Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.

ABSTRACT: Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography (MEG) in 350 adults (18 to 88 y). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred time-series features proved superior. Notably, temporal autocorrelation (AC) emerged as the most robust predictor of age. Distinct patterns of AC within the visual and temporal cort

SUBMITTER: Stier C 

PROVIDER: S-EPMC11873822 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature