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Refining sleep homeostasis in the two‐process model

The two-process model postulates that the interaction between the sleep–wake-dependent Process S and the circadian Process C accounts for essential aspects of sleep regulation (Borbély, 1982; Daan et al., 1984). The model has been widely adopted as a conceptual framework of sleep regulation. The original paper has been cited close to 1000 times, and – quite an unusual pattern – the frequency of citation has steadily increased over the years. To characterize the tendency to maintain sleep propensity within a certain range, the term sleep homeostasis was coined (Borbély, 1980) and has also gained wide acceptance. The attractiveness of exploring sleep homeostasis was largely due to the availability of a physiological correlate: EEG slowwave activity (SWA). This measure has allowed monitoring sleep pressure under a variety of experimental paradigms in both humans and animals. Moreover, SWA was shown to be inversely correlated with brief awakenings during sleep (Franken et al., 1991). This behavioral correlate of sleep homeostasis, which had been recognized early on (Tobler, 1983), was critical for expanding the field of sleep research to the realm of invertebrates, in particular to Drosophila, an ideal species for genetic studies. Another crucial development was the recognition that the dynamics of SWA in human sleep showed regional differences. Thus SWA recorded from the frontal area showed a steeper decline than the record obtained from the parietooccipital area (Werth et al., 1996). Do the properties of sleep homeostasis exhibit regional specificity, and, if so, how should these findings be interpreted? This question was examined by Zavada et al. (2009). The authors recorded the EEG from 26 locations and confirmed that during sleep SWA dissipated most rapidly at frontal derivations. To account for the regional changes, they propose a novel Process Z (for Zavada?) that exhibits local specificity, whereas Process S is viewed as a global process that determines by its interaction with Process C the timing of sleep. The instantaneous rates of change of Z were computed from the initial value of SWA and the sequence of vigilance states applying an iterative procedure to optimize the fit between empirical and simulated values. Because only baseline data were used, a quantitative estimate of the rise rate of Process Z could not be obtained. Zavada and coworkers recognize some limitations in their approach, such as the assumption of independence of 1 Hz frequency bins. They conclude that there is a single brain-wide Process S with dynamics independent of location. However, due to non-S related processes, SWA is inadequate for fully characterizing Process S. Thus, due to the regional variability of SWA, Process Z is postulated which unlike Process S is not involved in the timing of sleep. A model should not only account for a specific data set, but predict changes for different experimental conditions. In an extended version of the two-process model, a large data pool was used for parameter estimation and three independent data sets were used for testing the performance of the model (Achermann et al. 1993). Schedules with different circadian phase and different durations of prior sleep and waking showed a good fit of intraepisodic buildup and decline of SWA. The puzzling occasional resurgence of SWA towards the end of sleep emerged from the simulations. Moreover, a permanently active rise of Process S was introduced as a novel feature with considerable conceptual implications. Also the role of a noise component accounting for the variability of the actual data was assessed. However, the necessity of using external variables for the REM sleep trigger and for short arousals clearly demonstrated the limitations of the model. Zavada and coworkers used the Achermann et al. version of the model as a basis of their simulations. A salient feature of the two-process model is its simplicity. A multitude of phenomena can be accounted for by the interaction of only two processes. The addition of further processes may enhance the performance of the model, but it increases also its complexity. Thus a Process W representing sleep inertia was added to S and C to simulate subjective sleepiness (Folkard and Åkerstedt, 1992). To account for long-term changes of neurobehavioral performance under different sleep restriction schedules, a process modulating the homeostatic process across days and weeks was introduced in a novel model conceptually rooted in the two-process model (McCauley et al., 2009). The merit of the paper by Zavada et al. is the attempt to deal with the fact that SWA, the main correlate of Process S, has in addition to its temporal dynamics a topographic variability. It is inevitable that the two-process model evolves to accommodate new data generated by advances of recording and analysis techniques. A further aspect of the spatio-temporal pattern of SWA that will have to be addressed in the framework of the model is its high variability between individuals and its low variability within individuals (Buckelmüller et al., 2006).