1 2 Humans are living longer, resulting in our bodies outliving our brains. Although behavioral changes and better 3 management of health conditions can help reverse or slow down premature decline in brain function, pre- 4 symptomatic adults are not identified early enough when these changes would make the most significant positive 5 impact. Effective intervention requires early detection before irreversible brain damage occurs, but there is a lack 6 of objective, scalable tools to assess brain function in pre-symptomatic adults. 7 8 Brain Age (BA) is a biomarker that can be used for early detection of deterioration in brain function. BA reflects 9 an individual's age-adjusted structural and/or functional brain characteristics and has been shown to detect10 cognitive impairment. While effective, the cost and inconvenience of current assessment methods (e.g., MRI,11 polysomnography) prevent widespread usage of BA as a biomarker. Therefore, there is a clear unmet need for12 a new method for assessing BA.1314 NeuroGeneces' BA machine learning (ML) model, using at-home sleep data, will provide an objective and15 interpretable measure of age-adjusted brain function. In this Phase I STTR project, NeuroGeneces will expand16 the dataset of sleep recordings by conducting a Human Subject Study and validate the feasibility of a ML model17 that accurately predicts biological BA in cognitively healthy adults using an at-home sleep EEG headband.
Public Health Relevance Statement: PROJECT NARRATIVE
Cognitive decline is a primary health concern of adults over 45 years old. Although Brain Age (BA) has been
proven to be a biomarker for early detection of deterioration in brain function, current approaches for BA
assessment are ineffective, expensive, or inappropriate for large-scale screening. NeuroGeneces' machine
learning BA model for BA assessment, fed by a low-cost, home-based EEG sleep headband, could provide an
early, objective assessment of brain function.
Project Terms: <21+ years old>
