Brain dynamics which buttress cerebral functions entail stationary and non-stationary interactions between neuronal populations25. Sleep stages, which can be seen as enduring and widespread oscillatory modes sculpting brain activity, allow recurrent brief faster oscillatory activity, which sometimes lead to stage transitions26,27. Here, we focused on spontaneous arousals because their functional correlates remain undetermined. They are usually considered to induce sleep disruption and its detrimental functional consequences. However, spontaneous sleep arousals might also carry positive effects on brain functions. We quantified the prevalence of spontaneous arousals during undisturbed sleep in healthy individuals in late midlife (N = 101), and assessed whether it was associated with early cortical Aβ deposition and cognitive performance. Based on the theoretical concept that sleep arousals are diverse17, we classified them according to their temporal association with a change in muscular tone and a sleep stage transition. Based on this straightforward phenotyping in a large data sample we provide the first empirical evidence that different types of sleep arousals have distinct correlates in terms of cognition and brain amyloid burden. Indeed, we found that arousals associated with sleep transitions (T + E-) are associated with higher cortical Aβ deposition in brain regions affected early on by AD neuropathology, suggesting their association with sleep fragmentation and worse brain status. By contrast and unexpectedly, the more prevalent T-E + arousals, which do not result in sleep transitions, are all the more frequent as Aβ deposition is low and cognitive performance superior, particularly in the attentional domain. This arousal type is therefore associated to a more favourable brain and cognitive status. This is of particular importance since arousals have been reported to increase with age, and age represents the most important risk factor for cognitive decline and AD4.
Our analyses show that the main characteristic differentiating the two types of arousals is whether or not they lead to a sleep stage transition. A second important criterion consisted of the concomitant increase in EMG tone. Aside from their different links with Aβ burden and cognition, T + E- and T-E + arousals are not correlated with each other and differ in their spectral composition: T + E- bear a larger proportion of theta and alpha power while T-E + arousal are composed of a higher proportion of beta power. The reason why T-E- and T + E + arousals are not significantly associated with Aβ and cognition is unclear and might reside in diverging effects of sleep transitions and EMG bursts, which would obscure the relationship. Future studies are warranted to further investigate this issue.
Two hypotheses can be put forward to explain the heterogeneity in arousals. On the one hand, all arousals, triggered by a common set of brain areas, might be part of a continuum in which each arousal is characterised by the intensity in its driving neural activity, its spectral composition, its associated muscular tone and its probability of sleep stage transition. Alternatively, the two arousal types are distinct physiological events prompted by different triggering brain structures and propagation cerebral networks. Oddly enough, the origin of spontaneous arousals remains elusive. Recent fMRI data showed that subcortical regions (including the thalamus, midbrain, basal ganglia and cerebellum) were activated during non-REM (NREM) arousals while cortical regions were deactivated28. A recent yet-to-be-reviewed study in rodents provides evidence that arousals leading to sleep state transition are, at least partly driven by the locus coeruleus (LC), brainstem source of norepinephrine with strong and ubiquitous influence on distant cortical brain regions, including during sleep29. In addition, optogenetic stimulation of the LC causes immediate sleep-to-wake transitions, from both NREM and REM sleep and results in high-frequency EEG activity30,31. Hence, subcortical activity, for instance in the LC, could underlie transition-arousals while no-transition arousals could also merely be the reflection of cortico-cortical or thalamo-cortical interplay17. Identifying the brain sources of the two types of arousals would require invasive animal testing, coupling EEG to fMRI recordings in humans, or source reconstruction of high density EEG signals27.
The cellular and molecular underpinnings of the distinct relationship between the two types of arousals, Aβ burden, and cognition are currently unknown. We can reasonably speculate that T + E- arousals have 2 potentially deleterious impacts. Firstly, they interrupt a sleep stage and consequently all its associated cellular phenomena, like plasticity26. Secondly, it seems possible that they considerably increase cellular activity in diffused cerebral regions, a condition conducive to increase Aβ release. By contrast, T-E + arousals might promote Aβ clearance, hypothetically by increasing the pulsatility of cortical penetrating arteries32. Additionally, T-E + arousals might offer recurring opportunities to transiently synchronise distant brain areas, in frequency bands otherwise related to cognition (beta oscillations) without enduringly disrupting the underlying brain oscillations (i.e. sleep state), similarly to what sleep spindles allow over sigma band (12-16Hz) oscillations33. In complex dynamics wordings, T-E + arousals can be seen as distinct dynamics generated when the oscillatory trajectory is trapped in a local submanifold of an attractor34. These transient oscillations give rise to dynamic instability despite the fact that the global manifold does not change. Dynamic instability is a form of complexity in neuronal systems, which is critical for adaptive brain functions such as selection in self-organising systems, learning or memory25. On the other hand, T + E- arousals would represent a distinct type of complexity, where the involvement of the brainstem would lead to a change in oscillatory regime through a change in the attractor manifold. Similar transient oscillations have been previously reported during wakefulness and related to cognition25. Further studies are needed to unravel whether higher T + E-/lower T-E + arousal indexes are facilitating Aβ aggregation or if, conversely, accumulating Aβ burden is disrupting sleep processes4. Data in young individuals, in which current Aβ detection is typically negative24, as well as longitudinal studies are needed to address this issue.
We emphasise that (1) our cohort only comprised healthy individuals, devoid of SDB, and (2) we focused on spontaneous arousals, which are not generated in response to an endogenous or exogenous perturbation (e.g. apnoea or noise). Therefore, our findings probably do not apply to perturbation-induced arousals and their negative behavioural20,23 and neurodegenerative aftermaths10. It is tantalising to suggest, and empirically testable, that arousals found in SDB mostly consist in transition-arousals which would contribute in part to the higher risk for AD reported in SDB35. We further found no significant link between early Aβ burden and the number of full night-time awakenings during sleep or with time spent awake after sleep onset, two markers related to the fragmentation of sleep macrostructure defining in part sleep quality. The associations we find with Aβ burden in healthy late midlife appear therefore to be stronger with, if not specific to, sleep arousals, as compared to other indices of wakefulness during sleep or fragmentation of sleep. This contrast with a previous actigraphy study that reported correlations between WASO and Aβ burden in participants older than those included here (mean: 76.7 ± 3.5y).36 Our findings may therefore suggest that, at a younger age (~ 59y), the detrimental association between sleep quality and AD neuropathology initially concerns transition-arousals leading to sleep macrostructure fragmentation, before being subsequently detected over other markers of sleep fragmentation.
Sleep arousals may connect the sleeper’s brain with the surrounding endogenous and exogenous relevant incoming information and contribute to elements of cortico-cortical information processing17,34. as done through sleep spindles, another fundamental feature of sleep microstructure33. Our findings constitute the first empirical evidence of the conceptual existence of different arousal types differently associated to important parameters of cognitive and brain health17. Sleep micro-fragmentation, as easily indexed by automatic detection of spontaneous arousals, could therefore constitute a marker of favourable brain and cognitive trajectory in clinical practice, at least in late midlife adults and/or in individuals with still early AD neuropathology.