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...

• 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.

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 !

Link to project repository/sources

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

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

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