| Title: | Deep learning models for brain age prediction and unraveling patterns of aging with application to neurological disease diagnosis |
| Lead Institution: | University of Ljubljana |
| Principal investigator: | Professor Ziga Spiclin |
Application 68981
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About
Aging and related structural changes of the brain throughout lifespan have been the subject of research for multiple decades. The goal of this study is to contribute to research of a promising biomarker for neurological health, namely brain age. The expected research is expected to take place during the next 2.5 years. During this time, we wish to develop algorithms capable of prediction of an individuals age, given his or hers MR image of the head. The idea is that this predicted age is close to the chronological age of a healthy individuals. A larger gap may indicate an increased neurological aging and would require further examination. The ultimate aim of the research is not only to develop algorithms, but also to unravel the patterns of brain aging, which can lead to a better understanding of many neurodegenerative diseases, such as Alzheimer's dementia and multiple sclerosis, and thus hopefully provide a tool for early diagnostics of these diseases.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 9210 | BASE: Brain Age Standardized Evaluation | Lara Dular (+2) | 2023 | NeuroImage |
| 10079 | Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models | Lara Dular (+3) | 2024 | Computers in Biology and Medicine |
Enabling scientific discoveries that improve human health