From satra at mit.edu  Sun Nov 11 18:15:45 2018
From: satra at mit.edu (Satrajit Ghosh)
Date: Sun, 11 Nov 2018 18:15:45 -0500
Subject: [Neuroimaging] Please join us at Coastal Coding for Reproducible
 Neuroimaging: 10-16 Jan 2019 in Miami, FL
In-Reply-To: <8A316B65-EDD9-4B36-8E99-58FE28C156E7@fiu.edu>
References: <8A316B65-EDD9-4B36-8E99-58FE28C156E7@fiu.edu>
Message-ID: <CA+A4wOkcRhZ7cgwZLOFQWo_TqfaTpiF1u8SDU0mt89LqTmT+OA@mail.gmail.com>

---------- Forwarded message ---------
From: Angela Laird <alaird at fiu.edu>
Date: Fri, Nov 9, 2018 at 3:42 PM
Subject: Please join us at Coastal Coding for Reproducible Neuroimaging:
10-16 Jan 2019 in Miami, FL
To:


Dear Colleagues,

Florida International University (FIU) is hosting a Miami workshop + code
sprint in January 2019 called *Coastal Coding for Reproducible Neuroimaging*
:

*Jan 10-12: ReproNim + Nipype 2.0 Workshop*
The goals of this workshop are two-fold. First, to enable attendees to
become familiar with best practices for reproducible analysis
from ReproNim. Second, to learn how to create scalable and re-executable
neuroimaging workflows with the upcoming Nipype 2.0 platform. This event is
targeted towards novice neuroimaging users who meet the following
requirements: (1) Working knowledge of Python 3; and (2) Familiarity with
scripting in at least 1 neuroimaging package (e.g., AFNI, ANTS, FSL,
FreeSurfer, SPM). Advanced users are welcome to learn about the changes and
hack with Nipype developers.

*Jan 14-16: NiMARE Code Sprint*
NiMARE (Neuroimaging Meta-Analysis Research Environment) is a new Python
package for performing analyses using neuroimaging meta-analytic data.
Current tools for neuroimaging meta-analyses are spread out across several
packages and languages. NiMARE aims to provide a standardized interface for
a broad range of algorithms, along with interfaces to open meta-analytic
resources like Neurosynth, NeuroVault, and brainspell. The three-day NiMARE
coding sprint will occur after the Nipype workshop, with a one-day rest
period between, in which participants can enjoy Miami. At the sprint, we?ll
be working to design a standard for representing meta-analytic data, to
add implementations for new tools, and to improve the documentation and
test coverage of the existing codebase. Anyone interested in contributing
to this new package is welcome to join.

*Both the workshop and code sprint will be held at FIU?s scenic Biscayne
Bay Campus*, conveniently located 17 miles from the Miami International
Airport (MIA) or 19 miles from the Fort Lauderdale-Hollywood International
Airport (FLL).

Registration costs are $125 for one event or $200 for both events. Fees
cover breakfast, lunch, snacks, and plenty of coffee.

*Full details can be found at:*
https://coastal-coding.github.io

Please feel free to forward to your colleagues!

Best,
Angie

*****
Dr. Angie Laird, Ph.D.
Professor, Department of Physics
Director, Center for Imaging Science
Florida International University
11200 SW 8th ST
Miami, FL 33199

Office: AHC-4 Room 310
Lab: AHC-4 Room 380
http://cismri.fiu.edu
http://neurolab.fiu.edu

Phone: (305) 348-6737
Fax: (305) 348-6700
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From krishnainet at gmail.com  Mon Nov 12 14:48:48 2018
From: krishnainet at gmail.com (sri krishna)
Date: Tue, 13 Nov 2018 01:18:48 +0530
Subject: [Neuroimaging] tranformation to/from dicom to hl7
Message-ID: <CAHZb4M4CGGf8mE_w5xi2gBjMRe-k007PcpYExgsxThOY3cCimw@mail.gmail.com>

Is the library " *Neuroimaging *" supports to/fro transformation
HL7<=>DICOM?

If yes can you please help in pointing the api signatures to achieve the
same.
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From christophe at pallier.org  Tue Nov 13 10:06:35 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Tue, 13 Nov 2018 16:06:35 +0100
Subject: [Neuroimaging] computing cluster size thresholds
Message-ID: <CALyk9bwt_24+h3R1Qg21zXRS_ZRu6yRfh+u9MJFjs-rky=xykg@mail.gmail.com>

Dear all,

I am using nistats to perform a second level group analysis

I'd like to estimate the fwhm from the model's residuals to use afni's
3dFWHMx and 3dCLustSim to look at the distribution of cluster size under
the Null.

Is there a simple way to save a map of the residuals?

Or is there even a better idea ?


--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From gael.varoquaux at normalesup.org  Mon Nov 19 11:42:24 2018
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Mon, 19 Nov 2018 17:42:24 +0100
Subject: [Neuroimaging] New release of nilearn
Message-ID: <20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>

Hi everyone,

We are happy to announce the release of version 0.5 of nilearn, a tool
for multivariate analysis of brain imaging data in Python.

Maybe the most exciting addition in this release is interactive plotting
that naturally embeds in the Jupyter notebook, to plot volumes, surfaces,
or connectomes:
http://nilearn.github.io/plotting/index.html#interactive-plotting

Nilearn can be used for decoding or biomarker extraction, resting-state
fMRI analysis, or more general manipulation of brain images in Python:
http://nilearn.github.io/user_guide.html

A detailed changelog can be found at:
http://nilearn.github.io/whats_new.html

Nilearn is developed by a variety of contributors around the world:
https://github.com/nilearn/nilearn/graphs/contributors

Ga?l


From satra at mit.edu  Mon Nov 19 11:47:17 2018
From: satra at mit.edu (Satrajit Ghosh)
Date: Mon, 19 Nov 2018 11:47:17 -0500
Subject: [Neuroimaging] New release of nilearn
In-Reply-To: <20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
References: <20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
Message-ID: <CA+A4wOnxdma9YZuh3i+bkBdmwX2RzH_L0b8FhYAf3F0mSDcVDw@mail.gmail.com>

Awesome. thank you nilearn team.

cheers,

satra

On Mon, Nov 19, 2018 at 11:43 AM Gael Varoquaux <
gael.varoquaux at normalesup.org> wrote:

> Hi everyone,
>
> We are happy to announce the release of version 0.5 of nilearn, a tool
> for multivariate analysis of brain imaging data in Python.
>
> Maybe the most exciting addition in this release is interactive plotting
> that naturally embeds in the Jupyter notebook, to plot volumes, surfaces,
> or connectomes:
> http://nilearn.github.io/plotting/index.html#interactive-plotting
>
> Nilearn can be used for decoding or biomarker extraction, resting-state
> fMRI analysis, or more general manipulation of brain images in Python:
> http://nilearn.github.io/user_guide.html
>
> A detailed changelog can be found at:
> http://nilearn.github.io/whats_new.html
>
> Nilearn is developed by a variety of contributors around the world:
> https://github.com/nilearn/nilearn/graphs/contributors
>
> Ga?l
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From njvack at wisc.edu  Mon Nov 19 13:56:15 2018
From: njvack at wisc.edu (Nate Vack)
Date: Mon, 19 Nov 2018 12:56:15 -0600
Subject: [Neuroimaging] New release of nilearn
In-Reply-To: <13696_1542645824_0PIG004MV94U7U00_20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
References: <13696_1542645824_0PIG004MV94U7U00_20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
Message-ID: <CA+1BpOsKoZ418c58yYMAeYqT8wuiQLNEu8M-2USKatojv1r13g@mail.gmail.com>

Excellent!

nilearn is great, by the way ? thanks for all your work on it.

Best,
-Nate

On Mon, Nov 19, 2018 at 10:43 AM Gael Varoquaux <
gael.varoquaux at normalesup.org> wrote:

> Hi everyone,
>
> We are happy to announce the release of version 0.5 of nilearn, a tool
> for multivariate analysis of brain imaging data in Python.
>
> Maybe the most exciting addition in this release is interactive plotting
> that naturally embeds in the Jupyter notebook, to plot volumes, surfaces,
> or connectomes:
> http://nilearn.github.io/plotting/index.html#interactive-plotting
>
> Nilearn can be used for decoding or biomarker extraction, resting-state
> fMRI analysis, or more general manipulation of brain images in Python:
> http://nilearn.github.io/user_guide.html
>
> A detailed changelog can be found at:
> http://nilearn.github.io/whats_new.html
>
> Nilearn is developed by a variety of contributors around the world:
> https://github.com/nilearn/nilearn/graphs/contributors
>
> Ga?l
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From alexandre.gramfort at inria.fr  Tue Nov 20 06:15:45 2018
From: alexandre.gramfort at inria.fr (Alexandre Gramfort)
Date: Tue, 20 Nov 2018 12:15:45 +0100
Subject: [Neuroimaging] [ANN] MNE-Python 0.17
Message-ID: <CADeotZq8LtT6P17CzPkmLY6KsUquU7yN1aZFW-Am-98_kw9h4w@mail.gmail.com>

Dear community,

We are very pleased to announce the new 0.17 release of MNE-Python
(http://martinos.org/mne/stable/).

A few highlights
============
- This release will be the last release compatible with Python 2. The
next version will be Python 3.5+ only.
- Better support for Annotations, including readers for EEGLAB,
BrainVision, EDF, CSV, TXT and Brainstorm formats, and a new tutorial
in the documentation to dive in.
- Better support to import data from FieldTrip and Neurmag 122 systems.
- Add capability to read and save Epochs containing complex data (e.g.
after Hilbert transform).
- Add optically pumped magnetometer dataset and examples.
- New source morph object to unify morphing any type of source
estimates (surface or volume) from one subject to another for group
studies. It is now possible to do group studies when working on the
volume with MNE.
- Add ability to read and write beamformers.
- New source power spectral estimation example for resting-state data
- Better support for Reports, now they can be load/saved in HDF5 and
the existing figures from a report can be removed.
- Add support for reading MATLAB v7.3+ for EEGLAB.
- Add interactive visualization of volume source estimates using
plot_volume_source_estimates
- New BIDS-compatible raw filename construction
- Better helmet visualization for Artemis123 and CTF

Notable API changes
================
- Deprecation of annot and annotmap parameters in mne.io.read_raw_edf
- Deprecated mne.SourceEstimate.morph_precomputed,
mne.SourceEstimate.morph, mne.compute_morph_matrix,
mne.morph_data_precomputed and mne.morph_data in favor of
mne.compute_source_morph
- Calling mne.Epochs.decimate no longer copies the data when decim=1.
- Warning messages are now only logged when warn_explicit is set
(unless a logging file is being used) to avoid duplicate warning
messages.
- src.kind now equals to 'mixed' (and not 'combined') for a mixed
source space (i.e., made of surfaces and volume grids)
- The default value of stop_receive_thread in
mne.realtime.RtEpochs.stop has been changed to True
- Using the mne.io.Raw.add_channels on an instance with memmapped data
will now resize the memmap file to append the new channels on Windows
and Linux
- Mismatches in CTF compensation grade are now checked in inverse computation

For a full list of improvements and API changes, see:

http://martinos.org/mne/stable/whats_new.html#version-0-17

To install the latest release the following command should do the job:

    $ pip install --upgrade mne

As usual, we welcome your bug reports, feature requests, critiques,
and contributions.

Some links:
- https://github.com/mne-tools/mne-python (code + readme on how to install)
- http://martinos.org/mne/stable/ (full MNE documentation)

Follow us on Twitter: https://twitter.com/mne_news

Regards,
The MNE-Python developers

40 people made commits that contributed to this release (in alphabetical order):

* Alexandre Gramfort
* Antoine Gauthier
* Britta Westner
* Christian Brodbeck
* Clemens Brunner
* Daniel McCloy
* David Sabbagh
* Denis A. Engemann
* Eric Larson
* Ezequiel Mikulan
* Henrich Kolkhorst
* Hubert Banville
* Jasper J.F. van den Bosch
* Jen Evans
* Joan Massich
* Johan van der Meer
* Jona Sassenhagen
* Kambiz Tavabi
* Lorenz Esch
* Luke Bloy
* MJAS1
* Mainak Jas
* Marcin Koculak
* Marijn van Vliet
* Peter J. Molfese
* Sam Perry
* Sara Sommariva
* Sergey Antopolskiy
* Sheraz Khan
* Stefan Appelhoff
* Stefan Repplinger
* Steven Bethard
* Teekuningas
* Teon Brooks
* Thomas Hartmann
* Thomas Jochmann
* Tom Dupr? la Tour
* Tristan Stenner
* buildqa
* jeythekey

From seralouk at hotmail.com  Tue Nov 20 06:32:36 2018
From: seralouk at hotmail.com (serafim loukas)
Date: Tue, 20 Nov 2018 11:32:36 +0000
Subject: [Neuroimaging] New release of nilearn
In-Reply-To: <20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
References: <20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
Message-ID: <82276B40-8F96-4188-9FC4-90AF63D8C866@hotmail.com>

Amazing news. Thank you for this amazing Python module.

Serafeim

> On 19 Nov 2018, at 17:42, Gael Varoquaux <gael.varoquaux at normalesup.org> wrote:
> 
> Hi everyone,
> 
> We are happy to announce the release of version 0.5 of nilearn, a tool
> for multivariate analysis of brain imaging data in Python.
> 
> Maybe the most exciting addition in this release is interactive plotting
> that naturally embeds in the Jupyter notebook, to plot volumes, surfaces,
> or connectomes:
> http://nilearn.github.io/plotting/index.html#interactive-plotting
> 
> Nilearn can be used for decoding or biomarker extraction, resting-state
> fMRI analysis, or more general manipulation of brain images in Python:
> http://nilearn.github.io/user_guide.html
> 
> A detailed changelog can be found at:
> http://nilearn.github.io/whats_new.html
> 
> Nilearn is developed by a variety of contributors around the world:
> https://github.com/nilearn/nilearn/graphs/contributors
> 
> Ga?l
> 
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging


From elef at indiana.edu  Tue Nov 20 06:36:49 2018
From: elef at indiana.edu (Eleftherios Garyfallidis)
Date: Tue, 20 Nov 2018 06:36:49 -0500
Subject: [Neuroimaging] New release of nilearn
In-Reply-To: <82276B40-8F96-4188-9FC4-90AF63D8C866@hotmail.com>
References: <20181119164224.zcoe5amqrjodaefk@phare.normalesup.org>
 <82276B40-8F96-4188-9FC4-90AF63D8C866@hotmail.com>
Message-ID: <CAN6JmWMrgupWA=TdnFMbOJfeUzzRc3hGbTonP8SS94yQHN38Og@mail.gmail.com>

Great, thanks for all the hard work.

On Tue, Nov 20, 2018 at 6:32 AM serafim loukas <seralouk at hotmail.com> wrote:

> Amazing news. Thank you for this amazing Python module.
>
> Serafeim
>
> > On 19 Nov 2018, at 17:42, Gael Varoquaux <gael.varoquaux at normalesup.org>
> wrote:
> >
> > Hi everyone,
> >
> > We are happy to announce the release of version 0.5 of nilearn, a tool
> > for multivariate analysis of brain imaging data in Python.
> >
> > Maybe the most exciting addition in this release is interactive plotting
> > that naturally embeds in the Jupyter notebook, to plot volumes, surfaces,
> > or connectomes:
> > http://nilearn.github.io/plotting/index.html#interactive-plotting
> >
> > Nilearn can be used for decoding or biomarker extraction, resting-state
> > fMRI analysis, or more general manipulation of brain images in Python:
> > http://nilearn.github.io/user_guide.html
> >
> > A detailed changelog can be found at:
> > http://nilearn.github.io/whats_new.html
> >
> > Nilearn is developed by a variety of contributors around the world:
> > https://github.com/nilearn/nilearn/graphs/contributors
> >
> > Ga?l
> >
> > _______________________________________________
> > Neuroimaging mailing list
> > Neuroimaging at python.org
> > https://mail.python.org/mailman/listinfo/neuroimaging
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From satra at mit.edu  Tue Nov 20 07:12:33 2018
From: satra at mit.edu (Satrajit Ghosh)
Date: Tue, 20 Nov 2018 07:12:33 -0500
Subject: [Neuroimaging] [ANN] MNE-Python 0.17
In-Reply-To: <CADeotZq8LtT6P17CzPkmLY6KsUquU7yN1aZFW-Am-98_kw9h4w@mail.gmail.com>
References: <CADeotZq8LtT6P17CzPkmLY6KsUquU7yN1aZFW-Am-98_kw9h4w@mail.gmail.com>
Message-ID: <CA+A4wOmadjtsQ2iSxc=i6dXfe5dJmkZHME1RqwGGpwiRYj2PyA@mail.gmail.com>

great to see all these new releases! keep up the good work!

cheers,

satra

On Tue, Nov 20, 2018 at 6:17 AM Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

> Dear community,
>
> We are very pleased to announce the new 0.17 release of MNE-Python
> (http://martinos.org/mne/stable/).
>
> A few highlights
> ============
> - This release will be the last release compatible with Python 2. The
> next version will be Python 3.5+ only.
> - Better support for Annotations, including readers for EEGLAB,
> BrainVision, EDF, CSV, TXT and Brainstorm formats, and a new tutorial
> in the documentation to dive in.
> - Better support to import data from FieldTrip and Neurmag 122 systems.
> - Add capability to read and save Epochs containing complex data (e.g.
> after Hilbert transform).
> - Add optically pumped magnetometer dataset and examples.
> - New source morph object to unify morphing any type of source
> estimates (surface or volume) from one subject to another for group
> studies. It is now possible to do group studies when working on the
> volume with MNE.
> - Add ability to read and write beamformers.
> - New source power spectral estimation example for resting-state data
> - Better support for Reports, now they can be load/saved in HDF5 and
> the existing figures from a report can be removed.
> - Add support for reading MATLAB v7.3+ for EEGLAB.
> - Add interactive visualization of volume source estimates using
> plot_volume_source_estimates
> - New BIDS-compatible raw filename construction
> - Better helmet visualization for Artemis123 and CTF
>
> Notable API changes
> ================
> - Deprecation of annot and annotmap parameters in mne.io.read_raw_edf
> - Deprecated mne.SourceEstimate.morph_precomputed,
> mne.SourceEstimate.morph, mne.compute_morph_matrix,
> mne.morph_data_precomputed and mne.morph_data in favor of
> mne.compute_source_morph
> - Calling mne.Epochs.decimate no longer copies the data when decim=1.
> - Warning messages are now only logged when warn_explicit is set
> (unless a logging file is being used) to avoid duplicate warning
> messages.
> - src.kind now equals to 'mixed' (and not 'combined') for a mixed
> source space (i.e., made of surfaces and volume grids)
> - The default value of stop_receive_thread in
> mne.realtime.RtEpochs.stop has been changed to True
> - Using the mne.io.Raw.add_channels on an instance with memmapped data
> will now resize the memmap file to append the new channels on Windows
> and Linux
> - Mismatches in CTF compensation grade are now checked in inverse
> computation
>
> For a full list of improvements and API changes, see:
>
> http://martinos.org/mne/stable/whats_new.html#version-0-17
>
> To install the latest release the following command should do the job:
>
>     $ pip install --upgrade mne
>
> As usual, we welcome your bug reports, feature requests, critiques,
> and contributions.
>
> Some links:
> - https://github.com/mne-tools/mne-python (code + readme on how to
> install)
> - http://martinos.org/mne/stable/ (full MNE documentation)
>
> Follow us on Twitter: https://twitter.com/mne_news
>
> Regards,
> The MNE-Python developers
>
> 40 people made commits that contributed to this release (in alphabetical
> order):
>
> * Alexandre Gramfort
> * Antoine Gauthier
> * Britta Westner
> * Christian Brodbeck
> * Clemens Brunner
> * Daniel McCloy
> * David Sabbagh
> * Denis A. Engemann
> * Eric Larson
> * Ezequiel Mikulan
> * Henrich Kolkhorst
> * Hubert Banville
> * Jasper J.F. van den Bosch
> * Jen Evans
> * Joan Massich
> * Johan van der Meer
> * Jona Sassenhagen
> * Kambiz Tavabi
> * Lorenz Esch
> * Luke Bloy
> * MJAS1
> * Mainak Jas
> * Marcin Koculak
> * Marijn van Vliet
> * Peter J. Molfese
> * Sam Perry
> * Sara Sommariva
> * Sergey Antopolskiy
> * Sheraz Khan
> * Stefan Appelhoff
> * Stefan Repplinger
> * Steven Bethard
> * Teekuningas
> * Teon Brooks
> * Thomas Hartmann
> * Thomas Jochmann
> * Tom Dupr? la Tour
> * Tristan Stenner
> * buildqa
> * jeythekey
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From arokem at gmail.com  Tue Nov 20 12:25:26 2018
From: arokem at gmail.com (Ariel Rokem)
Date: Tue, 20 Nov 2018 09:25:26 -0800
Subject: [Neuroimaging] dmriprep
Message-ID: <CA+X4w0wbSjn6aJmJ5n4tAo1dv438f4uZX+W831U02pKmXbuiNw@mail.gmail.com>

Hello everyone,

We've started a new project that we call "dmriprep". The goal is to provide
a flexible, yet opinionated, workflow for preprocessing of diffusion MRI
data, that produces not only preprocessed data, but also

We invite your comments, issues and feature requests here:

https://github.com/nipy/dmriprep

These are early days for this thing, so join the fun on the ground floor!

Best wishes,

The dipy/dmriprep developers
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From arokem at gmail.com  Tue Nov 20 12:26:39 2018
From: arokem at gmail.com (Ariel Rokem)
Date: Tue, 20 Nov 2018 09:26:39 -0800
Subject: [Neuroimaging] dmriprep
In-Reply-To: <CA+X4w0wbSjn6aJmJ5n4tAo1dv438f4uZX+W831U02pKmXbuiNw@mail.gmail.com>
References: <CA+X4w0wbSjn6aJmJ5n4tAo1dv438f4uZX+W831U02pKmXbuiNw@mail.gmail.com>
Message-ID: <CA+X4w0wWgOYhsAQpcNAvDBHq8V=ONKs=U3HPQ_8QZ8KSLzREZQ@mail.gmail.com>

Whoops. Hit "send" too soon!

On Tue, Nov 20, 2018 at 9:25 AM Ariel Rokem <arokem at gmail.com> wrote:

> Hello everyone,
>
> We've started a new project that we call "dmriprep". The goal is to
> provide a flexible, yet opinionated, workflow for preprocessing of
> diffusion MRI data, that produces not only preprocessed data, but also
>
>
... but also detailed QA from your data, so that you can decide how you
would like to process the data in subsequent steps.

We invite your comments, issues and feature requests here:
>
> https://github.com/nipy/dmriprep
>
> These are early days for this thing, so join the fun on the ground floor!
>
> Best wishes,
>
> The dipy/dmriprep developers
>
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From elef at indiana.edu  Tue Nov 20 13:07:27 2018
From: elef at indiana.edu (Eleftherios Garyfallidis)
Date: Tue, 20 Nov 2018 13:07:27 -0500
Subject: [Neuroimaging] dmriprep
In-Reply-To: <CA+X4w0wWgOYhsAQpcNAvDBHq8V=ONKs=U3HPQ_8QZ8KSLzREZQ@mail.gmail.com>
References: <CA+X4w0wbSjn6aJmJ5n4tAo1dv438f4uZX+W831U02pKmXbuiNw@mail.gmail.com>
 <CA+X4w0wWgOYhsAQpcNAvDBHq8V=ONKs=U3HPQ_8QZ8KSLzREZQ@mail.gmail.com>
Message-ID: <CAN6JmWMyapMfsQOkuU7pvtyL0Ry0TEQCj1YH=5VxycHygmd1LQ@mail.gmail.com>

Great thanks!

On Tue, Nov 20, 2018 at 12:27 PM Ariel Rokem <arokem at gmail.com> wrote:

> Whoops. Hit "send" too soon!
>
> On Tue, Nov 20, 2018 at 9:25 AM Ariel Rokem <arokem at gmail.com> wrote:
>
>> Hello everyone,
>>
>> We've started a new project that we call "dmriprep". The goal is to
>> provide a flexible, yet opinionated, workflow for preprocessing of
>> diffusion MRI data, that produces not only preprocessed data, but also
>>
>>
> ... but also detailed QA from your data, so that you can decide how you
> would like to process the data in subsequent steps.
>
> We invite your comments, issues and feature requests here:
>>
>> https://github.com/nipy/dmriprep
>>
>> These are early days for this thing, so join the fun on the ground floor!
>>
>> Best wishes,
>>
>> The dipy/dmriprep developers
>>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From tracygilliam5 at gmail.com  Tue Nov 20 14:54:17 2018
From: tracygilliam5 at gmail.com (tracy gilliam)
Date: Tue, 20 Nov 2018 13:54:17 -0600
Subject: [Neuroimaging] Research Collaboration
Message-ID: <CAKUQp0+a6+MP1uKb+B9QBuS=6337f_JtG-nZUsao5KUuBHz8oA@mail.gmail.com>

Dear Colleagues,

Reaching out today for help.

While the National Science Foundation did not approve funding for our Small
Business Innovation Research proposal, we did receive sufficient assessment
from the three merit reviews and panel review to gain keen insights from
our peers and the division into our research focus. Out of 1203 entries,
only 150 grants were awarded. With the excellent feedback, we would like to
move forward with our research to collaborate with university sources. Our
research needs to connect with fMRI studies where researchers used
photographs to map areas of the brain. We are looking for introductions or
recommendations.
We are wanting to understand how cognitive identities can be segmented to
understand patterns of change in communities, cities, states and the
nation. If you are interested, need more information, or can recommend
studies, please message me. Thanks so much. Happy Thanksgiving!

Tracy Gilliam
The Neurographics Group
TracyGilliam5 at gmail.com
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From alessandro.stranieri at gmail.com  Fri Nov 30 11:26:58 2018
From: alessandro.stranieri at gmail.com (Alessandro Stranieri)
Date: Fri, 30 Nov 2018 18:26:58 +0200
Subject: [Neuroimaging] Understanding atlas usage
Message-ID: <CADiMhdhqBE2aSLGGXw64QKw5ievpe6ZbjO1Dk8Mz=Ua7JSk1sg@mail.gmail.com>

Hi,

I am working on a small project with the adhd data. My background is
software engineering, but I am very new to nipy and neuroscience in general
so I might get some terminology wrong.

Currently I am working my way through the correlation/connectome examples.
I have a few doubts but, in order to keep it simple, I will just
post a couple, hoping for help.

1. The fetch_adhd functions states that a maximum of 40 subjects can be
retrieved. This means that if I want to use more, I need to do all the
processing myself from the files at
https://www.nitrc.org. Is it correct?

2. nilearn provides some atlases and I have more or less understood how to
use them. However, on NITRC one can also find 2 functional parcellation
templates, and time series are provided
for those parcellations. I think I managed to create and display
connectomes with those templates but, what if I want to know the label of a
region? Those regions are labelled by numbers.
For example, if I use the aal atlas, I can see 'Precentral_L',
'Precental_R' and so on. Can I do the same with the cc200 for example? I
fear that, since those parcellation are functional
and not anatomical, there are no real labels associated to the region. But
I would still like to have a data-structure that allows me to query things
like: this region is in V1, or in the
frontal lobe or other.

Best Regards,
Alessandro
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From bertrand.thirion at inria.fr  Fri Nov 30 16:59:28 2018
From: bertrand.thirion at inria.fr (bthirion)
Date: Fri, 30 Nov 2018 22:59:28 +0100
Subject: [Neuroimaging] Understanding atlas usage
In-Reply-To: <CADiMhdhqBE2aSLGGXw64QKw5ievpe6ZbjO1Dk8Mz=Ua7JSk1sg@mail.gmail.com>
References: <CADiMhdhqBE2aSLGGXw64QKw5ievpe6ZbjO1Dk8Mz=Ua7JSk1sg@mail.gmail.com>
Message-ID: <aab3db50-35b3-b310-cf2a-c5cf5b6a1796@inria.fr>

On 30/11/2018 17:26, Alessandro Stranieri wrote:
> Hi,
>
> I am working on a small project with the adhd data. My background is 
> software engineering, but I am very new to nipy and neuroscience in 
> general so I might get some terminology wrong.
>
> Currently I am working my way through the correlation/connectome 
> examples. I have a few doubts but, in order to keep it simple, I will 
> just
> post a couple, hoping for help.
You're probably using Nilearn. Could you post these questions on 
Neurostars.org, so that other people can benefit from the answers ?
>
> 1. The fetch_adhd functions states that a maximum of 40 subjects can 
> be retrieved. This means that if I want to use more, I need to do all 
> the processing myself from the files at
> https://www.nitrc.org. Is it correct?
>
Yes.
> 2. nilearn provides some atlases and I have more or less understood 
> how to use them. However, on NITRC one can also find 2 functional 
> parcellation templates, and time series are provided
> for those parcellations. I think I managed to create and display 
> connectomes with those templates but, what if I want to know the label 
> of a region? Those regions are labelled by numbers.
> For example, if I use the aal atlas, I can see 'Precentral_L', 
> 'Precental_R' and so on. Can I do the same with the cc200 for example? 
> I fear that, since those parcellation are functional
> and not anatomical, there are no real labels associated to the region. 
> But I would still like to have a data-structure that allows me to 
> query things like: this region is in V1, or in the
> frontal lobe or other.
>
The best way to learn about parcellations is to run the corresponding 
examples of the library.

Each atlas comes with different information, so you may not have 
functional labels if the atlas comes from a functional parcellation, 
such as resting-state for instance.

HTH.

Bertrand

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