CNS*2026 Halifax

Introduction
Cognitive decline occurs in aging [1] and is accompanied by changes in electroencephalography (EEG) signals [2]. However, the cellular mechanisms underlying these EEG alterations cannot be directly assessed in living humans. Key cellular and microcircuit mechanisms implicated in human aging include reductions in inhibition from different interneuron types, dendritic spine density, and NMDA receptor signaling [3–5], but the links to age-related cognitive decline and EEG changes remain to be established.


Methods
To overcome experimental limitations, we trained artificial neural networks (ANN) to estimate microcircuit aging using simulated EEG biomarkers generated by detailed models of human cortical microcircuits that integrated key microcircuit mechanisms in human aging [6]. We then applied the ANNs to estimate microcircuit aging for each subject in the LEMON dataset [1] (ages 20-80) from their resting-state EEG and examined associations between estimated microcircuit aging and cognitive scores.


Results
The simulated EEG biomarkers from aging microcircuit simulations accounted for a large portion of the range of changes in aging patient EEG. The ANNs estimated microcircuit aging with high precision in silico, and when applied to human EEG data, estimated microcircuit aging corresponded with subject age and was correlated with cognitive decline across multiple cognitive domains. Furthermore, we found sex-specific differences in correlations with microcircuit age for some of the cognitive domains. Among EEG biomarkers used by the ANN, the aperiodic features most strongly influenced microcircuit aging estimations.


Discussion
We demonstrate a modeling-informed approach to estimate microcircuit aging in human subjects from non-invasive EEG, and showed that microcircuit aging was associated with cognitive decline. Future directions will be to estimate changes in the levels of the individual microcircuit mechanisms to tease apart their contributions to cognitive decline. Our approach and tools improve the mechanistic understanding of aging and may further serve in clinical stratification of associated pathologies.


References
1.\tBabayan A, et al. (2019). Sci Data. 6(1):180308. DOI: 10.1038/sdata.2018.308
2.\tMerkin A, et al. (2023). Neurobiology of Aging. 121:78–87. DOI: 10.1016/j.neurobiolaging.2022.09.003
3.\tChen Y, et al. (2023). Neurobiology of Aging. 125:49–61. DOI: 10.1016/j.neurobiolaging.2023.01.013
4.\tPetanjek Z, et al. (2011). Proceedings of the National Academy of Sciences. 108(32):13281–6. DOI: 10.1073/pnas.1105108108
5.\tPegasiou CM, et al. (2020). Cerebral Cortex. 30(7):4246–56. DOI: 10.1093/cercor/bhaa052
6.\tGuet-McCreight A, et al. (2025). Aging Cell. e70329. DOI: 10.1111/acel.70329

Acknowledgement
Alexandre Guet-McCreight and Etay Hay thank the Krembil Foundation for their generous funding support. Alexandre Guet-McCreight thanks the Canadian Institutes of Health Research—Institute of Aging for funding support.