Metadata Schema of Common Data Elements for Traumatic Brain Injury working group aims to develop a metadata schema for federated data analysis on Traumatic Brain Injury (TBI) datasets through the Medical Informatics Platform (MIP). It is based on data elements from NINDS CDE catalog.

The metadata schema has the following concept roots:

• Demographics - Demographic information
• Neurological assessment - Glasgow Coma Scale and pupil reactivity scores
• Imaging - Structured reporting on imaging data including cisternal compression, epidural hematoma, midline shift, marshall classification
• Second insults - Hypoxic and hypotensive episodes
• Outcomes - Glasgow Outcome Scale Extended score including mortality
• Laboratory tests - Values of glucose and hemoglobin level

Countries in Asia Pacific face a major societal burden due to decline in mental abilities and health of the rapidly aging population. The technology solutions to this challenge will come from the intersection of aging neuroscience, cognitive engineering and information sciences. We call this field neuroinformatics for aging, where the management and analysis of data will be combined with neurorobotics, neuropsychology, clinical neurology and social neuroscience to delay the decline or improve the mental abilities among the aging. The Working Group hosts a scientific symposium, an annual meeting between neuroscientists, neuroinformaticians, roboticists, neuropsychiatrists, neurologists and neurosurgeons. Forging international collaborations will be the primary goal at these meetings, as the means to (1) find and match expertise to project needs of each member group/country (2) promote the dissemination of tools, analysis techniques and knowledge of neuroinformatics (3) raise the visibility of neuroinformatics to researchers and clinicians from each member country through important use-cases in aging.

This Working Group coordinates common efforts for defining and describing cell types across neuroscience, to reduce duplicate efforts and to improve interoperability and reuse of cell type-specific data collected across groups. The key focus is facilitating interactions between groups collecting large-scale datasets used to define novel cell types (such as single-cell transcriptomics) and ontologists and tool developers who have considerable experience curating cell type-specific data, with the goal to reduce duplicate efforts and to improve interoperability and reuse of cell type-specific data collected across groups.

The coordination of this effort has now moved to the Allen Institute, as part of their work on cell nomenclatures which began with the Cell Type Ontology Workshop held at the Allen Institute in Seattle in 2019. The outcome of the workshop was a framework for developing nomenclature:  the Common Cell Type Nomenclature, or CCN.  It was initially applied to brain cells and types, and intended to encompass existing naming strategies used in publications across diverse research teams, to allow tracking of many different taxonomies across diverse areas of bioscience. The CCN is described in this 2020 paper in eLife. To learn more about the current activities of the CCN, visit: https://portal.brain-map.org/explore/classes/nomenclature.

This Working Group coordinates community efforts for the development of open, use case- driven and shared validatable data models (schemas, vocabularies) to enable the FAIR principles (Findable, Accessible, Interoperable and Reusable) for basic, computational and clinical neuroscience (meta)data. It uses provenance information to define the context from which scientific data was generated, including the type of data, its significance, quality and potential for integration and reuse. Data models have been developed thus far for  electrophysiology, neuron morphology, brain atlases, and computational modelling. Future developments could include brain imaging, transcriptomic and clinical form data, as determined by the Working Group membership and community interests.

The following projects have adopted Neuroshapes:

• Blue Brain Project
• Human Brain Project
• Krembil Centre for Neuroinformatics

This Working Group formed as the result of a merger of several INCF Working Groups working in the areas of neuroimaging and reproducibility. The group has several separate projects that all have reproducibility in neuroimaging as an overarching theme, specifically focusing on data sharing, data management, and data description. The working group is composed of 3 task forces: Brain Imaging Informatics (NIDASH), Brain Imaging Data Structure (BIDS), Neuroimaging Data Model (NIDM).

The motivation for this Working Group is the ongoing development of complex data-driven models of neuronal networks, as well as the emergence of general purpose software packages and standardised formats to make it easier to build, specify and share such networks. To encourage researchers to use these common tools and formats (and ultimately create and disseminate higher quality models) the group gathers together developers creating these packages to share code and ideas, to encourage interoperability and have a forum for users and developers to find out about the state of the art and to contribute to a better ecosystem of tools and standards in this area.

This Working Group develops standards and best practices for quality control (QC) of neuroimaging data, including standardized protocols, easy to use tools and comprehensive manuals. Assessing the quality of neuroimaging data requires human visual inspection. Given the complex nature, diverse presentations, and three-dimensional anatomy of image volumes, this requires inspection in all the three planes and multiple cross-sections through each volume. Often, looking at raw data is not sufficient, but statistical measurements (e.g. across space or time) can greatly assist in identifying the artefacts or rating their severity. For proper QC, multiple types of visualizations and metrics often need to be taken into account, which is time-consuming and subjected to large variabilities.With sample sizes and number of modalities both increasing, there is a great need for developing appropriate QC annotation protocols and corresponding assistive tools.