Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.

Rapid eye movement sleep (REMS) is increasingly suggested as a discriminant sleep state for subtle signs of age-related neurodegeneration. While REMS expression is under strong circadian control and circadian dysregulation increases with age, the association between brain aging and circadian REMS regulation has not yet been assessed. Here, we measure the circadian amplitude of REMS through a 40-h in-lab multiple nap protocol in controlled laboratory conditions, and brain microstructural integrity with quantitative multi-parameter mapping (MPM) imaging in 86 older individuals. We show that reduced circadian REMS amplitude is related to lower magnetization transfer saturation (MTsat), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*) values in several white matter regions mostly located around the lateral ventricles, and with lower R1 values in grey matter clusters encompassing the hippocampus, parahippocampus, thalamus and hypothalamus. Our results further highlight the importance of considering circadian regulation for understanding the association between sleep and brain structure in older individuals.

Study objectives: Daytime napping is frequently reported among the older population and has attracted increasing attention due to its association with multiple health conditions. Here, we tested whether napping in the aged is associated with altered circadian regulation of sleep, sleepiness and vigilance performance. Methods: Sixty healthy older individuals (mean age: 69y., 39 women) were recruited with respect to their napping habits (30 nappers, 30 non-nappers). All participants underwent an in-lab 40-h multiple nap protocol (10 cycles of 80 mins of sleep opportunity alternating with 160 mins of wakefulness), preceded and followed by a baseline and recovery sleep period. Saliva samples for melatonin assessment, sleepiness and vigilance performance were collected during wakefulness and electrophysiological data were recorded to derive sleep parameters during scheduled sleep opportunities. Results: The circadian amplitude of melatonin secretion was reduced in nappers, compared to non-nappers. Furthermore, nappers were characterized by higher sleep efficiencies and REM sleep proportion during day-compared to night-time naps. The nap group also presented altered modulation in sleepiness and vigilance performance at specific circadian phases. Discussion: Our data indicate that napping is associated with an altered circadian sleep-wake propensity rhythm and thereby contribute to the understanding of the biological correlates underlying napping and/or sleep-wake cycle fragmentation during healthy aging. Altered circadian sleep-wake promotion can lead to a less distinct allocation of sleep into night-time and/or a reduced wakefulness drive during the day, thereby potentially triggering the need to sleep at adverse circadian phase. SIGNIFICANCE STATEMENT: While napping has raised increasing interest as a health risk factor in epidemiological studies, its underlying regulation processes in the aged remain largely elusive. Here we assessed whether napping in the older population is associated with physiological and behavioral changes in circadian sleep-wake characteristics. Our data indicate that, concomitant to a reduced circadian amplitude in melatonin secretion, healthy older nappers are characterized by reduced day-night differences in sleep efficiency and more particularly in REM sleep, compared to their non-napping counterparts. These results suggest altered circadian response as a cause or consequence of chronic napping in the aged and thereby contribute to the understanding of nap regulation during healthy aging.

Fixed sleep schedules with an 8 h time in bed (TIB) are used to ensure participants are well-rested before laboratory studies. However, such schedules may lead to cumulative excess wakefulness in young individuals. Effects on older individuals are unknown. We combine modelling and experimental data to quantify the effects of sleep debt on sleep propensity in healthy younger and older participants. A model of arousal dynamics was fitted to sleep data from 22 young (20–31 y.o.) and 26 older (61–82 y.o.) individuals (25 male) undertaking 10 short sleep–wake cycles during a 40 h napping protocol, following >1 week of fixed 8 h TIB schedules. Homeostatic sleep drive at the study start was varied systematically to identify best fits between observed and predicted sleep profiles for individuals and group averages. Daytime sleep duration was the same on the two days of the protocol within the groups but different between the groups (young: 3.14 ± 0.98 h vs. 3.06 ± 0.75 h, older: 2.60 ± 0.98 h vs. 2.37 ± 0.64 h). The model predicted an initial homeostatic drive of 11.2 ± 3.5% (young) and 10.1 ± 3.5% (older) above well-rested. Individual variability in first-day, but not second-day, sleep patterns was explained by the differences in the initial homeostatic drive for both age groups. Our study suggests that both younger and older participants arrive at the laboratory with cumulative sleep debt, despite 8 h TiB schedules, which dissipates after the first four sleep opportunities on the protocol. This has implications for protocol design and the interpretation of laboratory studies.

Background: Abnormal tau accumulation is first observed in several neuromodulatory subcortical nuclei before its emergence in the allocortex, and long before the first deposits of amyloid‐beta (Aβ) plaques. Crucially, the posterior hypothalamus has been put forward as a critical site for Alzheimer’s disease (AD) pathogenesis but its role in AD‐related processes remains poorly investigated in humans. Here, we aimed to investigate whether the structural integrity of the posterior hypothalamus, as assessed in vivo, is associated with PET‐measured AD pathology and cognitive performance in cognitively unimpaired (CU) and impaired older individuals.Methods: 151 CU individuals, 75 mild cognitive impairment (MCI) and 35 AD patients (mean ± SD age = 76.6 ± 6.7y., 137 women; Table 1) from the ADNI3 dataset were included. The volume of the posterior hypothalamus (corrected for intracranial volume) was derived from structural T1‐weighted images using a deep convolutional network algorithm (Figure 1a). ^[18F]Flortaucipir‐PET was used to measure tau burden in the entorhinal cortex (standardized uptake volume ratio [SUVR], partial volume corrected values). Amyloid‐β (Aβ) positivity was determined with ^[18F]Florbetaben‐ or ^[18F]Florbetapir‐PET in a cortical summary region (SUVR values). Cognition was evaluated with the Preclinical Alzheimer’s Cognitive Composite (PACC). Analyses were adjusted for age, sex, and education.Results: AD patients exhibited lower posterior hypothalamic volume compared to CU Aβ‐ (p = 0.003) and CU Aβ+ participants (p = 0.01) but no significant difference was observed between CU and MCI patients nor between MCI and AD patients (Figure 1b). Linear regression models revealed that lower posterior hypothalamic volume was associated with higher entorhinal tau‐PET values (p = 0.02, Figure 2a). In addition, CU Aβ+ with higher posterior hypothalamic volume exhibited higher entorhinal tau‐PET values compared to a negative slope in CU Aβ‐ participants (p = 0.05, Figure 2b). Finally, lower posterior hypothalamic volume was related to lower PACC scores (p<0.001, Figure 2c), which was mainly driven by AD patients (p<0.05 for comparisons with CU and MCI, Figure 2d).Conclusions: Our findings indicate that the posterior hypothalamus is linked to disease‐related pathophysiological and cognitive changes. Future analyses will explore whether posterior hypothalamic volume is associated with inflammatory markers and changes in tau accumulation and cognitive performance over time.