The Virtual Brain (TVB) is a scientific simulation platform that provides all means to generate, manipulate and visualize connectivity and network dynamics of the brain networks. Researchers in Computational Neuroscience use brain network models to understand the dynamic behavior of the healthy and diseased brain, as measured by various neuroimaging techniques such as fMRI, EEG, and MEG. TVB is currently the only neuroinformatics project providing a platform for researchers to work together on modeling studies using brain network models with realistic connectivity. The modeling of the simulations requires datasets generally hosted on data storage platforms like Zenodo, OpenSourceBrain, EBRAINS, etc. One has to download the dataset manually, unzip it and then use it inside the web GUI. Instead of manually downloading and unzipping the data, the project's goal is to have a dedicated framework to manage the downloading and unzipping the dataset from remote sources. Right now, we are focusing on the Zenodo platform, but there are other platforms like OpenSourceBrain and EBRAINS for which we would like to extend the downloading functionality.

The feature space and the classification results produced by the platform are stored in several separate files. The idea is that the types and values of image features and classification outcomes would be stored in an SQLite database for cross-comparisons between sessions. The candidate is required to use the SQLite database engine in order to integrate it with the GUI of ASP/IJ.

This working group serves users and use cases of MATLAB (and Simulink) for cellular-scale neuroscience, including electrical live recordings (e.g., neural probes), optical live recordings (e.g., calcium imaging), and anatomical imaging (e.g., connectomics). The working group is attuned to the associated data streams (e.g., behavioral data), computational methods (e.g., spike sorting, cellular source separation, neural simulation), and research resources/infrastructure (e.g., data archives, data standards). Cellular-scale neuroscience seeks to understand the function and structure of the central & peripheral nervous system at the levels of neurons & glia, including how they connect and interact as modules/networks at larger scales (aka systems neuroscience) and across select modules/networks distributed across the brain/body (the “mesoscale”).

This Working Group aims to develop tools for the ARTEM-IS standard for electrophysiological methods reporting. ARTEM-IS stands for an Agreed Reporting Template for EEG Methodology - International Standard. Accurate reporting is critical for transparent, reproducible, replicable research in the scientific record, and allows advanced forms of meta-science to be conducted. Systematic reviews of EEG literature have highlighted specific and actionable weaknesses in the way that methods are typically reported - in particular, a lack of specificity about individual methodological decisions in the methods sections of journal articles. This challenge can be addressed by designing tools that facilitate detailed methodology documentation, where the structure of the tool helps to reduce errors, ambiguities and omissions in reporting. This outcome will be achieved more effectively through the collaborative action of EEG stakeholders, so that the tools are designed to maximise ease of use, clarity and specificity.

The NIDM Working Group is further developing the NeuroImaging Data Model (NIDM) originally conceived by the INCF Neuro Imaging DAta SHaring (NIDASH) Task Force and the BIRN Derived Data Working Group (DDWG).

NIDM describes an extension of the W3C PROV standard for the domain of human brain mapping and, in recent years, has been working closely with the Brain Imaging Data Structure (BIDS).

Currently this Working Group has two active subgroups focusing on:

• NIDM-experiment: Interlinkage of existing BIDS datasets chaired by David Keator
• BIDS-Prov: Representing neuroimaging provenance (follow up of NIDM-Worklows) chaired by Camille Maumet and Satrajit Ghosh

Previously this group developed NIDM-Results, a software-agnostic model to represent results of mass univariate studies (such as fMRI studies).

This working group will turn the COBIDAS recommendations and guidelines into a series of checklists hosted on a website, to let users report information faster and with more detail. 

The machine-readable output can form the foundation of a Methods section. This will enhance adoption and use of emerging neuroimaging standards such as BIDS and fMRIprep, facilitate data sharing and pre-registration, and help with peer-review.

The groups envisions that using checklists to report methods and results can:

• Provide comprehensive human and machine readable descriptions of the data  collection and analysis pipelines to reduce inefficiencies and frictions in reuse
• Facilitate the creation and preparation of pre-registration and registered reports, and help users think about and create pipelines before they start collecting data
• Help make peer-review more objective: by supplying an app to check pipelines
• Facilitate systematic literature reviews and meta-analyses 
• Facilitate data sharing 

The implementation of this project should remain flexible enough to accommodate the inclusion of new items in the checklist as new methods mature, and reusable to enable easily setting up a checklist website for a different field.

This working group is joint between OCNS and INCF. The group focuses on evaluating and testing computational neuroscience tools; finding them, testing them, learning how they work, and informing developers of issues to ensure that these tools remain in good shape by having communities looking after them. Since many members of the WG are themselves tool developers, we will also learn from each other and will work towards improving interoperability between related tools.

When The Virtual Brain(TVB) runs simulations on cortical surfaces, we need to be able and compute geodesic distances (distance on the surface) instead of the trivial Euclidean distances. For this calculus, we have a small C++ library, which had become outdated. We need to:

• start with an analysis done by the student if the current implementation should be reused and fixed, or completely replaced, then

• proceed with the fix/replacement as concluded at the previous step.

  • If we are to fix the current implementation, we need to fix the 6 issues reported on Github during this project, and also

  • make sure the library compiles correctly with the latest version of clang.

• we need to have unit-test written for the main flows as well as for some common exceptions

• the unit-tests should run automatically by integration in our CI Jenkins system

• at the end of this project, also tvb-gdist packages on Pypi and conda-forge should be updated.

The current implementation is hosted here:

• https://github.com/the-virtual-brain/tvb-geodesic

• https://github.com/conda-forge/tvb-gdist-feedstock

The SciUnit framework was developed to help researchers create unit tests for scientific models. Currently, unit tests exist for models of single neurons and small networks thereof. However, unit tests for models concerned with large-scale brain network dynamics, such as meso-scale, mean-field descriptions and corticothalamic circuit models have not been developed yet.

Primary Goals

● To create a basic GUI interface for the reconstruction pipeline which gives the ability to users to provide input data, choose configurations, identify the outputs, and check logs in case of any problem which occurs during the whole process.

● To integrate the GUI with our Pegasus workflow engine for automation, fault-tolerance and debugging and to provide the job status and execution statistics.

Secondary Goals

● Implement GUI automated testing.

● To implement more functionality for the GUI at a higher level of abstraction.