Sens.ai and the Buck Institute have developed Brain Years, a functional brain-age model built from EEG, event-related potentials and task performance across users from adolescence to their late 80s, using machine learning to identify which signal combinations track chronological age and functional capacity. An early intervention study reported a 5.18-year improvement on the measure after two months, though the researchers emphasize that placebo-controlled prospective trials are required before any claim of altered brain aging. The distinction matters for longevity practice: functional assessment describes trajectory, not just whether a value sits within a normal range.
Key Points
- Functional EEG-based clock measures brain capacity, not structural appearance
- Two-month intervention produced 5.18-year improvement on the measure
- Placebo-controlled trials needed to confirm biological rather than test-performance change
Longevity Analysis
Trajectory-based measurement addresses a structural weakness in conventional assessment, where a value inside the reference range conveys nothing about the direction of travel. A functional readout of cognitive capacity gives practitioners a way to interpret how attention, processing and recovery are actually performing rather than inferring function from imaging alone, and to track whether a given intervention moves the needle over repeated measurement cycles. The open question — whether a shifting score reflects genuine neural change or improved familiarity with the task — is precisely the question that disciplined, controlled follow-up is designed to answer.
Original published by Longevity.Technology, by Kyle Umipig.