The “Neural Shift” of Sleep Quality and Cognitive Aging: A Resting-State MEG Study of Transient Neural Dynamics

Abstract

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully understood. We tested the hypothesis that a shift in brain networks dynamics relates to sleep quality and cognitive performance across the lifespan. Network dynamics were assessed using Hidden Markov Models (HMMs) in resting-state MEG data from a large cohort of population-based adults (N = 564, aged 18–88). Using multivariate analyses of brain-sleep profiles and brain-cognition profiles, we found an age-related “neural shift,” expressed as decreased occurrence of “lower-order” brain networks coupled with increased occurrence of “higher-order” networks. This “neural shift” was associated with both increased sleep dysfunction and decreased fluid intelligence, and this relationship was not explained by age, sex or other covariates. These results establish the link between poor sleep quality, as evident in aging, and a behavior-related shift in neural dynamics.

Brain‐wide network analysis of resting‐state neuromagnetic data

“…Functional or effective connectivity measured by fMRI has provided essential information on the functional organization of the human brain network. Age‐related changes in functional connectivity have been extensively examined in resting‐state fMRI studies (Andrews‐Hanna et al, 2007 ; Chan et al, 2014 ; Ferreira & Busatto, 2013 ; Geerligs et al, 2015 ; Grady, 2008 ; Grady et al, 2016 ; Masuda et al, 2018 ; Tibon et al, 2021 ; Tibon & Tsvetanov, 2021 ). However, functional connectivity estimated by fMRI involves methodological issues, such as vascular reactivity and head motion, which may change with diseases or age (Geerligs et al, 2017 ; Lehmann et al, 2017 ; Tsvetanov et al, 2015 ).…”

Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort

“… 16 Thus, there is no consensus regarding whether aging involves multi-frequency dynamic oscillatory changes or is characterized by dominant alpha frequency deficiency. Previous Cam-CAN studies 17-20 have indicated reduced neural efficiency or specificity rather than compensation across lifespan. For example, Tibon et al 17 , 18 showed that there was an age-related ‘neural shift’ with decreased occurrence of ‘lower-order’ networks in early visual states and increased occurrence of ‘higher-order’ fronto-temporal-parietal networks in visual and sensorimotor states.…”

“…Previous Cam-CAN studies 17-20 have indicated reduced neural efficiency or specificity rather than compensation across lifespan. For example, Tibon et al 17 , 18 showed that there was an age-related ‘neural shift’ with decreased occurrence of ‘lower-order’ networks in early visual states and increased occurrence of ‘higher-order’ fronto-temporal-parietal networks in visual and sensorimotor states. Another leading theory, 19 , 20 the posterior-to-anterior shift in aging (PASA), states that the anterior regions are recruited when posterior cortical function is impaired.…”

Structural complexity of brain regions in mild cognitive impairment and Alzheimer’s disease

“…Structure coefficients can be viewed as "loadings", indicating to what degree each variable contributes to the predictions made by the model. These coefficients can be used to guide interpretation and are particularly useful in the presence of multicollinearity (Sherry and Henson, 2005;Tibon et al, 2021;Tibon and Tsvetanov, 2022). Therefore, we used the computed structure coefficients for two main purposes, as detailed below: (1) to investigate the correspondence between the two datasets, and (2) to identify brain regions in which FD most reliably predicts group attribution.…”