The fascination with the phenomenon ‘sleep’ is probably best reflected in the ever-expanding ways in which sleep researchers describe this ‘state-of-being’. Our description of sleep is sometimes limited by the requirements to use standard approaches, technologies and reporting formats. These standards, such as those required for sleep-scoring, have no doubt greatly facilitated communication, comparisons, etc. Non-standard approaches to the analyses of core state variables, such as the electroencephalogram (EEG) or motor activity and body position will, however, shed new light on sleep and its functions.
Intra- and Interindividual Differences in Slow Wave Sleep (SWS): Where do they Come from? What do they Mean?
In the current issue of the Journal of Sleep Research, Langheim et al. (2011) apply analysis methods developed in the field of magneto-encephalography to high-density EEG recordings (60 s in duration) obtained during wakefulness, early SWS, late SWS, rapid eye movement (REM) sleep with rapid eye movements, and REM sleep without rapid eye movements. After applying what is referred to as autoregressive integrative moving average modelling, the authors describe differences between the states and differences between early and late SWS. The objective of these analyses was to describe changes in functional connectivity between brain regions. The results show that in SWS, strong positive interactions exist in a left fronto-temporal parietal cluster and that this functional connectivity is reduced during late SWS. The data not only imply that some aspects of the sleep process may be lateralized, but also highlight large interindividual differences in this facet of the sleep phenotype.
When scoring sleep EEGs, we are all confronted with the large interindividual differences in the amplitude of the EEG, and slow waves in particular. In many approaches to the quantification of sleep regulatory processes, these differences in the absolute values of the amplitude of the EEG have been ignored by emphasizing relative changes, e.g. by expressing slow wave activity (SWA) in recovery sleep as a percentage of SWA in baseline sleep. There is, however, a renewed interest in the determinants of interindividual differences in absolute levels of the amplitude of slow waves, for example. Buchmann et al. (2011a) used structural magnetic resonance imaging to investigate associations between individual differences in maximal SWA during baseline sleep and grey matter volume, white matter volume, thickness of the skull and outer liquor rooms, in a sample of 20 young adults (aged on average 25 years). Perhaps the most surprising finding of this study was a lack of correlation between grey matter and SWA, because in a previous study of adolescents these variables were found to be correlated (Buchmann et al., 2011b). In the present sample, 38% of the between-subject variation in SWA was explained by differences in the volume of the corpus callosum. This latter finding emphasizes the importance of interhemispheric connections and reminds us that there are global as well as local aspects to sleep and the sleep EEG.
Although the EEG is widely used to describe sleep, quantifying motor activity using video approaches represents an alternative approach to ‘sleep phenotyping’, and may shed some light on sleep regulation, and disturbances thereof, in conditions with a significant motor-component, such as narcolepsy–cataplexy. Frauscher et al. (2011) describe motor events by using whole-night video-polysomnographic analyses in narcolepsy–cataplexy patients and controls. Mean motor activity was higher in the patients, similarly in non-REM and REM sleep, and involved more body parts in the patients than in controls. The data support the notion of a ‘general sleep motor dysregulation’ in narcolepsy–cataplexy.
Sleep is a rich phenotype and the rapid expansion of available monitoring, imaging and analyses approaches will help us to capture that richness.