Projects

Projects can be filered by keywords / skills

You can even propose your own by filling the form here!

Project 1 NeuroCausal: Development of an Open Source Platform for the Storage, Sharing, Synthesis, and Meta-Analysis of Neuropsychological Data by Valentina Borghesani

We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR.

Following the steps of what enable a similar transition in functional neuroimaging, we are breaking down our over-ambitious goal in two stages: (1) create a meta-analytical platform covering lesion-related data hence allowing causal inferences; (2) a data-sharing platform taylored to clinical needs.

Find out more on our website: https://neurocausal.github.io/

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 2 Make Brainhack Accessible Worldwide: Translation of Brainhack Glossary into Different Languages by Abigail Licata

Project Description

The Brainhack (BH) Jupyter book is a collaborative project within the BH community aimed at developing an open, inclusive, and interactive document that details the vision of BH events across the globe. This important resource is a supplement to the Neuron 2020 community paper and serves as a common resource for members to draw upon when planning future BH events. While the BH jupyter book is in its early stages of development, there is a crucial aspect of the book that lays its foundation: BH glossary. The BH glossary contains relevant community terms and, given the global nature of BH, translations of the glossary are necessary to encourage understanding and promotion of BH ideals worldwide.

The Jupyter BH book glossary has been translated into Italian, Spanish, German, Russian, Portuguese, and Hindi. This project within the scope of our local Geneva BH event can be conducted in two ways: 1) by improving upon existing glossary translations or 2) by creating a newly translated glossary from a language that has not yet been considered.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 3 Mind-bending VR at your eartips! by Konstantinos Samaras-Tsakiris and Victor Férat

Concocting together electroencephalogram (EEG) earbuds, Virtual Reality (VR) and neural feedback… what can come out of this sci-fi melting pot? Join us to create the most immersive experience that goes a step further from VR, adapting to your mental state.

Neurofeedback is a technique with established therapeutic and educational potential. We are interested to create neurofeedback in virtual reality environments using EEG sampled *only from inside the ear*. We will combine a novel EEG sensor shaped like earbuds and a VR headset to immerse the participants in an environment where they can control a tunable parameter (eg. the gravity!) with their own brain activity. The comfortable form factor of the EEG earbuds can facilitate deeper immersion in the experience, potentially boosting the neurofeedback effect.

But how can we translate the neural signal into meaningful action? Most published research indicates neuromarkers in standard, full-scalp EEG; much less is known about in-ear EEG. In this project we aim to bring together translatable neuromarkers and machine learning to bridge the gap in the feedback loop and give the subject a "mental exercise" that will help them navigate the virtual world.

Over the weekend we can also build a fledgling VR game starting from Unity engine demos to amplify the effect of neurofeedback and make it even more visible. We’ll use the Meta Quest 2 VR headset.

Note (skills):: many different skills will have a role to play in this project, so don't hesitate to join if you're only interested in 1 of the elements!

> Drawing by Lou Planchamp

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 4 Neurodata Without Borders Viewer by Anıl Tuncel

This project puts equal weights on learning and building. While working on this project, we will learn the most loved programming language of our time [3] and build something useful for the neuroscience community with it.

Neurodata Without Borders (NWB) [1] is a standard data format to represent neurophysiology data files. It enables interoperability between neurophysiology data produced by different neuroscience labs. Examples of the data stored in .NWB format range from patch clamp experiments to optical physiology experiments. The Blue Brain Project uses the standard NWB format to store and share the electrophysiology data such as the ion-channels data available on Blue Brain Channelpedia [10].

The underlying storage technology used by the NWB format is the binary HDF [2] format. While storing the files as binary brings advantages on the read/write speed as well as the file size, it stores the data in a way that is not readable by humans. I.e. one cannot open the file in a text editor to see the contents, thus there is need for a viewer.

There exists a viewer software (written in Java) for viewing the HDF files however it does not meet the specific needs of the NWB user as it's not tailored for the neuroscientists. There also exists web services to view the NWB files however they require the users to upload the files to their servers in order to view them.

The NWB View project should be able to run offline, enabling researchers to view their files without relying on web services for both privacy and usability reasons. The project should also run fast and consume little memory since the data stored in NWB files can be vast.

This bring us to Rust. Rust is a language empowering everyone to build reliable and efficient software. It is built to be a language for the next 40 years. For the seventh year, Rust has been named the most loved language in Stack Overflow's Developer Survey [3]. Microsoft Azure's CTO said it is time to halt starting new projects in C/C++ and use Rust [4]. Linus Torvalds announced that Rust will be a part of Linux Kernel [5]. Rust is the most popular language for the WebAssembly [7] and thus it will be important for the future of web development.

Rust is also important for the Python developers. An implementation of Python exists in Rust that allows running Python in the web browser [8]. Besides, there are many Rust-backed Python libraries. Some programming languages are being developed to bring Rust features to Python use-cases [9].

The bioinformatics community is moving to Rust [6]. Neuroscience community can also benefit from a similar move.

Knowing Rust is already a highly valuable asset today and it going to be even more important in the future.

In this project, while learning Rust, we are going to build a simple graphical user interface (GUI) in Rust to display NWB files. There exists powerful GUI libraries in Rust mature enough for building the NWB viewer. The screenshot taken shows the capabilities of the EGUI library.

Some of our requirements are the following.

• Displaying the current and voltage traces from a patch clamp experiment.


• Displaying the contents of the NWB file e.g. the stimuli applied to the cell.


• Computing and displaying statistics about the cell's voltage. E.g. resting membrane potential.


• Approximately computing the rheobase of the cell and displaying it.


• Displaying the experimental notes in the tabular view.

For this we need the software developers, neuroscientists and it would be great to have at least one person with design and aesthetic skills. :)

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 5 BIDSapp-lify Physiopy python toolboxes by Stefano Moia

Physiopy is a community-driven effort that aims to offer a full data preparation pipeline for non-neural physiological recordings and to build consensus on “best practices” among researchers; all in an open, transparent, and reproducible manner. One of the four pillars of the community is a set of Python toolboxes to analyse and preprocess physiological data and obtain regressors to denoise neuroimaging data.

In this project, we want to maximise the support for the Brain Imaging Data Standard (BIDS) schema. We will obtain this by:

• create an interface to read data and metadata in BIDS standard
• reorganise data export to comply with BIDS derivatives
• compose a Docker recipe to automatically create Docker containers at each toolbox release
• add boutiques metadata

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 6 Writing and organising documentation for physiological data handling by Stefano Moia

Non-neuronal physiological recordings adoption in conjunction with neuroimaging data is not a widespread use in neuroimaging research. To facilitate the adoption of such data, Physiopy is working on maintaining a documentation of best practices in dealing with such data. In this project, we work on improving the community documentation by improving the website interface and compiling documentation based on Physiopy best practices meetings and existing literature.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 7 Expand phys2bids data support to more MRI vendors by Stefano Moia

In order to spread non-neuronal physiological recording adoption in neuroimaging, Physiopy maintains a toolbox to translate physiological proprietary data into Brain Imaging Data Standard (BIDS) schema, phys2bids. In this project we'll expand support to Siemens and other proprietary data types.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 8 Epileptic events: From features extraction to classification by FABIEN FRISCOURT

Epilepsy is a frequent neurological condition characterized by episodes of abnormal highly synchronized activity of a population of neurons called epileptic seizure. In a kainate model of temporal lobe epilepsy, spontaneous recurrent seizures start to occur after a period of approximately 28 days. But, even if seizures start during this so-called chronic phase, some pathological events start to already occur few days after the induction of the epileptic condition. But even for a specialist eye, those events are difficult to identify. The goal of this Brainhack project is to dive into pre-marked pathological events and looking for signal features that can be used to build an automatic detection.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 9 Contributing to Connectome Mapper 3, an Open-Source processing pipeline software for mapping the human connectome at multiple scales by Jonathan Wirsich , Andrea McKavanagh and Isotta Rigoni

Connectome Mapper 3 is an open-source pipeline software, released as a BIDS App, that provides flexible pipelines for both diffusion MRI and functional MRI for mapping hierarchical multi-scale connectomes from multi-modal datasets. During the brainhack, any kind of contribution to this open-source project is welcome ✨. For instance, you can:   Create tutorial notebooks reading and analysing connectome files.  Join the task force that extends Connectome Mapper 3 to EEG. (See GitHub Pull Request)  Join the task force to create a BIDS-App to interface DSI studio with CMP3  Integration de FSL top up in the diffusion pipeline  Integration of multi-tissue multi-shell CSD (mrtrix3) into the diffusion pipeline  Start working on the generation of a processing summary report.  Propose your own.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 10 Improving the interpretability of the image quality metrics computed by MRIQC by Céline Provins and Mikkel Schöttner

MRIQC (Esteban et al. 2017) is a tool to help researchers perform quality control (QC) of their structural and functional MRI data. This tool not only outputs visual reports that can be manually rated but also automatically extracts a set of image quality metrics (IQMs). A question that comes often is : “How should we interpret the IQMs ? Which IQMs are more important ?”

In this project, we aspire to answer those questions with the help of the movement-related artefacts (MR-ART) dataset (Nárai et al. 2022). This dataset contains structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. Furthermore, the quality of the images has been rated by two expert raters. After running MRIQC on the dataset, the goal is to perform dimensionality reduction of the IQMs and to compare the IQMs to the manual ratings in the quest of improving the interpretability of the IQMs. Additionally, we can also get our hands dirty and rate the images ourselves. We would then compare our own manual quality ratings and the expert ones. Lastly, our method and findings will be gathered in a jupyter notebook that will contribute to the Nipreps QC book .

Our plan is not set in stone and we can adapt the analysis to the interests of the participants. Any ideas of analysis leveraging the manual quality ratings and IQMs extracted from this dataset is welcome !

References

• Esteban, Oscar, Daniel Birman, Marie Schaer, Oluwasanmi O. Koyejo, Russell A. Poldrack, and Krzysztof J. Gorgolewski. 2017. “MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites.” Edited by Boris C Bernhardt. PLOS ONE 12 (9): e0184661–e0184661. https://doi.org/10.1371/journal.pone.0184661.
• Nárai, Ádám, Petra Hermann, Tibor Auer, Péter Kemenczky, János Szalma, István Homolya, Eszter Somogyi, Pál Vakli, Béla Weiss, and Zoltán Vidnyánszky. 2022. “Movement-Related Artefacts (MR-ART) Dataset of Matched Motion-Corrupted and Clean Structural MRI Brain Scans.” Scientific Data 9 (1): 630. https://doi.org/10.1038/s41597-022-01694-8.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 11 LocalHIP: Pipeline for automated electrode localization in iEEG by Sebastien Tourbier

Spanning stereo intracranial electroencephalography (SEEG) procedure requires the implantation of electrodes in about 1% of the brain. This procedure is very rare and is used to characterize the epileptogenic zone (EZ) in patients with pharmaco-resistant epilepsy, who require surgery to stop their seizures.

Each electrode has between 5 to 18 recording sites, and the precise location of each electrode contact can allow clinicians to relate the SEEG pathological signal identify the EZ. The accurate localization of each SEEG electrode is indeed crucial to correctly define the EZ. However, this procedure is not trivial and very time-consuming, as it requires a good knowledge and understanding of the implantation procedure, and some expertise in brain anatomy.

To support clinicians in this task, a number of semi-automated and automated methods have been proposed during the last decade (See references). These techniques typically leverage the hyper- or hypo-intensity of the electrode contact imaged after electrode implantation and work with pre- and post- implantation imaging dataset that could consist of T1 Magnetic Resonance Imaging (MRI) scans and/or Computed Tomography (CT), depending on the imaging protocol followed by the site in which the method was developed. While the developed techniques have shown to be performant in its specific set of imaging data, no existing solution could adapt to the data availability to the best of our knowledge.

In this project, we would like to kick-off the development of a new community-driven and open-source automated processing pipeline tool for the localization of SEEG electrodes contacts. This tool would aim to provide a universal pipeline in the BIDS App framework with modular workflows which would adapt to data availability (MRI, CT), minimize user interactions, and maximize its re-usability, portability, and reproducibility.

References

• J.P. Princich, D. Wassermann, F. Latini, S. Oddo, A.O. Blenkmann, G. Seifer, S. Kochen. Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates. Front. Neurosci., 7 (2013), pp. 1-8, 10.3389/fnins.2013.00260.
• A.O. Hebb, A.V. Poliakov. Imaging of deep brain stimulation leads using extended hounsfield unit CT Stereotact. Funct. Neurosurg., 87 (2009), pp. 155-160, doi:10.1159/000209296.
• Arnulfo G, Narizzano M, Cardinale F, Fato MM, Palva JM. Automatic segmentation of deep intracerebral electrodes in computed tomography scans. BMC Bioinforma. 2015;16(1):99.
• Narizzano M., Arnulfo G., Ricci S., Toselli B., Canessa A., Tisdall M., Fato M. M., Cardinale F. “SEEG Assistant: a 3DSlicer extension to support epilepsy surgery” BMC Bioinformatics (2017) 10.1186/s12859-017-1545-8.
• IntrAnat Electrodes: A Free Database and Visualization Software for Intracranial Electroencephalographic Data Processed for Case and Group Studies (2018).
• EpiTools, A software suite for presurgical brain mapping in epilepsy: Intracerebral EEG, Journal of Neuroscience Methods, 2018.

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences

Project 12 Diversity of piriform cortex neuron identity by ferreira clothilde

Our project aims to understand the development of neurons forming the piriform cortex in mammals. The piriform is a structure and is involved in the perception and integration of odour information in the brain. This structure is part of the so-called paleo-cortex, a part of the brain that appeared earlier in evolution compared with the Neocortex that integrates inputs from the other sensory modalities, such as touch or vision. As little information is available about the neurons populating this brain region, the goal of our study is to characterize the neuronal diversity and identity composing it. In order to produce a multidimensional description of these cells, we carried out single cell transcriptomics alongside with PatchSeq, a technique that allows to retrieve electrophysiological features, morphology and transcriptomics at a single cell level. With this technique, we collected over 100 neurons for which we successfully obtained electrophysiological recording by whole cell patch-clamp, morphology by diffusion of biocytin and transcriptomic by nuclei aspiration through the patch pipet for sequencing. From the morphological information we have imaged all neurons and their morphology was reconstructed in 3D with Imaris software and we would highly benefit from inputs on how to unbiasedly classify the morphology types present in our dataset. The final aim is to integrate all the 3 modalities into a single dataset and create links between electrophysiology, morphology and gene expression. We are looking forward to collaborate

Project skills

At least one of these preferred, but contributing to general ideas / discussion is always welcome!

Programming

Terminal / Git

Machine Learning

MRI processing

EEG processing

Virtual Reality

Neurosciences