Event-related and resting-state oscillatory dynamics in the healthy ageing brain: how EEG-age and MEG-age can be used as markers of general brain functioning

Abstract

With an ageing global population, the number of older adults with deleterious age-related changes in the brain, including dementia, will continue to increase unless we can make progress in the early detection and treatment of such conditions. This thesis presents a set of research projects that have used EEG and MEG to advance our understanding of oscillatory dynamics in the ageing human brain. First, the Firefly Model (FM) of short-term, event-related oscillatory dynamics was tested. The FM offered an empirically credible, alternative explanation of information processing that relies on systematic oscillatory phase synchronisation and frequency slowing. Second, inspired by the aphorism, ‘All models are wrong, but some are useful’, the FM was used to develop a new phase-based metric – time of synchronisation gradient, tsynchG – for tracking age-related changes in the brain. This tsynchG metric was established as a new EEG-estimate of brain age, with EEG-age significantly correlating with chronological age, before being estimated in MEG for the first time. Thereafter, long-term, resting-state oscillatory dynamics were examined, with peak alpha frequency (PAF) and alternative amplitude-based EEG-age estimates examined as distinct methods of tracking age-related changes in the brain. Using multivariate methods to analyse the broad EEG power spectrum (0.1 Hz to 45 Hz), the resting-state EEG-age and chronological age were also correlated strongly, and EEG-age was a more accurate estimate and accounted for more variance in chronological age than well-established PAF estimates of age. In summary, new phase, frequency, and amplitude metrics are introduced as estimates of brain age, framed as markers of general brain functioning. This thesis offers novel contributions to our understanding of the ageing human brain and how to detect and track deleterious age-related changes. There is substantial scope for research projects to build on these foundations, particularly in enhancing the signal-to-noise ratio of the newly established metrics.

Date of Award	Dec 2024
Original language	English
Awarding Institution	Aston University
Supervisor	Adrian Burgess (Supervisor), Caroline Witton (Supervisor) & Matthew Buckley (Supervisor)