Local sleep: A new concept in brain plasticity

The relationship between sleep health and multimorbidity in community dwelling populations: Systematic review and global perspectives

Previous research has identified a relationship between sleep problems and multimorbidity, defined as the co-occurrence of two or more chronic health conditions in the same individual. This systematic review sought to summarize the literature on the association between sleep duration and quality and multimorbidity in adult community-dwelling populations. A comprehensive search of the PubMed, Embase and CINAHL databases identified studies published between January 1990 and January 2023. Studies were included if they focused on community-dwelling populations, used an observational design, measured sleep quality or duration, used multimorbidity as the main study outcome, and explored the relationship between sleep and multimorbidity. Two reviewers independently conducted study screening, data extraction, and bias assessments. Twenty-four cross-sectional and five prospective cohort studies met the inclusion criteria, with studies from 16 countries and two with cross-country comparisons, and a total participant number of 481,862. Overall, poorer sleep quality and sleep duration outside current recommendations were consistently associated with multimorbidity, though with varying strength. This association was present regardless of specific multimorbidity definitions from different studies. Definitions of sleep duration and quality were inconsistent across studies, possibly contributing to mixed evidence on the observed association. Most studies were cross-sectional, limiting the assessment of the temporal direction of association. Our results corroborate relationships between poor sleep and risk of multimorbidity in adult community-dwelling populations around the world. Examining this relationship may help increase public health awareness of sleep as a modifiable risk factor for the prevention of chronic disease and healthy aging.

Closed-loop modulation of local slow oscillations in human NREM sleep

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.