Research Questions
Can a deep learning model be developed using UK Biobank neuroimaging data to accurately predict the brain neural aging state of healthy individuals?
What are the key neural features and brain regions identified by the deep learning model in UK Biobank data that are associated with brain neural aging?
Is there a correlation between the predicted brain neural aging state from the model and potential early pathological changes or cognitive reserve in healthy individuals (explored through UK Biobank long – term data)?
Research Objectives
Extract and preprocess high – resolution neuroimaging data (such as T1 – weighted MRI, DTI) of healthy individuals from UK Biobank.
Construct and validate a deep learning model (e.g., CNN, Transformer – based architecture) for brain neural aging prediction using the preprocessed data.
Analyze the neural features and brain regions emphasized by the model and explore their biological significance in brain aging.
Scientific Rationale
Brain neural aging in healthy individuals is a gradual process, and understanding it can help identify early signs of neurodegenerative diseases. UK Biobank has a large sample of neuroimaging and long – term follow – up data of healthy individuals, which overcomes the limitations of small sample sizes in previous studies. This research can provide a basis for early screening of brain aging – related diseases and the formulation of neuroprotective strategies.
