Nistats: the General Linear Model, fast and easy

[bthirion]

Title Nistats: the General Linear Model, fast and easy

Presentor and Affiliation
Bertrand Thirion, Inria

Collaborators
Nistats is developped by a growing international community from the Nilearn ecosystem: https://github.com/nistats/nistats/graphs/contributors.

Github Link (if applicable)
https://github.com/nistats/nistats
https://nistats.github.io/

Abstract (max. 200 words):
Nistats is a pure Python library for applications of statistical
analysis to fMRI. It provides efficient, well documented and tested
tools for the creation of design matrices and for the specification
and fit of mass-univariate models (individual and group-level models).
It also provides utilities to download neuroimaging datasets and comes
with a wide gallery of examples. It leverages Nilearn for data access
and visualization.
Some new capabilities are currently developed: NIDM-compatible
results, support for surface data, mixed-effects model, non-parametric
tests.
The Open Science Room is the perfect venue for GLM users and contributors to
meet, and we would like to demo Nistat's core functionality.

Preferred Session
3. Demo: New advances in open neuroimaging methods

Additional Context
The demo should come after the Nilearn one.

[jbpoline]

Will be happy to be there ! JB

…

On Tue, May 14, 2019 at 4:58 PM bthirion ***@***.***> wrote: *Title* Nistats: the General Linear Model, fast and easy *Presentor and Affiliation* Bertrand Thirion, Inria *Collaborators* Nistats is developped by a growing international community from the Nilearn ecosystem: https://github.com/nistats/nistats/graphs/contributors. *Github Link (if applicable)* https://github.com/nistats/nistats https://nistats.github.io/ *Abstract (max. 200 words):* Nistats is a pure Python library for applications of statistical analysis to fMRI. It provides efficient, well documented and tested tools for the creation of design matrices and for the specification and fit of mass-univariate models (individual and group-level models). It also provides utilities to download neuroimaging datasets and comes with a wide gallery of examples. It leverages Nilearn for data access and visualization. Some new capabilities are currently developed: NIDM-compatible results, support for surface data, mixed-effects model, non-parametric tests. The Open Science Room is the perfect venue for GLM users and contributors to meet, and we would like to demo Nistat's core functionality. *Preferred Session* 3. Demo: New advances in open neuroimaging methods *Additional Context* The demo should come after the Nilearn one. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#14?email_source=notifications&email_token=AACDE2HVDVPGIMJJ5JLV3VTPVMRYJA5CNFSM4HM5TVP2YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4GTYYSDA>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AACDE2DYDGY6LPPGFAYORHDPVMRYJANCNFSM4HM5TVPQ> .

[TimVanMourik]

Hi @bthirion, I’m happy to tell you that we’d like to host your presentation as a lightning talk in the OSR in the Machine learning in Neuroscience session. This will be a talk of 5 minutes + 5 minutes of questions. We decided to rebrand one session of lightning talks to a machine learning theme as a result of many applications around this theme. We cannot give you a slot in your preferred session due to the very high number of applications.

We’ll update the program in the ReadMe.md shortly. We’d much appreciate it if you could submit slides and other presentation material to the presentations folder by means of a Pull Request to this repository, preferably but not necessarily before the presentation.

[bthirion]

Sounds good, thx.
I was thinking of doing a demo rather than a formal presentation.

[TimVanMourik]

The format is completely up to; I’m all in favour of demos as opposed to the formality of presentations 👍