Grant: Canada Research Chairs

Advances in digital sciences and technologies, accompanied by the massive dissemination of digital tools in health, are bearers of hope for improving the performance and capacities of our prevention and care systems. Thereby, they contribute to the quality of life of all citizens and to the control of associated costs. Integration, management, analysis and interpretation of the ever-increasing volume and quality of collected data and developed models are one of the great challenges of biomedical sciences and public health. A first challenge is to harness information collected at different scales of time and space, i.e., that are multi-scale: clinical, population, cellular and molecular datasets to turn new scientific insights into knowledge (e.g., biomarkers, mechanisms). A second challenge is to develop new precision medicine approaches or prevention strategies based on long term predictive tools using models and data available and auditable by the community. A third challenge is to convert these new scientific insights into tools to better evaluate the organization and management of the health system. Digital medicine has been hugely successful to find personalized biomarkers from medical images (e.g., MRI, PET, or EEG) by exploiting the power of digital image processing and machine learning techniques. But it is not clear how to incorporate in those medical image analysis methods the information contained within an individual patient’s genomic, metabolomic or behavioral data. Answering the previous challenges will allow us to find personalized biomarkers at the scale of medical images, molecular and allow accounting for socio-demographic profile to produce explainable personalized risk health status (RHS) tailored to individual subjects for neurodegeneration. To do so we will develop the whole-brain dynamic computational models and cutting-edge artificial intelligence techniques, and we will use existing databases comprising different longitudinal neuroimaging and behavioral data. This research program will push forward the frontiers of current available methodologies to progressively exploit larger ranges of spatiotemporal scales, from molecular to behaviour. It proposes to develop the missing bricks to tackle the diversity of human brain data available at different scales and according to different frequencies over long periods of time, to come up with tools able to shed light on the neurodegenerative disease as well as an understanding of its causes