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

• 成果类型: Article
• 署名作者: Stier, Christina; Balestrieri, Elio; Fehring, Jana; Focke, Niels K.; Wollbrink, Andreas; Dannlowski, Udo; Gross, Joachim
• 署名单位: University of Munster; University of Munster; University of Gottingen; UNIVERSITY GOTTINGEN HOSPITAL; University of Munster
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11999
• DOI: 10.1073/pnas.2411098122
• 发表日期: 2025-02-25
• 关键词: brain electrical-activity; eeg power; network; neurodevelopment; timescales; childhood; patterns; epilepsy; alpha

摘要

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 emerged as the most robust predictor of age. Distinct patterns of AC within the visual and temporal cortex were most informative, offering a versatile measure of age- related signal changes for comprehensive health assessments based on brain activity.