Our vision for the VirtualBrainCloud is a product for personalized medicine that improves the quality of life of EU citizens by enabling targeted prevention, early diagnosis, disease progression prognosis, individual treatment plans and development of novel therapies for neurodegenerative diseases with focus on Alzheimer’s and Parkinson’s disease. We believe that this vision will be reached by implementing an European cloud-based platform that not only connects two critical streams of biomedical research, systems biology and computational neuroscience, but that also connects clinics, researchers, patients and students.
The annual worldwide cost of Alzheimer’s dementia was 777.81 billion Euro in 2015. This number could rise to 7.41 trillion Euro in 2050. Early diagnosis would save up to $7.9 trillion in medical and care costs by 2050 in the US alone. However, the emergent pathology is highly variable across people, necessitating individualized diagnostics and interventions. The VirtualBrainCloud addresses this by bridging the gap between computational neuroscience and subcellular systems biology, integrating both research streams into a unifying computational model that supports personalized diagnostics and treatments in NDD. The VirtualBrainCloud not only integrates existing software tools, it also merges the efforts of two big EU initiatives, namely The Virtual Brain large scale simulation platform of the EU Flagship Human Brain Project and IMI- AETIONOMY initiative (European prevention of Alzheimer’s dementia consortium).
VirtualBrainCloud will develop and validate a decision support system that provides access to high quality multi-disciplinary data for clinical practice. The result will be a cloud-based brain simulation platform to support personalized diagnostics and treatments in NDD. The resulting software is tailored to the individual, and bridges multiple scales to identify key mechanisms that predict NDD progression. The interdisciplinary VirtualBrainCloud consortium will develop robust solutions for legal and ethical matters by interacting with other projects and initiatives such as HumanBrainProject, Alzheimer’s Europe patient organizations and ELIXIR, an organization that manages and safeguards EU research data.
The successful implementation of this project will have an impact on the development of future disruptive iHealth solutions, which will offer prevention and personalized healthcare solutions and will contribute to reduce the current overburden of the healthcare system. Realizing VirtualBrainCloud will accelerate this necessary transformation towards a more user centered health care system and aims to contribute to the overall goal of improving healthcare for the benefit of end users, bringing down costs by promoting prevention and personalized ‘citizen-centric’ rehabilitation. This could in the long-term contribute towards a more sustainable European healthcare system through reduction of avoidable disease burden while significant improving the quality of care.
Our Objective is to develop and validate VirtualBrainCloud, a dedicated cloud-based environment that leverages the potential of big data and high-performance computing (HPC) for personalized prevention and treatment of neurodegenerative diseases (NDD).
The methodological foundation of the project rests on three pillars: clinical expertise, computational, and biomedicine and integration.
Clinical experts in the field of NDD provide clinical data and expertise to ensure the clinical relevance of the project at all stages. Clinicians provide the consortium with high-quality multi-omics and imaging data from large dementia patient cohorts. They also provide expertise in characterization of AD and PD patients, treatment options, best practices, and prevention options. Most importantly, the major focus of ‘Clinical data & Disease progression models’ includes a comparative assessment regarding the predictive performance, robustness, precision, clinical feasibility and level of automation of existing biomarkers, laboratory tests, clinical information, conventional measures, advanced neuroimaging, machine learning classifiers and brain network models.
Disease progression modelling develops longitudinal models of biomarker dynamics based on reference cohorts of major AD and PD studies and integrates them with existing models. The focus during disease progression modelling is the development of rigorous criteria for biomarker-driven staging, which is currently done clinically, and subtype identification. We focus on NDD as a continuum and hypothesize that cognitive staging can be accomplished using a multidimensional model based on continuous biomarkers that provide a more nuanced and detailed description of individual disease stages.
A further part of the project is to develop a novel interface that bridges the gap between systems biology and computational neuroscience by connecting the molecular scale of genes, proteins, metabolites, and other molecules with large-scale brain dynamics emerging from the concerted interaction of distributed brain areas.
Personalized brain network simulation is the third component of the computational biomedicine pillar, which is concerned with the predictive personalized simulation and recommendation of counter-measures. We will use and further develop TheVirtualBrain (TVB) large-scale brain network model component of the VirtualBrainCloud to act as a recommendation system that shows patients options to lower their personal risk to develop dementia.
Cloud – computing is key paradigm for implementation of services in the VirtualBrainCloud project. The objectives in this pillar are to evaluate and to develop workflows of data processing, standardization, FAIRification, and pipelining. The VirtualBrainCloud consortium will ensure the highest provenance and lineage tracking of data through all transformations, analyses and interpretations.
The second central methodological aspect of the integration pillar is the integration of developed prototype software into a robust and medically certifiable software that has a cloud environment-based backend and various front-ends that apply for different user groups.
Public summaries and progress reports will be published here soon