Principal Investigator: Dr Ni Shu
Beijing Normal University, Beijing, ChinaTags: 49749, brain age, cognition, individual difference, Machine Learning, multimodal, neuroimaging
As the aging of population intensifies, the burden of age-associated functional decline and neurodegenerative disease is increasing. It is vital that we better understand individual differences in the brain ageing process. Thus we aim to work on individual prediction of brain ageing and cognition by combining multi-modal neuroimaging data and machine learning approaches. However, the sample size of current datasets limited the performance of predictive models. The UK Biobank is an extremely valuable data source for promoting the predictive ability of models, by providing a large sample size of imaging data with important biological phenotype information. We will develop algorithms by integrating multi-modal imaging data to predict brain age and cognition at the individual level and build a standard pipeline to make the algorithms which can be easily used. Upon completion, our research will help clinicians make better use of machine learning to improve early diagnosis and personalized medical treatment of neurodegenerative diseases. In the last, we plan to finish the project within 36 months.