Research Questions:
How do genetic susceptibility, environmental exposures, and lifestyle factors interact to influence brain structural and functional aging?
Rationale:
Brain aging and cognitive decline represent major public health challenges in our aging society. While previous studies have examined risk factors, the complex interplay between multiple biological and environmental factors in determining brain health remains poorly understood. Recent evidence suggests that combining multiple data modalities could significantly improve our ability to predict and prevent accelerated brain aging.
Key Knowledge Gaps:
1. Limited understanding of how different risk factors interact across the lifespan to influence brain aging trajectories
2. Insufficient validation of early biological markers for accelerated brain aging
Specific Objectives:
1. Develop an integrated risk score for accelerated brain aging by:
– Analyzing genetic variants associated with brain aging
– Assessing lifestyle factors
– Measuring blood-based biomarkers (inflammatory markers, metabolomics)
2. Construct and validate machine learning models to:
– Identify critical temporal windows when specific factors have maximum impact
– Detect interaction effects between different risk factors
– Predict individual trajectories of brain aging
3. Define distinct patterns of brain aging by: Identifying subgroups with different risk factor combinations; Determining modifiable factors with greatest potential for intervention.
Methodological Innovation:Novel integration of temporal sequences in multimodal data; Advanced machine learning approaches for handling high-dimensional, heterogeneous data; Causal inference methods to distinguish correlation from causation.
Expected Outcomes:
1. Identification of critical intervention windows and modifiable risk factors
2. Characterization of distinct brain aging pathways
3. Development of personalized prevention strategies
