Prediction of dementia in older adults using nonlinear EEG features

Project Overview

This research focuses on developing innovative electroencephalogram (EEG) biomarkers to predict dementia risk before clinical onset. By analyzing brain activity patterns during sleep and wakefulness, we aim to create early warning systems for cognitive decline and tools to monitor disease progression.

Research Significance

Early prediction of dementia creates crucial opportunities for intervention and prevention. Our research leverages physiological changes that occur before clinical symptoms appear, potentially enabling earlier and more effective treatments.

Key Research Areas

Development of EEG-based biomarkers across different sleep/wake stages
Investigation of sex and ethnicity influences on biomarker effectiveness
Analysis of longitudinal changes in brain activity patterns
Integration of genetic factors related to Alzheimer’s disease and sleep/circadian rhythms

Expected Impact

This research aims to develop:

  • Approaches for evaluating treatment effectiveness
  • Cost-efficient, non-invasive tools for early dementia prediction
  • Methods for monitoring disease progression