The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas. Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys. The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes. In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.
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Changes in sleep EEG with aging in humans and rodents
Abstract Sleep is one of the most ubiquitous but also complex animal behaviors. It is regulated at the global, systems level scale by circadian and homeostatic processes. Across the 24-h day, distribution of sleep/wake activity differs between species, with global sleep states characterized by defined patterns of brain electric activity and electromyography. Sleep patterns have been most intensely investigated in mammalian species. The present review begins with a brief overview on current understandings on the regulation of sleep, and its interaction with aging. An overview on age-related variations in the sleep states and associated electrophysiology and oscillatory events in humans as well as in the most common laboratory rodents follows. We present findings observed in different studies and meta-analyses, indicating links to putative physiological changes in the aged brain. Concepts requiring a more integrative view on the role of circadian and homeostatic sleep regulatory mechanisms to explain aging in sleep are emerging.
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- Award ID(s):
- 1724405
- NSF-PAR ID:
- 10352315
- Date Published:
- Journal Name:
- Pflügers Archiv - European Journal of Physiology
- Volume:
- 473
- Issue:
- 5
- ISSN:
- 0031-6768
- Page Range / eLocation ID:
- 841 to 851
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00424-021-02545-y
