Biological age from electroencephalographic activity could be decoupled from a gradual stochastic accumulation of pathologies over adulthood
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".