EEG and Computational Aspects of How Ageing Affects Sleep Slow Waves.

Abstract

Ageing is known to affect sleep slow waves, but the underlying mechanisms are unknown. Here we aim to precisely quantify the effect of aeging on the shape and dynamics of sleep slow waves in a large cohort of human subjects, and to explore possible underlying mechanisms using a computational model. We analyzed EEG sleep recordings from 2377 healthy individuals aged 19 to 85, collected over multiple nights in their natural environments using the DREEM headband. The fine-structure analysis of slow waves was conducted to assess changes in frequency, amplitude, and variability with age. Additionally, we developed a computational model to investigate possible underlying mechanisms. The study reveals that with aeging, sleep slow waves show a significant reduction in frequency, increase in variability, and decrease in amplitude. Older individuals also experience more sleep fragmentation. REM sleep changes are less consistent, with some findings of minor decreases and others showing no significant changes. The computational model supports these observations by replicating the age-related changes in slow waves from a decrease in excitatory drive, suggesting that ageing affects excitatory interactions at large scales. In conclusion, by examining individuals free from sleep disorders and controlled lab environments, this study provides a detailed characterisation of age-related changes in sleep slow waves and proposes a potential mechanism involving alterations in cortical network connectivity.