| Title: | Age Prediction from NeuroImaging and Genetic Profiles |
| Lead Institution: | University of Calgary |
| Principal investigator: | Dr Ethan MacDonald |
Application 55274
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About
Understanding of the aging process is critical to understanding functional declines and diseases associated with aging such as cardiovascular disease and cancer. In this analysis study, our aim is to improve the understanding of how various features change during the aging process and to construct a mathematical model which can be used to determine how old a person is biologically. This would provide a tool which would allow for investigation of the influence of various factors on the aging process and to flag outliners which have a large deviation of the estimated age and chronological age. While age estimation has been done and there are many new applications published in the literature this year. However, model calibration with the UK BioBank database is relatively limited to date, and due to its size we expect it to perform well and allow for more advanced model types. Being able to estimate the age of a person allows us to get a sense of their overall health. In Canada, there are supercomputing resources available for University employed researchers, with greater than 58,000 cores for performing experiments just like this. Such a system is required to run image analysis jobs of this scale and to answer the important questions proposed. A project of this nature is generally tedious to implement as it can take a long to transfer data, arrange and structure the data for such analysis. Furthermore, a project of this nature is often iterative as a processing pipeline of this nature takes time to debug. As I am independent researching working on this project I have a limited time which I can dedicate to the project and currently do not have other team members to allocate to this task. It is for these reasons I am requesting a 3-year timeframe to conduct this project. The UK BioBank database also has many additional factors such as lifestyle and genetics, which previous age prediction experiments did not have. So, by using these additional variables we will be able to determine how they affect neurodegeneration. In addition to improved age prediction accuracy, we will also glean insights into how these lifestyle and genetic factors change affect the neurodegeneration process.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 19452 | Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank | Aashka Mohite (+7) | 2025 | Human Brain Mapping |
Enabling scientific discoveries that improve human health