Grant: Project Grant

Alzheimer's disease (AD) develops gradually, with brain changes emerging years before symptoms appear. Detecting these early changes is crucial for identifying individuals at risk and developing timely interventions. However, current diagnostic tools are expensive, invasive, or not designed to track subtle, individual differences in brain activity over time-making early detection difficult. This project aims to develop a fast, noninvasive approach to assess brain health using brief, minutes-long recordings of brain activity. By applying advanced computational techniques, we will extract a personalized neurophysiological profile from these recordings-a unique signature of an individual's brain function. This approach will allow us to monitor healthy brain aging and detect the earliest signs of AD before symptoms appear, providing a powerful tool for prevention and intervention strategies. To achieve this, we will first establish lifespan brain activity benchmarks using a large dataset of over 1,700 healthy individuals (ages 4-88), identifying how neurophysiological traits remain stable or evolve with age. We will then apply this profiling method to a rare 15-year dataset of individuals at risk for AD, tracking how their brain activity changes over time and identifying early deviations that may signal future cognitive decline. By leveraging existing datasets, this project can be rapidly implemented, accelerating progress toward early AD detection and precision medicine. If successful, it could provide a cost-effective, widely accessible tool for doctors and researchers to identify individuals at risk, years before symptoms develop. This could help guide early interventions aimed at preserving brain health, slowing disease progression, and improving quality of life. In the future, this approach could be extended to other brain disorders, including mental health conditions, paving the way for personalized brain health monitoring and targeted treatment strategies.