brainhackorg / global2019 Goto Github PK

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Led by: Sylvain Takerkart
Project Description

Finalize the integration of an extension of nilearn that would allow performing cortex-based searchlight decoding in nilearn.
Skills required to participate

What different types of people could contribute?
Links to resources

(Optional)

[github's page](https://github.com/SylvainTakerkart)
[nilearn pull request](https://github.com/nilearn/nilearn/pull/1959)

Illustration

(Optional)
Link to a beautiful picture

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https://brainhack.ch/past_events/opengeneva2019/index.html#bmodel

Leader:
Manik Bhattacharjee and Victor Ferat

PlasticBrain is a 3D-printed brain built from an MRI. During the hackathon we would like to display realtime brain activity as recorded with an EEG headset onto it (e.g. power in alpha/beta/gamma frequency bands). The goal is to visualize functional activity so that it can be used in a biofeedback context for research and clinical applications, and for teaching.

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https://framagit.org/brainhack_marseille/ref2019/-/issues/9

Led by: Bjørg Kilavik ([email protected]), ...
Project Description

Discuss adequate existing analysis methods and tools, and if necessary work towards establishing new tools (in matlab or python) to quantify the dynamics of individual burst/spindle events of LFP beta oscillations across macaque motor cortical layers.

Example scientific questions that can be addressed once such tools are identified/developed:

To which extent do individual beta burst events have the same cortical depth of origin?
What is the spread across cortical depth of individual bursts?
Do bursts 'translate' or spread across cortical depth (laminar contacts), between the start and end of an individual burst?
What about phase consistency across cortical depth for each oscillation cycle, from the start to the end of each burst?

Skills required to participate

Experience in analysis of electrophysiological data, or in analysis techniques used in other fields that can be translated to the electrophysiological domain
Illustration

NiceFigureHackathon

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by Giulia Bommarito, Louis Albert and Michael Dayan

Multiple sclerosis (MS) is a disease characterized by lesions in the brain, particularly visible on MRI data. The gold standard to mark these lesions still consists in looking at MRI slices one by one and manually draw/color the lesions, leading to unnatural shapes when seen in 3D. This is concerning as the resulting "lesion masks" are often used to characterize the disease, including in drug clinical trials. This project aims at creating a virtual reality tool to draw MS lesions directly in 3D, for a chance to possibly improving the current gold standard in MS lesion segmentation!

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Source: https://brainhack-vienna.github.io/

In this project, we work on a bio-mechanically constrained image-based model of human fetal brain development and try to relate the cortical expansion to other evolutionary of developmental processes.

Contact: Ernst Schwartz

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https://framagit.org/brainhack_marseille/ref2019/-/issues/11

Led by: Bastien Cagna, Bruno Giordano, Sylvain Takerkart, Pascal Belin
Project Description

Develop a searchlight Representation Similarity Analyses tool in python based on the Nilearn package.
Skills required to participate

Python, Statistics, RSA

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https://brainhackto.github.io/Global-Toronto-11-2019/projects.html

Name: Leon French

Contact: [email protected]

Institution/Company: KCNI, CAMH

Project Description: Advances in microfluidics, RNA sequencing and barcoding have enabled transcriptomic profiling of thousands of individual cells. These methods have been particularly helpful in understanding distinct patterns of cell type-specific gene expression. In addition to transcriptomic based cellular profiling, morphological and physiological measures are also used to discriminate cell-types. This project will integrate images (20x) of cells with single-cell gene expression to better understand relationships between the molecular and cellular scales. The source data is derived from the 3,005 cells assayed in the Zeisel et al. (2015) single cell study of the mouse CA1 and S1 brain regions. This project will extend previous work that used image processing and computer vision techniques to automatically extract image and cell morphology features by Minh An Ho and Leon French. We will intersect these features with transcriptomic data from the same cells, and specifically, previous approaches for identifying multi-cell contamination in single-cell RNA-seq data developed by Shreejoy Tripathy. With image and expression features for each cell, we will test if contamination of other cell-types can be detected from the images. Our work will also determine and characterize which genes are associated with image features and test if they are also correlated with electrophysiological properties.

Goals:

Tools Used: pandas, tidyverse

Areas of Interest: Machine Learning;Statistical Analysis;Visualization;Neuroimaging;Genomics

GitHub Link: https://github.com/leonfrench/BrainHack2019Project

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https://framagit.org/brainhack_marseille/ref2019/-/issues/5

Led by: Etienne Combrisson, [email protected]
Project Description

Visbrain is an open-source python package for plotting brain data. It relies on VisPy, a package that provide efficient GPU based plotting. VisPy recently added the support of embedded plot inside jupyter notebook / lab. The aim of the project is to also brings the Jupyter support to Visbrain and be able to plot brain data inside it and allow efficient real-time interactions.

In addition, the support for custom colormaps / colorbars is also required (see #50)
Skills required to participate

Object oriented Python programming
Jupyter notebooks / lab enthousiame

Links to resources

[Documentation](http://visbrain.org/)
[Github repo](https://github.com/EtienneCmb/visbrain)
[Combrisson et al. 2019](https://www.frontiersin.org/articles/10.3389/fninf.2019.00014/full)

Illustration

Picture from the paper

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Source: https://brainhack.irisa.fr/#projects

With the ever accelerating development of augmented reality and artifical intelligence technologies, we might be tempted to think that matrix like scenarios - where everything we feel and know is simulated - could become a reality.

This conceivable future might be attracting to some people, yet it is easy to see the risks and limits of such paradigm.

Numerous intelectuals (Alain Damasio, Eric Sadin, ...) question progress and technology and propose to collectively redefine the goals and trajectory of human development.

From this perspective, Diminished Reality aims at utilizing advanced technologies in a subversive fashion to promote alternative and low-tech experiences.

The project consists in a virtual reality headset coupled with a stereo-vision camera and basic image processing algorithms designed to immerge the user in an quantized, voxel based reality.

Contact: Arthur Masson

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Intracranial aneurysm
Context and issues ⮟
Objectives and expected outcomes

BULLE aims to design and implement an atlas-based neuroimaging processing workflow for automatic labelling of the cerebrovascular trunk, also known as the circle of Willis. These approaches, known as atlas-based segmentation, are based on the registration between an anatomical atlas and the patient images, and the subsequent transfer of annotations from the atlas to the images. We expect to gather experts in 3D MRI registration and vascular imaging. Based on their expertise we will collaboratively develop a reusable Jupyter notebook dedicated to the automatic annotation of cerebral vessels.

Contact: Alban Gaignard Perrine Paul Gilloteaux Vincent L’Allinec Romain Bourcier

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fNIRS

Functional Near InfraRed Spectroscopy (fNIRS) is a novel technique that allows brain mapping and hemodynamic response registration in a rather cheap and portable manner. The idea of this project is to go through the basics of data acquisition and processing for different kinds of motor and cognitive tasks, with the help of a recently bought NIRx device and software developed in Matlab and LabView.

Contact: Héctor García

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Neurofeedback prediction

A new kind of data is available : EEG-fMRI Neurofeedback scores. Neurofeedback approaches (NF) provide real-time feedback to a subject about its brain activity and help him or her perform a given task. Brain activity features are extracted, online, from non-invasive modalities such as EEG or fMRI for example. Here, EEG and fMRI signals were acquired simultameously, and subjects recieved, in real time, NF informations extracted from EEG and fMRI. The goal of this project, is to learn EEG patterns in order to predict NF scores coming from fMRI using EEG signals only, as the use of fMRI is expensive and time consuming.

Contact: Claire Cury

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Source:
https://brainhack-dallas.github.io/mini-brainhack-utd/#projects

Matt Kmiecik
[email protected]

Goal: Explore applications of machine learning to electrophysiological data

Description: Two participant groups (controls vs. individuals with alcoholism) were shown images that were either identical or different while their scalp EEG was recorded. This brainhack project will explore ways to visualize event-related potentials (ERPs) and identify features within electrophysiological signals that differentiate how alcoholic from non-alcoholic individuals identify similarities and differences in their visual field.

Tools: R, GitHub, EEGLAB, MATLAB

GitHub:
https://github.com/mkmiecik14/ml-eeg

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https://framagit.org/brainhack_marseille/ref2019/-/issues/6

Led by: Régis Trapeau, Bastien Cagna
Project Description

The goal would be to further develop this project created during the PRIME-DE meeting in London. PRIMate-Ressource Exchange aims to provide an overview of the main difficulties and curate a collection of solutions that currently exist within the broader NHP-MRI community for specific processing steps that are commonly performed on NHP MRI data.
Skills required to participate

Experience, feedback or questions on PNH MRI processing.
Links to resources

https://prime-re.github.io/
[Git repo](https://github.com/PRIME-RE/prime-re.github.io)

Illustration

logo

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https://framagit.org/brainhack_marseille/ref2019/-/issues/3

Led by: David Meunier ([email protected]), Bastien Cagna ([email protected])
Project Description

The aim of this project is to create a python package that provide all the tools needed to preprocess anatomical data of non humain primate. It also aim to provide a standard pipeline for different species, starting with macaques.
Skills required to participate

MRI preprocessing, Python
Links to resources

[Git repository](https://framagit.org/mars-hackat2019/anat-mri-pipeline/macapype)

Illustration

Link to picture

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Ju-Chi Yu
[email protected]

Goal: Create chapter on PCA and CA, with their inference analysis

Description: The goal of this book is to provide a vignette about the basics and the advanced applications of multivariate analysis. We are hoping to use this book to provide researchers with guidence on applying multivariate techniques (to neuroimaging data). The current aim is to (1) start with simple toy examples to illustrate principal component analysis (PCA) and singular value decomposition (SVD), and also to (2) generate a template for other methods that we hope to include in the future.

Tools: R (bookdown), GitHub

GitHub:
https://github.com/juchiyu/MultivarBook.git

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https://brainhack.ch/past_events/opengeneva2019/index.html#TMS_tracto

Leader:

Takuya Morishita, Philipp Koch and Olivier Reynaud

Transcranial magnetic stimulation (TMS) is used to study electrophysiological fundaments of brain function. Besides this TMS has the outstanding opportunity of non-invasive neuromodulation of the cortex, a promising treatment concept for multiple diseases like e.g., stroke or depression. Hereby, specific brain areas are targeted using T1 weighted MR images of the subject guided by anatomical landmarks. Still, this does not consider individual underlying anatomical and functional features including e.g., white matter connections. To move towards personalized precision medicine in neuromodulation, incorporating individual patient characteristics in treatment planning is inevitable. Taken toegther, this project aims to precise the targeting of brain areas for TMS using individual structural connectivity estimates.

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Source: https://brainhack.irisa.fr/#projects

Reproducibility

With the ever accelerating development of augmented reality and artifical intelligence technologies, we might be tempted to think that matrix like scenarios - where everything we feel and know is simulated - could become a reality.

The goal of the project is to examine, edit and organize the code and content of a study on analytic variability in task-based fMRI, to describe the data structures, high-level functions, and to display the results in a Jupyter Notebook, in order to facilitate its reproducibility.

In this study, we try to highlight the existing analytic variability in task-based fMRI pipelines, by comparing groups of subjects data from the Human Connectome Project which we have preprocessed and analyzed with different pipelines.

Contact: Xavier Rolland

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Source: https://brainhack.ch/past_events/opengeneva2019/index.html#BIDS_supermri

Sebastien Tourbier

The essence of this project is to develop the next generation of the open-source Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK), a set of C++ image processing tools necessary to perform motion-robust super-resolution fetal MRI reconstruction. This new version will integrate all new advances in the neuroimaging field over the past three years with the advent of:

the Brain Imaging Data Structure ([BIDS](http://bids.neuroimaging.io/)), a standard to organize and describe neuroimaging data,
and the [BIDS App](https://bids-apps.neuroimaging.io/) framework, which promotes reproducibility and portability. all state-of-the-art solutions for enhanced portability, reusability, reproducibility and replicability in neuroimaging.

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https://brainhackto.github.io/Global-Toronto-11-2019/projects.html

Name: Aras Kayvanrad

Contact: [email protected]

Institution/Company: Rotman Research Institute

Project Description: This project aims to create flexible and scalable fMRI preprocessing pipelines. fMRI Pipeline uses an object-oriented framework to create and run preprocessing pipelines. In this framework, each pipeline consists of a number of preprocessing steps, where each proprocessing step is an object of class PreprocessingStep. This design allows to construct versatile pipelines in which different preprocessing steps can be used in any desired order.

Goals:

Tools Used: scipy, nibabel, nipipe, pandas, fsl, afni, freesurfer

Areas of Interest: Containerization/Pipelining;Quality Assurance Tools;Statistical Analysis;Visualization;Neuroimaging

GitHub Link: https://github.com/kayvanrad/fmri_pipeline

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https://framagit.org/brainhack_marseille/ref2019/-/issues/13

Led by: Hmamouche Youssef, Magalie Ochs, Laurent Prévot, Thierry Chaminade
Project Description

This is an under developement project which aims to provide a system for hemodynamic brain activity prediction based on behavioral features. The behavioral features represent verbal and non-verbal variables extracted during an experience of human-human and human-robot conversations conducted on several subjects. The goal is to detect the behavioral features that are responsible for the activation of each brain area, by means of prediction. A feature selection step is performed to select the input variables for the prediction of brain activity, then the most relevant input features are those how lead to the best prediction score.
Skills required to participate

fMRI data, conversations signals processing (video, audio and eyetracking data), Machine learning, Python.
Links

https://github.com/Hmamouche/PhysSocial/

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https://brainhackto.github.io/Global-Toronto-11-2019/projects.html

Name: Jenny Rieck & Derek Beaton

Contact: [email protected]

Institution/Company: Rotman Research Institute

Project Description: C-MARINeR is a focused sub-project MARINeR: Multivariate Analysis and Resampling Inference for Neuroimaging in R. The "C" stands generally for connectivity, but specifically and statistically: covariance or correlation. The C-MARINeR project aims to develop and distribute an R package and ShinyApp. Together, R + Shiny allows for ease of use and, hopefully, simpler exploration of such complex data, and quicker adoption of the techniques. CovSTATIS is the base method in C-MARINeR. CovSTATIS is effectively a multi-table PCA designed for covariance matrices. CovSTATIS allows for multiple connectivity (correlation or more generally covariance) matrices to be integrated into a single analysis. CovSTATIS produces component (a.k.a. factor) maps with respect to the compromise matrix (weighted average), and then projects each individual matrix back onto the components. K+1CovSTATIS is a novel extension of CovSTATIS that allows us to use a "target" or reference matrix. For example, a theoretical resting state structure (a la Yeo/Schaffer maps). K+1CovSTATIS also produces component (a.k.a. factor) maps with respect to the compromise matrix (weighted average), except the compromise matrix is no longer a weighted average of all matrices, rather, it is a weighted average of all matrices with respect to a "target" matrix. Then each of those matrices are projected back onto the components.

Goals: Our primary goal is to make a small package and ShinyApp to perform the same types of analyses we use for integrating and analyzing multiple connectivity matrices (across tasks, individuals, and groups). We want to make CovSTATIS and similar methods easily accessible. See our Github page for task lists, tools, and how to participate

Tools Used: Primary: R, various R packages, git/github, RStudio, Shiny. Secondary: HTML, CSS, Possibly Rcpp/RcppEigen/RcppArmadillo

Areas of Interest: Machine Learning;Statistical Analysis;Visualization;Neuroimaging;Cognitive Neuroscience

GitHub Link: https://github.com/jennyrieck/C-MARINeR

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https://brainhackto.github.io/Global-Toronto-11-2019/projects.html

Name: Jordan DeKraker

Contact: [email protected]

Institution/Company: University of Western Ontario

Project Description: Our lab has developed a pipeline to computationally unfold the human hippocampus, which is helpful for mapping its morphological properties, delineating its subfields, as well as for visualization. We're still working on full automation of this pipeline using deep learning (CNN via NiftyNet) and would be interested to receive feedback or suggestions. We're also looking for collaborators who would be interested in extending this pipeline to new datasets, imaging modalities, or perhaps even different species.

Goals: Looking for feedback, suggestions, or collaborators interested in extending our pipeline to new data

Tools Used: Matlab, NiftyNet, ANTs, BIDS

Areas of Interest: Open data initiatives (e.g: standardization, file formats, open datasets);Machine Learning;Visualization;Neuroimaging;Cognitive Neuroscience

GitHub Link: https://github.com/jordandekraker/Hippunfolding

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https://brainhack-dallas.github.io/mini-brainhack-utd/#projects

Ekarin Pongpipat
[email protected]

Goal: Create a book on creating a more informative APA statement using bootstrap and permutation testing

Description: Our current book uses a bare minimum APA statement. However, we would prefer to not promote this and would prefer to promote a “Gold” standard APA statement. We believe that the APA statement should include adjusted R-squared as a measure of effect size, its confidence interval (CI), bootstraped CI of the estimates, and permuted p-values. Ideally, in the book, we would write a chapter on each boostrapping, permutation, and then a chapter on APA Gold Standard

Tools: R (bookdown), GitHub

GitHub:
https://github.com/epongpipat/boot-perm-apa

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Source: https://brainhack-vienna.github.io/

Connectomics have been used so far to look for quantifying global and local differences in the functional or structural brain networks. Very few studies have used connectomes to investigate the spreading of misfolded proteins which is at the basis of Parkinson’s (PD) and Alzheimer’s disease (AD). It is believed that diseases as AD and PD are spread by misfolded proteins or agents which moves along brain connections (axons and dendrides of the neurons) starting from specific regions to others. For instance, AD has a progression of tau pathology consistently beginning in the entorhinal cortex, the locus coeruleus, and other nearby noradrenergic brainstem nuclei, before spreading to the rest of the limbic system as well as the cacingulate and retrosplenial cortices. While Parkinsons starts from the brainstem and spread to the neocortex. During previous studies we compared the developed tools in a novel manner validating real datasets (ADNI and PPMI). Now we want to try other methods for predicting tau deposit or atrophy. In particular, we want to simulate deposits/spreading of misfolded proteins proceeding via the brain’s anatomic connectivity network via Autoregressive models or anything proposed by you. We will use human data provided by the supervisors.

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Source: https://brainhack-vienna.github.io/

The aim of this project is to quantify shared patterns of functional connectivity across species, and use them for cortical surface alignment.

Contact: Karl-Heinz Nenning

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Source: https://brainhack.irisa.fr/#projects

Structural connectivity can be assessed using diffusion MRI. It relies on the idea that water trapped within axons or glial cells undergo restricted diffusion patterns that can be modelled, estimated and used to provide a mathematical representation of the brain microstructure, ultimately leading to a mapping of the structural connections in the brain. Lots of tools are available nowadays to perform this task, which is known as tractography. However, there is very little in the community about open-source softwares that allow users to perform sound statistical analyses of tractography data.

This project is an attempt to create such an open-source software from scratch. Given that the ultimate goal is to perform statistical analysis and data visualisation, it seems natural to lean towards R, which a programming language dedicated to statistical computing (https://www.r-project.org). The objective of the project will be:

1. To build the state of the art about already existing R packages for tractography data; 2. To brainstorm our new R package API, including the definition of relevant object classes and related methods for later uniform integration of statistical methods; 3. To start the package, including package creation, website for promoting it, GitHub repository, description, logo creation and so on; 4. To start implementing existing statistical methods for tractography data.

Contact: Aymeric Stamm

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Name: Yohan Yee

Contact: [email protected]

Institution/Company: Mouse Imaging Centre, Hospital for Sick Children; Medical Biophysics, University of Toronto

Project Description: As subjects of MRI experiments, both mice and humans have contributed to our understanding of the how brain develops, functions, and changes in disease states. Yet, our knowledge derived from both fields remains to be integrated and translated between species. With mouse neuroimaging becoming more popular, a tool to translate findings from the mouse to humans and vice versa could be valuable. In other words, if a treatment in a mouse causes brain region A to change in structure, can we predict which regions in the human brain will show a similar change, if at all? This project is essentially about building a function that maps a given coordinate in one species to (potentially multiple) coordinates in the other species, with the goal being that this mapping reflects some sort of similarity in the function and evolutionary origins of the pair of coordinates. The complex folding structure of the human cortex, compared to the flat mouse cortex, makes this anatomical registration challenging. Instead, I propose that we use spatial expression patterns of homologous genes as a proxy for common function and origin, and connect regions across species by similarities in their gene expression profiles. Spatial gene expression data for both species are available from the Allen Institute.

Goals: Produce a spatial mapping between the mouse brain and the human brain based on gene expression similarity, along with a tool to visualize this mapping and query associated genes

Tools Used: R and associated packages (tidyverse, RMINC, shiny); Python, pyminc

Areas of Interest: Visualization;Neuroimaging;Genomics;Systems Modelling

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Led by: Dipankar Bachar ([email protected])
Project Description

Single cell SplitSeq is a recent and cheap method to produce single cell data. But, as it is a new method, there exists not many tools and pipeline to de-multiplex and analyse the splitseq data. So, the aim of this brain hack project is to better understand the method of SpliSeq , to test the existing tools, to propose/create new algorithm and if possible to develop tools/pipeline in order to demultiplex/analyse Spliseq data.
Skills required to participate

Understanding basic Single Cell transcription method.
Illustration

Picture

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https://framagit.org/brainhack_marseille/ref2019/-/issues/12

Led by: Laurent PERRINET ([email protected]), ...
Project Description

We have developed a tool for the analysis of eye tracking movements @ https://github.com/invibe/ANEMO - this is mainly a 2-persons project and we need to torture it to make it something for which the world will be proud of us! :-)
Skills required to participate

People with an interest in eye movements, psychophysics and signal processing will be interested as it may guide the creation of similar tools for the analysis of experimental data. Goal is to get more validated parameters - and get more robust quantitative experimental results.
Links to resources

[GitHubRepo](https://github.com/invibe/ANEMO)
[Article](https://laurentperrinet.github.io/publication/pasturel-18-anemo/)

Illustration

an example result

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Source:
https://framagit.org/brainhack_marseille/ref2019/-/issues/14

Led by: Ruggero Basanisi
Project Description

The aim of the project is to create a python pipeline able to link BrainVISA, a neuroimaging software platform developed in Marseille, to MNE, that is currently one of the most used python tool to perform data analysis on MEG/EEG data. BrainVISA includes MarsAtlas, an anatomical atlas that divedes the brain in 82 cortical parcels and 14 subcortical parcels. The main objectives of this pipeline are:

storing data in a clean and functional database
perform cortical source space reconstruction
perform subcortical volume reconstruction
labelling based on MarsAtlas parcellation
visualization tools based on visbrain

Skills required to participate

Prerequisites No particular skills are required.
Links to resources

Links

[BrainVISA](http://brainvisa.info/web/index.html)
[MarsAtlas](https://meca-brain.org/software/marsatlas/)
[MNE](https://mne.tools/stable/index.html)
[visbrain](http://visbrain.org/index.html)

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https://brainhack.ch/past_events/opengeneva2019/index.html#epilepsy

Leader:
Ellie Shamshiri, Margherita Carboniand Maria Rubega

The goal of the project is two-fold: - to manipulate and display brain connectivity data (resolved in space, time and frequency) of epilepsy patients and normal controls acquired with electroencephalography (EEG) and functional MRI - to leverage these data to predict surgery outcome (success vs failure) of patients suffering from intractable epilepsy

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Source:
https://framagit.org/brainhack_marseille/ref2019/-/issues/8

Led by: Fred Barthelemy, Sylvain Takerkart
Project Description

The goal of this project is to define standards for data management and application of the FAIR principles to our data in the context of open science. We propose to organize the discussions / workgroups around 4 points:

The organization and the formatting of the data storage. Discuss about the structuration of the data in different projects, find shared points and limitations.
The creation of a digital lab book. Develop an informatics tool to enter the information about the experiment for future use in data management.
The creation of procedures for all aspects of animal experiment. In order to approach the standards of Good Laboratory Practices, discuss around the installation of common written protocols defining standards in housing, care and experiment. -The definition of a GUID for the animals. Determine how a unique identifier standard could be set for animals involved in experiments.

Skills required to participate
Illustration

think_fair

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WMQL

Diffusion tensor imaging (DTI) allows for the estimation and quantitative analysis of white matter tracts properties, for instance fiber tracts integrity. DTI has been used, for example, to characterize cerebrospinal tract (CST) properties from diffusion MRI in stroke patients, and identify biomarkers of motor recovery based on CST properties. In the case of stroke, a challenge is to robustly take in account the lesion when estimating fibers tracts.

In this project, we are interested in developing a workflow to automatically extract white matter bundles from diffusion MRI exploring a range of tools (ANIMA https://github.com/Inria-Visages/Anima-Public, WMQL https://tract-querier.readthedocs.io/en/latest/index.html, Freesurfer https://surfer.nmr.mgh.harvard.edu/) in order to robustly estimate CST properties. We will first design and test the workflow on healthy subject data, and eventually apply it to stroke patients’ images to verify how the lesion affects automatic CST estimation.

Contact: Giulia Lioi

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Source:
https://brainhack.ch/past_events/opengeneva2019/index.html#ML_tadpole

Leader:
Mazen Mahdi and Jonas Richiardi

The goal of this project is to rapidly develop and test algorithms to forecast disease evolution in Alzheimer's disease, using pre-extracted brain imaging markers from MRI (volume, thickness...), PET (metabolism, amyloid load...), Diffusion (diffusivity), liquid biomarkers, and clinical scores, provided by the TADPOLE challenge project.

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Source: https://brainhack.irisa.fr/#projects

Develop interfaces and workflows for Anima in Nipype

Nipype and Anima

Nipype looks like a great framework to define small processing bricks and automatically assemble them to form pipelines. At last year brainhack, we had time to define interfaces for the Anima open-source software. The idea this time is to go all the way to define a pipeline based on Anima interfaces and try to run it on a cluster on some toy data.

Contact: Olivier Commowick

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Source: https://brainhack-vienna.github.io/

Create a Python library for fast, easy and flexible conversion of data correspoding to biological neuronal networks to artificial neuronal networks in a ready-to-be-trained format.

Contact: Alexandros Goulas

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Source:
https://brainhack.ch/past_events/opengeneva2019/index.html#conn_viz

Leader:
Renaud Marquis

Current progress in neuroimaging allows to collect data from multiple imaging modalities and at multiple scales. While numerous software packages allow the processing of such data, visualization can become problematic with increasing number of dimensions. The goal of this project is to use D3.js, a JavaScript library relying on common web standards, to visualize complex brain connectivity from multiple imaging modalities (EEG, functional and diffusion MRI) at multiple scales in an interactive fashion to facilitate insights, data and knowledge sharing.

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Source: https://brainhack.ch/past_events/opengeneva2019/index.html#ML_FI

Raphael Liegois and Thomas Bolton

The way cerebral function shapes human behaviour remains largely unknown. In this project, we propose to explore if brain dynamics, evaluated from functional MRI measurements, can be used to predict a simple measure of fluid intelligence.