Brain reserve is an important but under-quantified concept in neuroscience. Clinically, many older adults who appear stable may experience sudden cognitive decompensation (e.g., delirium, acute confusion) following infections or surgery, analogous to patients with borderline cardiac function deteriorating after stressors. Despite recent progress in brain age modeling, there is no quantitative, clinically usable metric for brain reserve akin to EF in cardiology. Moreover, the underlying genetic and proteomic determinants of brain reserve and acute brain dysfunction remain unclear. Integrating Mendelian randomization and multi-omics analysis can provide causal insights and identify potential intervention targets.
1.Propose a “Brain Reserve Function” index, inspired by cardiac EF, by combining brain age estimation, genomics, and proteomics.
2.Apply two-sample Mendelian randomization (MR) to identify causal genomic and proteomic determinants of brain reserve and acute cognitive decline.
3.Integrate imaging, cognitive, genetic, and protein biomarker data from UK Biobank using advanced machine learning and multi-omics analysis.
4.Evaluate the predictive utility of the brain reserve index for acute cognitive decompensation (delirium, in-hospital cognitive disorders) and long-term decline.
Aiming:
1.Develop and validate a composite brain reserve function index based on mechine learning methods, using brain MRI-based brain age modeling, genomics, and proteomics in UK Biobank.
2.Identify causal genetic and protein determinants of brain reserve and acute cognitive dysfunction via two-sample Mendelian randomization (MR) using large-scale GWAS and pQTL summary statistics.
3.Investigate associations between the brain reserve index and cognitive outcomes (delirium, cognitive impairment, dementia) and assess its predictive value for early risk stratification and prevention.