This project will utilize the UK Biobank’s rich multimodal dataset to investigate how modifiable risk factors-including lifestyle behaviors, environmental exposures, and psychosocial influences-impact brain aging and neurological disorders through underlying biological mechanisms. By integrating longitudinal multi-omics data (genomics, transcriptomics, proteomics, metabolomics, microbiome, epigenetic clocks) with advanced neuroimaging (structural/functional MRI, PET amyloid/tau), electrophysiology (EEG, EMG), digital phenotyping (wearables, mobile apps, voice/speech, facial expression), cognitive assessments, and detailed environmental exposure metrics (air pollution, noise, urbanicity), we aim to construct a comprehensive, multidimensional understanding of brain health. Advanced analytical techniques-including deep learning, causal inference, and Mendelian randomization-will be applied to derive robust and interpretable insights. The study has three core aims: (1) to characterize distinct and shared neural signatures across brain aging and a broad spectrum of neurological conditions (e.g., Alzheimer’s, Parkinson’s, stroke, neuroimmune and neurogenetic disorders); (2) to elucidate causal pathways linking modifiable risk factors (e.g., diet, activity, sleep, stress) with molecular mechanisms and neuroimaging biomarkers; and (3) to build and validate predictive models integrating multi-omics, imaging, digital phenotyping, and clinical data for personalized risk stratification and early intervention. This work will generate critical insights into how modifiable exposures shape brain health, advancing precision neurology and informing evidence-based prevention strategies across the lifespan.
