What is it about?
Our brains constantly change as we age, but our understanding of how these changes affect brain activity over a lifetime lags behind. Specifically, it has been unclear which aspects of brain activity are the best indicators of aging. This study addresses this gap by extensively profiling regional brain signals in adults aged 20 to 90 years using magnetoencephalography (MEG), which records tiny magnetic fields produced by the brain. We found that temporal autocorrelation of the brain’s signals was most indicative of a person’s age. Temporal autocorrelation measures how similar a brain signal is to itself over short intervals and potentially reflects fundamental network reconfiguration during aging.
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Why is it important?
Our results can aid in developing markers for monitoring healthy aging and deepen our understanding of age-related pathologies. Furthermore, this study challenges the traditional approach of focusing on spectral signal features and emphasizes the importance of simple time-domain characteristics in neural signals.
Perspectives
By relying on predictive modeling, this work complements earlier descriptive studies of how electrophysiological activity varies across age groups. I am excited to see how these findings might help identify the precise anatomical, chemical, or functional properties underlying individual lifespan trajectories.
Dr. Christina Stier
University of Muenster
Read the Original
This page is a summary of: Temporal autocorrelation is predictive of age—An extensive MEG time-series analysis, Proceedings of the National Academy of Sciences, February 2025, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2411098122.
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