Biological age from electroencephalographic activity could be decoupled from a gradual stochastic accumulation of pathologies over adulthood

The brain's biological age, or brain age, can be inferred using electroencephalography (EEG) and may reflect various factors, including early-life influences or the gradual accumulation of pathological changes. We aimed to investigate the relationship between brain age and age-related changes by evaluating the predictive accuracy of brain age models across different stages of adulthood. We hypothesized that if brain age predominantly reflects the accumulation of stochastic brain damage, the models would better predict the age of younger adults than older adults. We analyzed routine clinical EEG data from public hospitals, categorizing outpatients into young, middle-aged, and older age groups. Source activity was reconstructed from the EEG recordings. We focused on spectral power within six brain lobes defined by the Destrieux brain atlas. Random forest models were trained using EEG features from middle-aged adults to predict brain age. We then evaluated the models' accuracy in young and older adults by assessing the R-squared values of the predictions. Contrary to our hypothesis, the predictive performance, on average, did not decline with increasing age, suggesting that brain age may not primarily reflect a gradual accumulation of damage. These findings imply that individual differences in brain rhythms may remain relatively stable across adulthood, indicating that other factors contribute to brain aging beyond accumulated pathological changes.