Periodic Reporting for period 1 - eBRAIN-Health (eBRAIN-Health - Actionable Multilevel Health Data)
Berichtszeitraum: 2022-07-01 bis 2023-06-30
eBRAIN-Health main objective is to deliver a distributed research platform for modeling and simulating complex neurobiological phenomena of human brain function and dysfunction in a data protection compliant environment. The goal is to provide thousands of multilevel virtual brains - the eBRAIN-Health “twins" from patients and healthy human controls for research and innovation.Brain data from multiple sources will be pre-processed and annotated with a common data model - such that they all will relate to common spatial and temporal reference frameworks. The platform thus will offer a next generation clinical research infrastructure - compliant with the EU General Data Protection Regulations (GDPR) and creates an open yet protected space for groundbreaking digital health innovation by the research infrastructure communities comprising academia and the private sector. eBRAIN-Health utilizes neurodegenerative disorder data as a demonstration case. The project will integrate large, complex datasets and computational simulations from diverse academic medical research projects to advance our understanding of the underlying causes of these diseases, enabling identification of new potential treatment options. eBRAIN-Health systems can subsequently be applied as a benchmark/model for many other disease areas. The eBRAIN-Health infrastructure is designed to respect the sensitive nature of medical data, while making these data accessible for further research. Combined with access to high-performance computational power, the systems deployed in the project will allow generation of meaningful algorithms, models and simulations. The platform will be attractive not only to the academic research community but to the private sector. Start-ups and established drug developers can use the infrastructure for early digital/in-silico screening of new approaches, stress-testing them against models of real-world data. This can accelerate development of successful treatments or save resources through early identification of failures. From a disease management perspective, the predictive power provided by these models and simulations will help improve clinical care, e.g. by identifying which intervention is best suited for a particular individual.
1. Limited data findability, access and usability:
· Data Findability and Access: Systematic data-driven approaches are hampered because patient-level data are difficult to discover and access or, if the (raw) data are known to exist, they are entirely inaccessible.
· Data Models: Metadata models of different cohorts and/or data modalities use different terminologies and refer to differing definitions and concepts, limiting usability beyond individual projects.
2. Limited analytical skills and lack of linkage with knowledge bases beyond individual projects:
· Data Processing: Individual scientists are often limited in data modalities or features they can analyze from the available raw data due to limitations in expertise and in access to computational power.
· Fragmentation: Existing biological and medical knowledge and data are distributed (e.g. in articles, knowledge bases). Seeking information is time-consuming and retrieving relevant data is often not feasible.
· Theory: Complex mathematical models and simulations are required to infer biophysical principles that transcend single levels and can link multilevel data.
3. Lack of access to HPC and robust, scalable GDPR compliant infrastructure:
· Industry-grade service: Sustainable, scalable, reliable quality assured data, tools and infrastructure
· Data Protection: Technical and organizational means are necessary to provide data protection by design and by default. There is a need for digital Research Infrastructures that provide high-performance computing, storage and services for running workflows, as well as adequate technical security of services and storage.
4. Addressing ethical and social challenges linked to the development and use of digital twins for dementia, including:
· Representation, bias, consent, inclusivity of research and promotion of diversity
· Legal capacity, shared and supported decision-making, communication, trustworthiness, and trust.