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Approved research

Assessing the link between physiological measurements and structural brain images using machine learning

Principal Investigator: Professor Galia Avidan
Approved Research ID: 43724
Approval date: February 22nd 2019

Lay summary

The overarching goal of the present proposal is to characterize structural brain changes in healthy individuals in a wide age range. Recent studies attempted to characterize the relationship between brain structure and age by predicting an individual's age using a structural MRI image. Advancement in the field of machine learning (ML) has enabled remarkable improvement in this domain, with several studies reporting a mean error of ~5 years in predicting age using neuroimaging as input. Linking brain anatomy with age enables to estimate the difference between subjects' brain-age as predicted by the ML model and the subject's real biological age or the delta brain age (DBA). We intend to further improve those existing tools, specifically create a more interpretable result, reduce the need for image preprocessing and examine how DBA is influenced by health-related risk or resilience factors. We plan to examine whether genetic markers of rapid or slow aging can be identified. We expect that the project would last 2-3 years, after which the results, the trained model and code would be openly available.