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.

• jneurosci
• sciencedirect
• nature

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

• issue one: ecological crisis
• issue two: meaning of research in neuroscience

• via framateam
• via github issue

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.

• myonset

• discuss package organisation
• implement new detection methods (e.g., bayesian changepoint detection)
• add code testing

• via mattermost
• via github issue

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.

• You can find more information about eLabFTW here
• on github

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

via github issue

• electronic lab notebook
• different way to use it

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).

To be announced

• 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

via mattermost

via github issue

• MRI data manipulation (including BIDS format)
• fMRI preprocessing (fmriprep, workbench)
• decoding (nilearn)

• issue one: tutorial of nilearn on surface analysis
• ssue two: find a good issue...

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.

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.

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.

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

https://github.com/ins-amu/vbjax

• 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

• Generate Sphinx docs ins-amu/vbjax#11
• Add tests for partial observed node dynamics ins-amu/vbjax#29
• Add colored noise option ins-amu/vbjax#7

nothing for the moment

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

https://github.com/ins-amu/vbjax#readme

• 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

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

• preprocessed connectomes and BOLD from EBRAINS (https://search.kg.ebrains.eu)
• MEG w/ Brainstorm based preprocessing (https://github.com/maedoc/friedchicken)

2

https://github.com/ins-amu/vbjax/graphs/contributors

Development status

2_releases_existing

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

Brainstorm, Jupyter, MNE, other

containerization, documentation, Matlab, Python

MEG, other

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 !

https://github.com/Inria-Empenn/narps_open_pipelines

• start new pipeline reproductions
• contribute to already stared pipelines
• proof read the documentation
• contribute to the documentation
• write tests for existing pipelines

• Contribute to the documentation (give feedback, help organizing)
• Write the pseudo-code for a pipeline
• Debug already implemented pipelines

Mattermost channel

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

• using the nipype python library
• lots of fMRI analysis workflow examples
• good practices for (python) coding

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

4

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

documentation, pipeline_development

1_basic structure

reproducible_scientific_methods, statistical_modelling

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

documentation, Python

fMRI

PTVR is a free and open-source library for creating visual perception experiments in virtual reality using high-level Python script programming. It aims at helping behavioral science researchers/engineers leverage virtual reality’s power for their research without the need to learn how virtual reality programming works. The philosophy of PTVR is thus very close to the approach of PsychoPy (https://www.psychopy.org/) that has been so important and influential since 2007 for Vision Science researchers displaying stimuli on 2D monitors.

The PTVR experiments are run in a VR headset making it easy to perform experiments in schools, hospitals, etc....

Having your experiment written in a PTVR script is very powerful in terms of reproducibility of science, Notably, this means that using this script allows anyone to integrally reproduce your experiment provided they have installed PTVR and possess a compatible VR headset.

Two types of sessions will be organized by groups of 4 persons:
a/ In the first type of session, PTVR features will be exemplified thanks to demo scripts. In this case, participants will simply discover PTVR by being subjects of simple experiments and by looking at the corresponding code. This should help participants to see whether PTVR might allow them to run some VR experiments they have in mind.
In this case, you do need to prepare anything in advance.

b/ In the second type of session, participants will try and write simple PTVR scripts

In this case, it would be helpful to :

• install a Python IDE like Spyder
• and install PTVR

You can find extensive information on the PTVR website: https://ptvr.inria.fr/

https://gitlab.inria.fr/PTVR_Public/PTVR_Researchers

For sessions of type b/ : deliverables will be new experiments or new demo scripts or new pedagogic scripts written by participants. They will be shared in this github repository:
https://github.com/ericcastet/Brainhack-Marseille-VR

1. Read the Documentation of the PTVR website, especially the 'User Manual' section.

2. issue two: if you already have a VR headset, install PTVR and test its demo scripts.

https://mattermost.brainhack.org/brainhack/channels/bhg23-marseille_virtual_reality_experiments_with_ptvr

https://ptvr.inria.fr/

In sessions of type a/, Participants will experience VR experiments created with PTVR and will be able to assess whether the code is appropriate for them.
In sessions of type b/, Participants will be beta users to help us assess if PTVR code is accessible for non specialists in VR programming.

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

1

For sessions of type b/ (see above), participants will be listed as beta users / tester in the PTVR website : about -> people.

2_releases_existing

reproducible_scientific_methods

Python

fMRI

How can we study the role of higher-order neural interdependencies within and across scales in the brain? Do perception and cognitive arise from higher-order neural interdependencies? Higher-order neural interdependencies (HOIs) are defined as interactions involving more than two neurons, neural populations or brain regions. Recently, the BraiNets team has developed a new tool for higher-order analyses on neural time series (https://github.com/brainets/hoi). The metrics are based on recent advances in information theory and network science, combined with efficient optimisation software (JAX). Participants from all backgrounds are welcome. Novices in the field may be able to familiarise with the metrics and Python toolbox. Advanced participants may take the opportunity to add novel metrics, functionalities, high-level scripts and optimisation tools. All participants may bring their own dataset and/or explore simulated data of spiking neural networks and networks of mean-field signals provided. The project has a GitHub repository (https://github.com/brainets/acrho), where we will share simulated data and the outcomes of the BrainHack as Jupyter Notebooks.

https://github.com/brainets/hoi
https://github.com/brainets/acrho

Share the theoretical and computational tools for higher-order analysis of neural data. Develop novel functionalities and script for optimised analysis on large datasets.

1. issue one: Read the documentation of the HOI toolbox https://brainets.github.io/hoi/
2. issue two: Read important papers cited in the HOI documentation
3. issue two: Explore the dataset containing simulations in the https://github.com/brainets/acrho repository
4. issue two: Try writing Notebooks to perform HOI analyses on the dataset

https://framateam.org/int-brainets/channels/brainhack_acrho_2023

Beginners: 1) learn the basic information theoretical notions and metrics; 2) run analysis on simulated data and extract HOIs.
Advanced: 1) contribute with novel metrics; 2) develop example Jupyter Notebooks with showcases on different types of brain data (resting-state fMRI, task-related MEG/iEEG)

The project contains two sets of data.
In the first dataset (dataset 1) we simulated the activity of a biologically realistic spiking neural network composed of excitatory and inhibitory neurons. We recorded spike times, that can be uploaded through the jupyter notebook Read_spike_train.ipynb. We simulated the response to two different stimuli of different amplitude. This was done for a homogeneous network and for a network including cell-to-cell diversity in inhibitory neurons (see di Volo & Destexhe, Sci Rep 2021). There is a file README to help the reading of the data.
In the second dataset (dataset 2) we employed a 1D spatially extended model of connected mean field models with anatomical connectivity following data in primary visual cortex. We study the response to external stimulation of two different amplitudes. Also for this case, we collected data for mean fields of homogeneous neurons and for a model including cell-to-cell diversity (see di Volo & Destexhe, Sci Rep 2021). A jupyter notebook helps reading data and plotting the spatio-temporal response of the model.

3

Contributors will be listed in the Github repository https://github.com/brainets/acrho

coding_methods, method_development, pipeline_development

0_concept_no_content

information_theory, neural_encoding, neural_networks, systems_neuroscience, other

Python

behavioral, ECOG, EEG, fMRI, MEG

1_commit_push

How to get started?
Python script to call and use Image J package

Where to find key resources?
Image J developer site

Combine image J plugins into python code

To code, generate a analysis suite