Transient resting-state network dynamics in cognitive ageing

Abstract

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganise and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=594) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lowerorder" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and impaired fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation.

How Does Aging Affect Decision-making? An Integrated Model Based on Neuroscience

“…Interpreting aging alterations in connectivity as detected by fMRI poses challenges due to methodological obstacles, including confounding factors such as vascular reactivity and head motion. These factors also exhibit variations in tandem with age, adding to the complexity of the analysis [19].…”