An α-oscillation-based brain clock derived from source-space resting-state EEG provides a sensitive functional marker of brain aging capable of capturing neurodegenerative processes as well as the impact of social disparities, with structural inequality emerging as the strongest predictor of brain age gap.
Key Findings
Results
Brain age gaps (BAGs) were significantly increased in MCI and dementia groups compared to healthy controls, with particularly pronounced effects in posterior cortical regions.
BAG was computed from spectral descriptors of α-activity in the rsEEG source space across 1228 participants
Groups included healthy controls, individuals with mild cognitive impairment (MCI), Alzheimer's disease patients, and behavioral variant frontotemporal dementia patients
Posterior cortical regions showed the greatest BAG increases, consistent with the known vulnerability of posterior networks in neurodegeneration
The study spanned 10 countries, providing cross-national validation of the BAG signal
Results
Structural inequality was the strongest predictor of brain age gap, surpassing cognition, education, and sex as predictors.
Participants resided in 10 countries with varying levels of structural inequality, enabling assessment of sociodemographic diversity
Structural inequality outperformed cognitive measures, years of education, and sex in predicting BAG magnitude
This finding suggests that socioeconomic and societal factors independently accelerate brain aging as reflected in α-oscillation patterns
The result highlights that brain clock estimates are sensitive not only to neurodegeneration but also to broader social determinants of health
Results
Source-space EEG α-oscillations serve as a viable functional marker of brain aging, providing an alternative to structural neuroimaging-based brain age estimation.
Most existing brain clock methods rely on structural neuroimaging (e.g., MRI), whereas this approach uses rsEEG, which is more accessible and scalable
Spectral descriptors of α-activity in source space were used to compute BAG across the full participant sample (N = 1228)
The approach demonstrated sensitivity to both neurodegenerative disease states and sociodemographic disparities
α-oscillations are described as 'a well-established marker of brain functional aging' in the study
Results
The EEG-based brain clock approach was designed and validated for use in underserved and resource-limited settings, supporting population-wide screening.
The study included participants from 10 countries with varying levels of structural inequality, reflecting diverse resource contexts
EEG is described as a 'scalable, accessible approach to brain health' compared to neuroimaging alternatives
The authors frame this approach as showing 'promise for translational use and population-wide screening in underserved, resource-limited settings'
Inclusion of countries with high structural inequality was deliberate to test generalizability beyond high-income, well-resourced populations
Background
Functionally based brain age estimation approaches remain scarce, particularly for assessing age-related neurodegenerative diseases.
The authors note that 'most current methods rely on structural neuroimaging' for brain clock estimation
'Functionally based approaches remain scarce, especially for assessing age-related neurodegenerative diseases'
This gap motivated the use of rsEEG α-oscillations as a functional alternative
The study positions itself as addressing a methodological gap in the brain aging and neurodegeneration literature
Methods
The study examined α-oscillation-based BAG in source space across a multinational sample of 1228 individuals spanning healthy aging, MCI, Alzheimer's disease, and behavioral variant frontotemporal dementia.
Data collected across 10 countries with varying levels of structural inequality
Resting-state EEG (rsEEG) source space analysis was used to compute spectral α-activity descriptors for BAG estimation
What This Means
This research suggests that brain aging can be measured using a type of brain electrical activity called alpha oscillations, recorded with EEG (electroencephalography). By comparing a person's predicted brain age (based on their EEG patterns) to their actual chronological age, researchers can calculate a 'brain age gap' (BAG) — a positive gap meaning the brain appears older than expected. The study analyzed EEG data from 1,228 people across 10 countries, including healthy individuals and people with memory and cognitive disorders such as mild cognitive impairment (MCI), Alzheimer's disease, and frontotemporal dementia. Results showed that people with these neurodegenerative conditions had larger brain age gaps, especially in the back regions of the brain, suggesting that alpha oscillation patterns are sensitive indicators of brain deterioration.
A particularly striking finding is that structural inequality — the degree of social and economic disparity in the countries where participants lived — was the single strongest predictor of brain age gap, even more so than cognitive performance, education level, or sex. This means that people living in more unequal societies tend to show signs of accelerated brain aging, independent of whether they have a diagnosed neurological condition. This points to a powerful role for social and environmental factors in shaping brain health over the lifespan.
This research matters because EEG is far cheaper, more portable, and more widely available than the brain scanning technology (MRI) that most brain age tools currently require. This suggests that an EEG-based brain clock could be deployed in lower-resource settings around the world to screen populations for signs of accelerated brain aging — whether due to disease or social disadvantage — without needing expensive infrastructure. The inclusion of participants from countries with high levels of inequality makes these findings particularly relevant for global health efforts aimed at underserved communities.
Otero M, Carriel-Rubilar F, Hernandez H, Cuadros J, Condado J, Sainz-Ballesteros A, et al.. (2026). Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity.. Communications biology. https://doi.org/10.1038/s42003-026-10205-z