4 - 6 December 2023

%d Days %h Hours %m Min %s Sec
twinned with the Brainhack Lucca (Italy)

Code of Conduct

As all Brainhacks, BrainHackMarseille is dedicated to providing a harassment-free Brainhack experience for everyone,
regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age or religion.

We do not tolerate harassment of event participants in any form.

Sexual language and imagery is not appropriate for any event venue, including talks.

Event participants violating these rules may be sanctioned or expelled from the event.


The registration deadline is Friday 24th November

Note that there is a limitation of 80 people on site and that the event will be light-hybrid (unconference + some projects).
For late registration, you should anyway fill the form (press on the grey 'REGISTER' button) and send an email to the organization team.
Lunch and social event are not guaranteed for late registrations.

To register to the event, please click on the button below to fill the form.


Monday 4th December

09h00-10h15 Welcome to BrainHack Marseille 2023 (In-person/Online)
  • 09h30-09h45 - Welcoming breakfast
  • 09h45-10h00 - Introduction to the event by Matthieu Gilson & David Meunier (Marseille), Ruggero Basanisi (Lucca)
10h15-13h00 Training session:
  • 10h00-11h30 - Python basic by Julien Caugant
  • 11h30-13h00 - Jupyter notebook / IA tools for machine learning by CEDRE team
13h00-14h00 Lunch Break
14h00-15h20 Sharing expertise session
  • 14h00-14h40 - How to 'Git' on with a clean code by Ruggero Basanisi (Lucca, IMT)
  • 14h40-15h20 - Programming, energy and environement, does 'green-coding' exist? by Marmaduke Woodman, Julien Lefèvre, Nathan Lemmers, Manuel Mercier, Matthieu Gilson (AMU, INS, INT, Insa)
15h20-15h50 Coffee Break
15h50-17h10 Sharing expertise on opensource projects:
17h10-18h00 Short projects presentations between Marseille and Lucca
18h00 BHM social event!

Tuesday 5th December

09h00-13h00 Project Work
  • 09h00-09h30 - breakfast
13h00-14h00 Lunch Break
14h00-18h00 Project Work
  • 17h00-17h30 - Intermediate Project Presentations (Marseille/Lucca)
  • 17h30-18h00 - Open Science game Julien Caugant (Pouillon room)
18h00-19h00 Round table on UsageRight in Scientific Research

Wednesday 6th December

09h00-13h00 Project Work
  • 09h00-09h30 - breakfast
13h00-14h00 Lunch Break
14h00-18h00 Project Work
  • 17h00-18h00 - Final Project Presentations & Brainhack Wrap-up


Here you can find all the informations about the event projects.
If you want to submit a project you should follow the link, fill the form, and open a github issue. Projects can be anything you'd like to work on during the event with other people (coding, discussing a procedure with coworkers, brainstorming about a new idea), as long as you're ready to minimally organize this!

In a scanner darkly: The next 50 years of neuroscience

by Hao Tam Ho & Jean-Michel Hupé

To celebrate its 50th anniversary in 2020, the Society for Neuroscience (SfN) published an upbeat viewpoint on “The Next 50 years of Neuroscience”. Apart from the fact that the article reads like a blatant admission of SfN’s commitment to transhumanism, more worryingly, it exposes the society’s and, by extension, the field’s complete disengagement from reality. There is not a single reference to global warming in the article, which mirrors exactly the daily silence on environmental issues in neuroscience labs around the world. This has led us to suspect that either neuroscientists live in a parallel universe or climate change is pure science fiction.

But hold on! Guilt-ridden and eco-anxious, a number of neuroscientists have recently published (in Neuron and Nature Reviews Neuroscience no less) recommendations on how to reduce the ecological impact of neuroscience research, demonstrating some awareness of what is going on outside the ivory tower. Incredulously, they claim that it is possible for neuroscience labs to "go green” by, e.g., stopping the exhausts from fume hoods when not in use and attending conferences and meetings virtually instead of flying there - all without affecting scientific output, of course. These uninspiring, unambitious and completely ineffective “mini” steps have the advantage of giving neuroscientists the illusion that they are contributing to mitigating rather than aggravating the ongoing environmental crisis. Thus, there is no need to question the objectives of neuroscientific research in the face of a potential ecological and societal collapse within possibly much less than 50 years.

We think it is time for neuroscientists to face reality. Therefore, we propose to write an opinion piece for a major neuroscience journal where we want to clearly and honestly discuss the challenges for the community in this time of ecological and socio-political upheaval. To our knowledge, such a publication does not exist yet. Moreover, we hope to convince the Brainhack community as a whole to support our project, which would send a strong signal to the rest in the field.

We shall start by reading and reacting to the three references listed below. Other key resources will include reports related to climate change and planetary boundaries, as can be retrieved from the IPCC and IPBES websites, for example. If needed, the organisers will present an up-to-date summary of the ecological situation to ensure that all participants are equally well informed. The workshop will follow a "world café" framework where all ideas, reflections and facts useful to the paper shall be discussed in order to bring about a consensus on the content and organisation of the article. The writing of each part will be done in sub-groups with ongoing rotations for the revisions. All participants will be listed as co-authors of the paper. The two organisers will be responsible for finishing up the paper, submitting it and so on. But any participant will be welcome to join this "steering committee" after the workshop.

Link to project repository/sources:
Goals for the BrainHack:
    Milestones :
  • (1) list of issues, arguments or facts that we may bring in the paper
  • (2) consensual short list of what we will put in the paper
  • (3) organized list (paper skeleton)
  • (4) first draft of the paper
Good first issues:
  • issue one: ecological crisis
  • issue two: meaning of research in neuroscience
Communication channels: What will participants learn? Participants will learn to think beyond their specialty and research project. They will learn from other disciplines. They will behave as a responsible citizen instead of just a scientist

Required skills

This is a brainstorming project, non-coding skills are required:
Curiosity 100%
Responsibility 100%
English reading 100%
Writing skills 100%

MYOnset: a Python package to detect EMG onset for electrophysiological studies

by Laure Spieser & Boris Burle
Laboratoire de Neurosciences Cognitives, Aix-Marseille University, CNRS

Among brain’s functions, selecting and executing actions is certainly one of the most important. In this research domain, investigating electromyographic (EMG) activity of muscles involved in actions execution can be an easy way to collect more information on processes of interest. Yet, once EMG is recorded, one needs to process and analyse EMG data in addition to other collected data (e.g., behavior, electrophysiological recordings, etc). Particularly, the detection of EMG bursts onsets is often a critical processing step. However, few tools are available to achieve it, and none was really suitable to use in typical experimental designs of experimental psychology such as reaction time tasks. To meet this need, we developed MYOnset, a Python package designed to help such EMG recordings processing, with particular attention given to the step of EMG bursts onsets and offsets detection.

MYOnset integrates tools for standard preprocessing of EMG recordings, like bipolar derivation and filtering. Regarding EMG onset detection, MYOnset proposes a two-steps method: first, an automatic detection of EMG bursts onsets and offsets, second, a step of visualization and manual correction of detected onsets and offsets. MYOnset integrates two algorithms combining different automatic detection methods. Further, MYOnset proposes a specific window for the visualization and manual correction step, which the most time-consuming step and for which no tool was available. This window offers an adapted view for EMG signals and the associated markers, i.e., experimental triggers and EMG onsets and offsets automatically detected. Importantly, user can interact with onset and offset markers to adjust onsets/offsets positions, insert new onsets/offsets, and remove existing onsets/offsets.

MYOnset package is available on PyPI and GitHub.

Link to project repository/sources:
Goals for the BrainHack:
  • discuss package organisation
  • implement new detection methods (e.g., bayesian changepoint detection)
  • add code testing
Communication channels: What will participants learn? Just have fun together ! and learn on electromyography signal if you're interested

Required skills

This is a data visualisation and physiology project, non-coding skills are required:
Python coding : 80%
Share ideas : 70%
Electrophysiology : 10%

Electronic laboratory notebook presentation and discussion (eLab)

by Sylvain Takerkart & Simon Moré & Killian Rochet

eLab is an electronic laboratory notebook for researchers. Useful for the acquisition of different experimental data/ metadata.

Contribution to no longer have a paper laboratory notebook.

Useful for the data acquisition process by researchers.

Is important in the data standardization process.

Link to project repository/sources:
Goals for the BrainHack:

The aim is the presentation of an electronic laboratory notebook: eLab. Allowing the acquisition of experimental data/ metadata.

This presentation will be followed by a general discussion on the use of laboratory notebooks and how to use them

Communication channels:

via github issue

What will participants learn?
  • electronic lab notebook
  • different way to use it

Required skills

This is a brainstorming project, non-coding skills are required:
sharing ideas : 100%

Surf(ac)ing fMRI data

by Matthieu Gilson, Julien Sein, Jean-Luc Anton, Andrea Bagante, Martin Szinte
mattermost ID: @matgilson , @julien.sein, @jl-anton, @andreabag

The goal of this project is to combine tools in a pipeline for surface-based analysis of fMRI data. Surface-based analysis is a powerful way to align data from different subjects and datasets ( nature , science).

Join us to test tools that will help you to analyze your own fMRI data at the whole-brain level!

The pipeline will combine open-science tools like fMRIprep, Workbench (from HCP), nilearn (Python library). We will provide a couple of subject data to benchmark the tools; they will be formatted in the BIDS format, which is a standard to share data.

Experience in Python is recommended.

You should install a Python distribution like Anaconda beforehand (https://anaconda.org/),we may also use MRI viewer like mango (https://mangoviewer.com/) and tools from Workbench (https://humanconnectome.org/software/connectome-workbench).

Link to project repository/sources:

To be announced

Goals for the BrainHack:
  • contribute to benchmarking of open-source tools in fMRI analysis
  • contribute to promoting sharable open-source tools in local neuroscientific community, beyond the computational community
Communication channels:

via mattermost

via github issue

What will participants learn?
  • MRI data manipulation (including BIDS format)
  • fMRI preprocessing (fmriprep, workbench)
  • decoding (nilearn)
Good first issues
  • issue one: tutorial of nilearn on surface analysis
  • ssue two: find a good issue...

Required skills

This is a coding project, basic git skills are required:
Python coding : 60%
data manipulation: 40%

Building models that interpret neuroimaging data

by Marmaduke Woodman

  • What are you doing, for whom, and why?

We are writing a new implementation of whole brain models oriented towards recent machine learning algorithms. This implementation is for students & post docs who will come up with tomorrow's theory of brain (dys)function and need better tools for doing so.

  • What makes your project special and exciting?

Our project is 🦄🦄🦄 because we are building fine-grained models of neural dynamics in entire cohorts where current whole brain models only maps coarse-grained statistics.

  • How to get started?

The package is being developed at https://github.com/ins-amu/vbjax and includes neural mass and field models, forward models for MEG/fMRI and some data fitting examples.

For the brain hack we will use data from HCP MEG with Brainstorm based preprocessing; scripts at https://github.com/maedoc/friedchicken, but this is not so much the focus of the project.

  • Where to find key resources?

More resources on the background of the modeling is available at https://thevirtualbrain.org and https://www.ebrains.eu/tools/the-virtual-brain.

Link to project repository/sources:


Goals for the BrainHack:
  • discuss use cases with potential users, even those unfamiliar with modeling
  • help new users install and run examples
  • extend existing set of models
  • write new examples for data users have already prepared
  • test deep neural network for more flexible time series modeling
Good first issues Communication channels:

nothing for the moment

  • Brainstorming use cases, data features & models 50%
  • Python coding & debugging 50%
Onboarding documentation


What will participants learn?
  • how to run a whole-brain simulation
  • how to use Jax & NumPyro, potentiall w/ GPUs
  • how to optimize a model to fit data
  • how to do Bayesian MCMC to find parameters consistent with data
Data to use

We are using mostly data from HCP https://db.humanconnectome.org,

Number of collaborators


Credit to collaborators


Development status



bayesian_approaches, causality, connectome, deep_learning, machine_learning, neural_networks, reproducible_scientific_methods, statistical_modelling, systems_neuroscience


Brainstorm, Jupyter, MNE, other

Programming language

containerization, documentation, Matlab, Python


MEG, other

Required skills

This is a brainstorming and coding project, Python skills are required:
Brainstorming use cases, data features & models 50%
Python coding & debugging 50%

NARPS Open Pipelines - A codebase to study variability of fMRI analysis workflows

by Boris Clénet - R&D Engineer, Empenn Team, INRIA Rennes
Mattermost : @bclenet

The goal of the NARPS Open Pipelines project is to create a codebase reproducing the 70 pipelines of the NARPS study (Botvinik-Nezer et al., 2020) and share this as an open resource for the community.

We hope this tools will help analysing and understanding variability of fMRI analysis workflows, hence participating in the reproducible research movement.

Find relevant information about how to get started is in the README.md file.

Join us and contribute to an open-source tool for the community !

Link to project repository/sources


Goals for the BrainHack:
  • start new pipeline reproductions
  • contribute to already stared pipelines
  • proof read the documentation
  • contribute to the documentation
  • write tests for existing pipelines
Good first issues
  • Contribute to the documentation (give feedback, help organizing)
  • Write the pseudo-code for a pipeline
  • Debug already implemented pipelines
Communication channels:

Mattermost channel

  • fMRI statistical analysis: 40%
  • python (+ nipype): 30%
  • writing and organizing documentation: 30%
Onboarding documentation

General information can be found here: README file
How to contribute: CONTRIBUTING file

What will participants learn?
  • using the nipype python library
  • lots of fMRI analysis workflow examples
  • good practices for (python) coding
Data to use

Although it may not be useful during the brainhack, the project's documentation (see corresponding section) gives information about required data.

Number of collaborators


Credit to collaborators

All project contributors are listed in the Credits section of the project.

documentation, pipeline_development

Development status

1_basic structure


reproducible_scientific_methods, statistical_modelling


AFNI, ANTs, BIDS, Datalad, fMRIPrep, FSL, Nipype, SPM

Programming language

documentation, Python



Required skills

This is a brainstorming and coding project, Python skills are required:
fMRI statistical analysis: 40%
python (+ nipype): 30%
writing and organizing documentation: 30%


David Meunier

Research Engineer

Dipankar Bachar

Research Engineer

Julia Sprenger


Melina Cordeau

PhD student

Simon Moré


Matthieu Gilson

Junior Professor

Manuel Mercier

Research Associate

Arnaud Le Troter

Research Engineer

Laurie Mifsud

PhD student

Caroline Strube

Research Coordinator

Christelle Zielinski

Data Analysis Engineer

Hugo Dary

Research Engineer

Marie Bourzeix

PhD student



Salle Pouillon, Saint Charles campus