Transient neural network dynamics in cognitive ageing

Abstract

Highlights MEG brain networks alternate rapidly but their functional relevance is unclear We show that the dynamics of these networks vary with age and cognition A lower-to-higher networks shift relates to increasing age and decreasing cognition The relation between this neural shift and cognition increases with older age This shift likely reflects reduction in neural efficiency rather than compensation

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

“…Using similar methods, we have previously observed a "neural shift, " expressed as increased occurrence of brain states involving "higher-order" networks and decreased occurrence of brain states that involve early visual networks. This neural shift was associated with both increased age and decreased fluid intelligence, suggesting that it likely reflects reduction in neural efficiency rather than compensation (Tibon et al, 2021). In light of the reduction in neural efficiency account, we predicted that the neural shift will be associated with the level of sleep dysfunction and that this association is distinguishable from the effects of age.…”

“…Exclusion criteria included poor vision (below 20/50 on Snellen test; Snellen, 1862) and poor hearing (threshold 35 dB at 1,000 Hz in both ears), ongoing or serious past drug abuse as assessed by the Drug Abuse Screening Test (DAST-20;Skinner, 1982), significant psychiatric disorder (e.g., schizophrenia, bipolar disorder, personality disorder), neurological disease (e.g., known stroke, epilepsy, traumatic brain injury), low score in the Mini Mental State Exam (MMSE; 24 or lower; Folstein et al, 1975), or poor English knowledge (non-native or non-bilingual English speakers); a detailed description of the exclusion criteria can be found in Shafto et al (2014), Table 1. Of these, only participants who were considered for our previous study (Tibon et al, 2021; N = 594, following the removal of 98 participants who did not have full neuroimaging data, 15 participants with poor MEG-MRI co-registration, and one participant who had no visits to one of the HMM states) were included. In addition, 30 participants who were included in Tibon et al (2021), but did not have full PSQI data (in all seven measures of sleep quality), were excluded from the current study.…”

“…Of these, only participants who were considered for our previous study (Tibon et al, 2021; N = 594, following the removal of 98 participants who did not have full neuroimaging data, 15 participants with poor MEG-MRI co-registration, and one participant who had no visits to one of the HMM states) were included. In addition, 30 participants who were included in Tibon et al (2021), but did not have full PSQI data (in all seven measures of sleep quality), were excluded from the current study. Thus, the final sample included 564 participants (age range 18-88; see Table 1 for participants' characteristics).…”

“…In addressing these issues, we related transient restingstate neural dynamics to sleep quality, cognitive performance, and age, using a multi-level multivariate approach (Passamonti et al, 2019;Tsvetanov et al, 2021a;Tibon et al, 2021). We used magnetoencephalography (MEG) as a direct measure of neural activity sampled at 1 kHz and higher.…”

“…Unlike fMRI, MEG is not affected by age-related changes in vascular factors (Tsvetanov et al, 2015), and allows simpler and more robust methods for correcting head-motion artifacts (e.g., Taulu and Simola, 2006). We used measures of resting-state MEG dynamic functional connectivity, inferred in our previous study (Tibon et al, 2021) from a population-based adult-lifespan cohort (18 to 88 years of age 1 ), using Hidden Markov Models. These measures capture transient states of activity and connectivity signatures lasting a few hundred milliseconds (HMM; Baker et al, 2014;Vidaurre et al, 2016Vidaurre et al, , 2017Vidaurre et al, , 2018Brookes et al, 2018;Hawkins et al, 2020).…”