Improving Magnetic Resonance Imaging Technologies for the Study of Brain Aging

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Our ability to measure and assess brain aging physiology relies upon imaging technologies, such as Magnetic Resonance Imaging (MRI). MRI improvements continue to show new utility for a broad set of applications, and improving how we gather and interpret data from imaging technologies is positioned to have a very large socio-economic impact for Canada and the rest of the world. Neuroimaging comes in several broad modalities that range in cost and imaging specificity, relative to other neuroimaging approaches, such as positron emission tomography (PET), MRI has the advantage of being more affordable and accessible, allowing imaging of the brain with hundreds of contrast modes and is less invasive on the research participant. The long-term goals of my research program are to contribute to gather large consortium databases used to study brain aging with novel sequences, and use these databases to distill new findings and associations with advanced data analytics and machine learning. In the short term, my objective is to develop a MRI sequence, validated in flow phantoms and human participants, to image the blood brain barrier permeability to water; and to develop specific machine learning models to enrich consortium databases; and models to analyze a range of imaging data collected from several large lifespan consortium databases. Recent developments in the field have shown the preliminary evidence of the ability to measure the flow of water across the blood brain barrier, using advanced MRI sequence methodology. However, these emerging methods need to be more robust and work in aging populations. The molecule responsible for the facilitating the passage of water through the blood brain barrier is called aquaporin, and evidence is mounting that reduced aquaporin could limit the clearing of neural waste products like amyloid, which is a molecule associated with accelerated brain aging. My program takes advantage of many large consortium databases. In my program I detail two big data and machine learning aims: 1) to build an image translation model that can predict position emission tomography images from less costly and less invasive structural MRI, and 2) to conduct advanced modelling of the brain aging process using multi-contrast MRI. This work expands existing cohort databases with new information and investigates methodology for distilling large data. The program will have ground-breaking technological advances and lead to technology to help address brain aging, which has an extremely negative socio-economic problem to Canada and the rest of the world, with a cost estimated to be well over a trillion dollars per year. Our technology advances for image translation work could yield information worth $150M. This program will train highly qualified personnel in advanced methods such as MR pulse sequencing, image processing, biophysical modelling, big data handling, high performance computing, and machine learning.