From arokem at uw.edu  Thu Jan 25 01:41:27 2024
From: arokem at uw.edu (Ariel Rokem)
Date: Wed, 24 Jan 2024 22:41:27 -0800
Subject: [Neuroimaging] NeuroHackademy 2024: Call for applications
Message-ID: <CA+X4w0zSf6X=DJBfzi3TE6Evxb6FLQLF4R2hJCC_caz2tiGeqA@mail.gmail.com>

We are happy to announce a call for applications to participate in
NeuroHackademy 2024!



This two-week hands-on workshop will be held in a hybrid format, July 29th-
August 10th, 2024 at the University of Washington in Seattle, Washington,
USA, and online.



NeuroHackademy is an opportunity for participants to learn state-of-the-art
methods for the analysis and management of large human neuroscience
datasets while also networking and working with domain experts and each
other on concrete neuroscience problems. The curriculum emphasizes large
datasets of publicly available data (such as the Human Connectome Project,
OpenNeuro, etc.), and on the value of making human neuroscience research
open and reproducible.



NeuroHackademy sessions in the first week will include lectures and
tutorials on data science, machine learning, data visualization, and data
resources, as well as extended Q&A sessions. The second week will be
devoted primarily to participant-directed activities: guided work on team
projects, hackathon sessions, and breakout sessions on topics of interest.
Participants will have an opportunity to present their own work in a
session that will take place in the second week of the event.



This event will be held in a hybrid format, with options to attend
in-person in Seattle, or online. Participants attending online will join
the event through multiple online channels, including zoom-casts of
lectures and breakout sessions, Slack conversations, and collaboration
through GitHub and through the course?s online Juptyerhub.



For more details and a preliminary list of instructors, see:
https://neurohackademy.org/



We are now accepting applications to participate at:
https://neurohackademy.org/apply/



Ideally, applicants should have some prior experience with programming and
with neuroscience data analysis, but we welcome applications from
participants with a variety of relevant backgrounds. For frequently asked
questions, please refer to this page:
https://neurohackademy.org/frequently-asked-questions/



Accepted applicants will be asked to pay a fee of $250 (in person) / $25
(online) upon final registration. Fees cover housing and two meals per day
for in person participants.



Important dates:

April 15th, application deadline

May 6th, notification of acceptance

May 20th, final registration deadline

July 29th - August 10th: NeuroHackademy
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From remi.gau at gmail.com  Wed Jan 31 08:25:37 2024
From: remi.gau at gmail.com (Remi Gau)
Date: Wed, 31 Jan 2024 14:25:37 +0100
Subject: [Neuroimaging] Nilearn 0.10.3
Message-ID: <d79b56e7-9c8d-47f0-a84b-f8a385894589@gmail.com>

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From bertrand.thirion at inria.fr  Wed Jan 31 16:40:18 2024
From: bertrand.thirion at inria.fr (Bertrand Thirion)
Date: Wed, 31 Jan 2024 22:40:18 +0100 (CET)
Subject: [Neuroimaging] Nilearn 0.10.3
In-Reply-To: <d79b56e7-9c8d-47f0-a84b-f8a385894589@gmail.com>
References: <d79b56e7-9c8d-47f0-a84b-f8a385894589@gmail.com>
Message-ID: <384220783.2382724.1706737218948.JavaMail.zimbra@inria.fr>

Thx R?mi ! 
Bertrand 

> From: "remi gau" <remi.gau at gmail.com>
> To: "Neuroimaging analysis in Python" <neuroimaging at python.org>
> Sent: Wednesday, January 31, 2024 2:25:37 PM
> Subject: [Neuroimaging] Nilearn 0.10.3

> Hello everyone!

> We have just released Nilearn 0.10.3!

> Update from PyPi:

> pip install --upgrade nilearn New Surface API

> We are further developing our surface API [
> https://nilearn.github.io/stable/modules/experimental.html | experimental
> module ] and we are interested in getting your feedback on this. We have an
> example showcasing what can be done here: [
> https://nilearn.github.io/stable/auto_examples/08_experimental/plot_surface_image_and_maskers.html#sphx-glr-auto-examples-08-experimental-plot-surface-image-and-maskers-py
> |
> https://nilearn.github.io/stable/auto_examples/08_experimental/plot_surface_image_and_maskers.html#sphx-glr-auto-examples-08-experimental-plot-surface-image-and-maskers-py
> ] .

> Please add comments in this issue: [
> https://github.com/nilearn/nilearn/issues/4158 |
> https://github.com/nilearn/nilearn/issues/4158 ] Changes

> Support for python 3.7 has been dropped. We recommend moving to python >= 3.11.

> Note we have bumped the minimum supported versions of some of our dependencies:

>     *

> Numpy ? v1.19.0
>     *

> SciPy ? v1.8.0
>     *

> Scikit-learn ? v1.0.0
>     *

> Nibabel ? v4.0.0
>     *

> Pandas ? v1.1.5
>     *

> Joblib ? v1.0.0

> This is a minor release with some exciting new features:

>     *

> Allow passing arguments to [
> https://nilearn.github.io/stable/modules/generated/nilearn.glm.first_level.first_level_from_bids.html#nilearn.glm.first_level.first_level_from_bids
> |  first_level_from_bids ] to build first level models that include specific
> set of confounds by relying on the strategies from [
> https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds.html#nilearn.interfaces.fmriprep.load_confounds
> |  load_confounds ]
>     *

> Support passing t and F contrasts to [
> https://nilearn.github.io/stable/modules/generated/nilearn.glm.compute_contrast.html#nilearn.glm.compute_contrast
> |  compute_contrast ] that have fewer columns than the number of estimated
> parameters. Remaining columns are padded with zero
>     *

> [
> https://nilearn.github.io/stable/modules/generated/nilearn.maskers.NiftiSpheresMasker.html#nilearn.maskers.NiftiSpheresMasker
> | NiftiSpheresMasker ] now has generate_report method
>     *

> Update the CompCor strategy in [
> https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds.html#nilearn.interfaces.fmriprep.load_confounds
> |  load_confounds ] and [
> https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html#nilearn.interfaces.fmriprep.load_confounds_strategy
> |  load_confounds_strategy ] to support fmriprep 21.x series and above
>     *

> Combine GLM examples plot_fixed_effect and plot_fiac_analysis into a single
> example plot_two_runs_model
>     *

> Allow setting vmin in [
> https://nilearn.github.io/stable/modules/generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain
> |  plot_glass_brain ] and [
> https://nilearn.github.io/stable/modules/generated/nilearn.plotting.plot_stat_map.html#nilearn.plotting.plot_stat_map
> |  plot_stat_map ]
>     *

> When plotting thresholded statistical maps with a colorbar, the threshold
> value(s) will now be displayed as tick labels on the colorbar

> You can see the full changelog of this release here: [
> https://nilearn.github.io/stable/changes/whats_new.html#id1 |
> https://nilearn.github.io/stable/changes/whats_new.html#id1 ]

> The full list of pull requests included in this version:

> [ https://github.com/nilearn/nilearn/releases/tag/0.10.3 |
> https://github.com/nilearn/nilearn/releases/tag/0.10.3 ]

> The full ?diff? since last version:

> [ https://github.com/nilearn/nilearn/compare/0.10.2...0.10.3 |
> https://github.com/nilearn/nilearn/compare/0.10.2...0.10.3 ]

> Contributors

> Thanks to our 7 new contributors !!!!

>     *

> NIkhil Krish ( [ https://github.com/NIkhilgKrish | @NIkhilgKrish ] ) made their
> first contribution in [ https://github.com/nilearn/nilearn/pull/4042 |  #4042 ]
>     *

> Mia Zawally ( [ https://github.com/MIZwally | @MIZwally ] ) made their first
> contribution in [ https://github.com/nilearn/nilearn/pull/4051 |  #4051 ]
>     *

> Jordi Huguet ( [ https://github.com/jhuguetn | @jhuguetn ] ) made their first
> contribution in [ https://github.com/nilearn/nilearn/pull/4028 |  #4028 ]
>     *

> Tamer Gezici ( [ https://github.com/TamerGezici | @TamerGezici ] ) made their
> first contribution in [ https://github.com/nilearn/nilearn/pull/4122 |  #4122 ]
>     *

> Christina Ro?manith ( [ https://github.com/crossmanith | @crossmanith ] ) made
> their first contribution in [ https://github.com/nilearn/nilearn/pull/4136 |
>  #4136 ]
>     *

> Suramya Pokharel ( [ https://github.com/SuramyaP | @SuramyaP ] ) made their
> first contribution in [ https://github.com/nilearn/nilearn/pull/4159 |  #4159 ]
>     *

> Paul Reiners ( [ https://github.com/paul-reiners | @paul-reiners ] ) made their
> first contribution in [ https://github.com/nilearn/nilearn/pull/4208 |  #4208 ]
> Nilearn links

>    * Github: [ https://github.com/nilearn/nilearn |
>     https://github.com/nilearn/nilearn ]
>    * Documentation: [ https://nilearn.github.io/stable/changes/whats_new.html#id1 |
>     https://nilearn.github.io ]
>    * Pypi: [ https://pypi.org/project/nilearn/ | https://pypi.org/project/nilearn/
>     ]
>     * X: [ https://twitter.com/nilearn | https://twitter.com/nilearn ]
>     * Mastodon: [ https://fosstodon.org/@nilearn | https://fosstodon.org/@nilearn ]
>     * Discord: [ https://discord.gg/SsQABEJHkZ | https://discord.gg/SsQABEJHkZ ]
>    * Digital object identifier: [ https://zenodo.org/doi/10.5281/zenodo.8397156 |
>     https://zenodo.org/doi/10.5281/zenodo.8397156 ]
>    * Research resource identifier: [
>    https://scicrunch.org/resources/data/record/nlx_144509-1/SCR_001362/resolver?q=nilearn&l=nilearn&i=rrid:scr_001362
>     | RRID:SCR_001362 ]

> --

> R?mi Gau (on behalf of the Nilearn dev team)

> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
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