Transient neural network dynamics in cognitive ageing

NEUROBIOLOGY OF AGING

卷:105

Article

Tibon, Roni¹  Tsvetanov, Kamen A.^2,3  Price, Darren¹  Nesbitt, David¹  Can, Cam^4,5  Henson, Richard^1,6 

[1] Univ Cambridge, MRC Cognit & Brain Sci Unit, 15 Chaucer Rd, Cambridge CB2 7EF, England

[2] Univ Cambridge, Dept Clin Neurosci, Cambridge, England

[3] Univ Cambridge, Dept Psychol, Cambridge, England

[4] Univ Cambridge, Cambridge Ctr Ageing & Neurosci Cam CAN, Cambridge, England

[5] MRC Cognit & Brain Sci Unit, Cambridge, England

[6] Univ Cambridge, Dept Psychiat, Cambridge, England

关键词: Magnetoencephalography;    Hidden Markov model;    Canonical correlation analysis;    Ageing;    Cognition;    Fluid intelligence;   

DOI  :  10.1016/j.neurobiolaging.2021.01.035

来源: Elsevier

【 摘 要 】

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 reorganize 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 = 595) 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 lower-order brain networks, coupled with increased occurrence of higher-order networks, was associated with both increasing age and decreased fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation, and suggest that this shift might reflect a stable property of the ageing brain. (C) 2021 The Author(s). Published by Elsevier Inc.

【 授权许可】

Free