Surface-based methods may boost your statistical power enough (by better
> alignment, exclusion of irrelevant tissue, and smoothing that doesn't cross
> sulcal banks, if you decide you need smoothing) that you may not need to
> rely as much on existing ROIs.  Parcel-based statistics have a lot of
> power, because the multiple comparisons are orders of magnitude smaller,
> spatially independent noise averages out, and the signal averages
> together.  We believe that a lot of old data would benefit from reanalysis
> using surfaces.
>
>
>
> However, our paper is mainly focused on specificity and continuous data.
> If you have a binary volume ROI and you only need a rough guess of it on
> the surface, you can get approximate answers, in a way that should reduce
> false negatives (and give more false positives) from the surface/volume
> transition problems.  You can map the ROI to the anatomical MNI surfaces of
> a group of subjects, and take the max across subjects.  Each individual may
> miss the expected group ribbon location in any given location, but it is
> very likely that every point in the expected group ribbon location will
> overlap with at least one subject in the group.  If this isn't enough, you
> can dilate the volume ROI a few mm first.
>
>
>
> Tim
>
>
>
>
>
> On Fri, Feb 23, 2018 at 11:18 AM, Glasser, Matthew <glass...@wustl.edu>
> wrote:
>
> Hi Mike,
>
>
>
> We have a preprint out on this exact question and the conclusion is that
> it is really hard to do this accurately for most brain regions:
>
>
>
> https://www.biorxiv.org/content/early/2018/01/29/255620
>
>
>
> Really the best idea is probably to go back and reanalyze the old data
> without volume-based smoothing and aligned across surfaces.  Erin Dickie,
> CCed is working on tools to make this a little easier, but still there are
> issues like needing a field map to get accurate fMRI to structural
> registration.  The good news is that one’s statistical power should be much
> better if brains are actually lined up, and using parcellated analyses
> instead of smoothing offers further benefits.
>
>
>
> Matt.
>
>
>
> *From: *<hcp-users-boun...@humanconnectome.org> on behalf of "Stevens,
> Michael" <michael.stev...@hhchealth.org>
> *Date: *Friday, February 23, 2018 at 8:58 AM
> *To: *"hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>
> *Subject: *[HCP-Users] Best Approach for using old volumetric data to
> pick parcels-of-interest
>
>
>
> Hi everyone,
>
>
>
> There’s been a lot posted here over the past year or two on the challenges
> and limitations of going back-and-forth between volumetric space and
> HCP-defined surface space, with solid arguments for moving to (and sticking
> with) CIFTI-defined brainordinates.  Here, I’m asking a slightly different
> question… The field has decades of research using volume-space fMRI
> timeseries analyses that helps to define where to look in the brain to test
> new hypotheses.  Has anyone got a well-thought-out approach for mapping
> such volume-space ROIs to the parcels within the new HCP 180 atlas?  I ask
> because the specificity of the HCP atlas sometimes offers a half dozen
> candidate parcels for hypothesis-testing for what we previously thought of
> as just one or two regions.  Even though our group currently has a half
> dozen newer NIH-funded studies that use HCP compliant sequences, most of
> th