Concepts and models of sleep regulation: an overview

Transcription

1 J. Sleep Res. (1992) 1, Concepts and models of sleep regulation: an overview ALEXANDER A.BORBELY and PETER ACHERMANN Institute of Pharmacology. University of Zurich, CH-8006 Zurich, Switzerland Accepted in revised form 25 February 1992; received 20 January 1992 SUMMARY Various mathematical models have been proposed to account for circadian, ultradian and homeostatic aspects of sleep regulation. Most circadian models assume that multiple oscillators underlie the differences in period and entrainment properties of the sleep/wake cycle and other rhythms (e.g. body temperature). Interactions of the oscillators have been postulated to account for multimodal sleep/wake patterns. The ultradim models simulate the cyclic alternation of nonrem sleep and REM sleep by assuming a reciprocal interaction of two cell groups. The homeostatic models propose that a sleep/wake dependent process (Process S) underlies the rise in sleep pressure during waking and its decay during sleep. The time course of this process has been derived from EEG slow-wave activity, an indicator of nonrem sleep intensity. The predictions of homeostatic models have been most extensively tested in experiments. The interaction of Process S with a single circadian process can account for multimodal sleep/wake patterns, internal desynchronization and the time course of daytime sleepiness. Close links have emerged between the processes postulated by the various models and specific brain mechanisms. Due to its recent quantitative elaboration and experimental validation, the modelling approach has become one of the potent research strategies in sleep science. KEYWORDS circadian, homeostatic, mathematical model, nap, ultradian 1 OVERVIEW OF THE MODELS Three basic processes underlie sleep regulation: (1) A homeostatic process mediating the rise in sleep pressure during waking and the dissipation of sleep pressure during sleep; (2) a circadian process, a clock-like mechanism defining the alternation of periods with high and low sleep propensity and being basically independent of prior sleep and waking; and (3) an ultradian process occurring within sleep and represented by the alternation of the two basic sleep states nonrem sleep and REM sleep. The three processes are illustrated schematically in Fig. 1. The tables provide a synopsis of models that have been proposed to account for circadian, ultradian and homeostatic aspects of sleep regulation. The mathematical equations of the major models are compiled in the appendix. 1.1 Models of circadian rhythms (Table 1) The main objectives of these models are (1) to simulate spontaneously occurring phenomena in free-running Correspondence: Professor A. Borbkly, Institute of Pharmacology, University of Zurich, Gloriastrasse 32, CH-8006 Zurich, Switzerland rhythms such as changing phase-relations between the sleep/wake cycle and the temperature rhythm, and the splitting of the rest-activity rhythm into two components; and (2) to account mainly for the effects of light, the major zeitgeber, on the phase, amplitude and period of circadian rhythms. The database of the models consists in general of records of rest and activity rather than of polygraphically recorded sleep and waking. Moreover, the models attempt to describe general features of the circadian system rather than focus specifically on the sleep/wake rhythm. Nevertheless, a distinction can be made between models that address more specifically issues that are relevant for sleep regulation, and which are being continuously elaborated (Table l), and older ones which cover more selective or marginal aspects of sleep (some entries of Table 4). The monographs by Enright (1980) and Strogatz (1986) on models of the sleep/wake cycle should be particularly mentioned not only because they cover the area in considerable detail but also in view of the many innovative propositions and analyses they contain. A common feature of models of circadian rhythms is that they consist of more than one oscillator. The influence of these multiple oscillators is little apparent under entrained conditions but may become manifest during free-run. They account for splitting and other features of the rest-activity 63

2 64 A. A. Borbkly and P. Achermann Oi00 23:OO necessary to account for the splitting phenomenon in animals. The circadian models summarized in Table 1 face two major problems: (1) There is no compelling evidence for the existence of more than one circadian pacemaker in mammals*. (2) The homeostatic aspect of sleep regulation is not addressed by circadian models (Nakao et a/. 1990a and 1991, make some provisions for such a process), thus requiring additional assumptions. I Circadian :OO 07:OO Ulrradian I I Time of day Figure 1. Schematic representation of the three major processes underlying sleep regulation. W, waking; S, sleep; N, NonREM sleep; R, REM sleep. The progressive decline of nonrem sleep intensity is represented both in the top and bottom diagram (decline of ultradian amplitude). The increase in the duration of successive REM sleep episodes is indicated. pattern in animals, and for the phase-dependence of sleep duration and the biphasic sleep patterns in humans. However, the assumption of multiple self-sustaining oscillators is not mandatory for accounting for various features of free-running rhythms. As pointed out by Eastman (1984), the pattern of internal desynchronization between the sleep/wake cycle and the temperature rhythm can be simulated by periodic or occasional phase-shifts of the former rhythm. Daan, Beersma and associates (Daan and Beersma 1984; Daan et a/. 1984; Beersma et al. 1987) then demonstrated that the interaction of a relaxation oscillator (Process S) with a single circadian process can account for various features of free-running rhythms such as the pattern of internal desynchronization, phase-trapping, and circabidian cycles as well as polyphasic sleep. However, the assumption of subharmonics of components of a single circadian oscillator or of multiple oscillators seems to be 1.2 Models of the nonrem-rem Sleep cycle (Table 2) A distinctive feature of this class of models is that they evolved from neurophysiological data obtained in animals (McCarley and Hobson 1975). Although the original anatomical and physiological assumptions have been somewhat modified (McCarley and Massaquoi 1992), the postulate that the nonrem-rem sleep cycle is generated by the reciprocal interaction of two neuronal systems, has been maintained. The original proposition of a Lotka- Volterra type of interaction was later transposed to humans, and further elaborated into the limit cycle reciprocal interaction model (McCarley and Massaquoi 1986; Massaquoi and McCarley 1990, 1992). Qualitative simulations of the changes in the duration and latency of REM sleep under various experimental conditions were performed. Moreover, the model was applied to predict the ultradian phase-response to a cholinergic agent and to forced awakenings. In its most recent extension (Massaquoi and McCarley 1992), homeostatic processes have been incorporated similar to those proposed by Achermann et af. (1990). Moreover, an arousal process has been included which affects both slow-wave activity and the limit cycle oscillation. 1.3 Models of sleep homeostasis and its interaction with circadian and ultradian processes (Table 3) It has been recognized as early as 1937 that sleep intensity is reflected by the predominance of slow waves in the sleep EEG (Blake and Gerard 1937). The relationship between slow-wave sleep and the duration of prior waking has been documented by Webb and Agnew (1971) and placed into a theoretical framework by Feinberg (1974). The term sleep homeostasis (Borbely 1980) was proposed to characterize the sleep/wake dependent aspect of sleep regulation. The two-process model, originally proposed to account for sleep regulation in the rat (BorbCly 1980; 1982a), postulates that Process S rises during waking and declines during sleep, and interacts with a circadian Process C. The time course of the * This statement requires qualification, since there is strong evidence for an independent circadian oscillator in the eye (Remi el al. 1991). However. this is unlikely to be rclcvant in the prcscnt context. For a discussion of the possibility that separatc paccmakcrs reside in the left and right suprachiasmatic nucleus, see Beersma and Dam (1992).

3 ~~~~ ~ ~~~ ~ ~ ~~ ~ ~~~~~ Concepts and models of sleep regulation 65 Table 1 Two-oscillator and three-oscillator models of circadian rhythms. Asterisk indicates that model equations are provided in the appendix Assumption Des cription Corn m en t E-M model* (Pittendrigh and Daan 1976; Daan and Berde 197X) Two coupled oscillators constitute the cir- Model of circadian rest-activity rhythm of cadian pacemaker. Only phase information is rodents. E-oscillator controlling initial acrepresented in the model equations. tivity bout in nocturnal rodent; M- oscillator controlling terminal activity bout. Oscillators exert mutual phase control. E synchronized by dusk, M by dawn. Differential effect of light. Accounts for (I) intcrdepcndcncc of free-running rhythm and activity time with changing light intensity; (2) after-effects of prior conditions on period and activity time; (3) splitting. Dud circudiun pucemukrr model (Beersma and Daan 1992) Two coupled oscillators constitute the circadian pacemaker. Elaboration of the E-M model. Two identical oscillators that arc usually in phase. Response to light pulses is identical. Splitting due to change in phase relation. Multiplicative interaction to account for activity pattern during splitting. The output of the oscillators determines the probability of activity. Originul x-y model*. Predominant effect o//ight on v (Kronauer et ul., 19x2). Two Van der Pol oscillators x and y affecting Phase and amplitude information reprceach other by 'velocity' type coupling. Lar- sented in the model equations but only ger effect of x on y than y on x. phase information interpreted. 'Strong' (x) circadian oscillator controlling REM sleep. core body temperature. cortisol; 'weak' (y) circadian oscillator controlling the sleep/wakc cycle. Simulation of data (Gander et ul. 1984b; Gander et a/. 198s): (I) Phcnonicna occurring at transition from internal synchronization to desynchronization (e.g. changes in period; 'phasetrapping'); (2) phase-dependence of sleep duration; (3) types of synchronization by zeitgcbcrs; (4) resynchronization after jet lag. Revised x-y mode/*. Predominant effect of light oti x (Kronaucr. 1990) Van der Pol oscillator for x; cumulative Simulation of the effect of light pulses on effect of light. Circadian modulation of sciv phase and amplitude of circadian rhythms sitivity to light. in humans. Three-oscillutor model * (Kronauer, I9X7a j Subdivision of y oscillator into identical y, and yz oscillators. Derived from x-y model. Only phase information represented in thc model equation. Simulation of 'split-desynchrony' patterns of human bimodal sleep pattern (main sleep. napping), and of splitting of rodent rest-activity rhythm. Neurobiological basis: Existence of two separate oscillators unconfirmed, but some evidence for bilateral oscillators (see Beersma and Dam 1992). Assumption of different phase-response curves not upheld by Tupaija experiments (Meijer et ul. 1990). Application of the model to pineal NAT rhythm (Illnerova et ul. 19x9). Neurobiological basis: see previous comment. Adaptation to photoperiod not accounted for. Neurobiological basis: Existence of two separate oscillators unconfirmed. In original model, sleep coincides with central two-thirds of y's trough. Later sleep assumed to occur symmetrically about mid-low y with a phasedelay of 30" (Gander ('1 d. 1984a). The model addresses the difficulty of driving the human circadian system to the point of singularity (i.e. zero amplitude. undefined phase). Neurobiological basis of y, and y2 unspeci lied. Two-o.scil1utor thermoregulutory model oj.sleep conrrol* (Nakao et ul. l99oa; 1991) Two circadian oscillators, one influencing Simulation of biphasic sleepiness pattcrn, temperature, the other sleepiness. Sleep ho- relation of sleep timing to temperature meostasis assumed to be related to longterm rhythm. temperature regulation (heat load during waking; heat dissipation during sleep). Adjustable thresholds determining onset and termination of sleep. Existence o f two separate circadian oscillators not confirmed. Mechanism for the accumulation of ii heat load and its delayed effect on sleep not specified. Available data still ambiguous.

4 66 A. A. Borbdy and P. Achermann Table 2 Models of the nonrem-rem sleep cycle. Asterisk indicates that model equations are provided in the appendix. Assumption Description Comment Reciprocal interaction model* (McCarley and Hobson 1975; revised interpretation: Hobson el al. 1986). NonREM-REM sleep cycle generated by two coupled cell populations in the brainstem with self-excitatory and self-inhibitory connections according to the Lotka-Volterra model. Limit cycle reciprocal interaction model: Original iiersion * (McCarley and Massaquoi 1986; Massaquoi and McCarley 1990) NonREM-REM sleep cycle generated by the reciprocal interaction of two coupled cell populations. Limit cycle reciprocal interaction model: Extended uersions (Massaquoi and McCarley 1992) NonREM-REM sleep cycle generated by the reciprocal interaction of two coupled cell populations. Incorporation of sleep homeostasis and arousal events. Simulation of data: Dischargc ratc of cholinergic FTG (or LDI/PPT) cells in cat. Application of the model with a modified definition of REM sleep to simulate changes in REM sleep in depressive patients (Beersma et ) Main fcaturcs of previous model maintained. but assumption of a stahlc limit cycle oscillation that is independent of initial conditions. Introduction of a circadian term which determines modc of approach to limit cycle. Simulation of data: (1) REMS latency and REMS episode durations in normal humans under various experimental conditions and in dcpressivcs; (2) prediction of an ultradian phaseresponse curve to a cholinergic agent; (3) effect of forced waking on the dynamics of nonrem/rem sleep cyclc hy an cxogenous excitatory input. Assumption of first-order decay dynamics for the iir~usal system. Oualitativc simulation of ultradian slow-wave activity pattern. Neurobiological basis: The role of postulated cell populations in the control of REM sleep. and their interactions have undergone revisions (Hobson ef al. 1986: McCarley and M. lssdquoi., 1992). Neurobiological basis: see previous comment. Neurobiological basis: See McCarley and Massaquoi (1992). homeostatic variable S was derived from EEG slow-wave activity. Various aspects of human sleep regulation were addressed in a qualitative version of the two-process model (Borbdy 1982b). Independently, an elaborated, quantitative version of the model was established (Daan and Beersma 1984; Daan et al. 1984) which predicted the timing of human sleep for a variety of different schedules. A further elaboration of the model was accomplished by Achermann and colleagues (Achermann 1988; Achermann and BorbCly 1990; Achermann et ul. 1990) by including the ultradian variations of slow-wave activity. Recently, Folkard and Akerstedt (1987, 1989, 1992: Akerstedt and Folkard 1990) simulated the changes of daytime alertness by the combined action of a homeostatic process, a circadian process and ;I sleep inertia process. It is interesting that the time constants of their homeostatic process that have been derived from sleepiness ratings, and the time constants of Process S derived from the sleep EEG, are in a similar range. 1.4 Miscellaneous models (Table 4) The models summarized under this heading address either general aspects of rhythms that are not directly related to sleep regulation or very specific points of sleep regulation. Enright (1980) showed that a circadian pacemaker with a high precision can be constituted from an ensemble of sloppy relaxation oscillators. Wever (1984, 1985, 1987) assumed the existence of several distinct yet interacting oscillators. The rest-activity pattern was obtained by applying a threshold. Three separate oscillators were proposed by Kawato et af. (1982), two of which were assumed to determine sleep onset and sleep termination. In addition, Winfree (1983) simulated the distribution of sleep onset and sleep termination by analysing the interaction of a recovery process and an oscillator, an approach that uses similar basic assumptions as in the two-process model. Strogatz (1986) accounted for the internal desynchronization of the sleep/wake rhythm and body temperature by simple two-oscillator models. The gated pacemaker model (Carpenter and Grossberg 1984, 1987) assumes an interaction of two cell populations in the suprachiasmatic nucleus (SCN) as well as some other specific physiological processes to simulate various properties of circadian rhythms. The current views on the physiological processes in the SCN are not all consistent with the assumptions of this complex model. Lawder (1984) proposed that the interaction of a linearly declining process and a periodic REM sleep process could account for the sleep stage distribution as well as for other features of sleep. His qualitative model does not

5 Concepts and models of sleep regulation 67 Table 3 Models of sleep homeostasis and its interaction with circadian and ultradian proccsses. Asterisk indicates that model equations are provided in the appendix. Assumption Description Comrneni Two-process model* (BorbCly, 1982b; Daan and Beersma, 1984; Daan ei a/. 1984; BorbCly et al. 1989) Sleep propensity in humans is determined by a homeostatic Process S and circadian Process C. The interaction of S and C determines the timing of sleep and waking. Model of ultradian variaiion of slow-wave activity* (Achermann, 1988; Achermann and Borbtly, 1990; Achermann el al. 1990) Derived from the two-process model. The level of S determines the buildup rate and the saturation level of slow-wave activity within nonrem sleep episodes. Three-process model of the regulaiion of sleepiness/alertness* (Folkard and Akerstcdt 1987, 1989, 1992; Akerstedt and Folkard 1990) Sleepiness/alertness are simulated by the combined action of a homeostatic process. a circadian Process, and sleep inertia (Process W). Time course of S derived from EEG slowwave activity; phase position and shape (skewed sine wave) of C derived from sleep duration data obtained at various times of the 24-h cycle. Simulation of data: (I) Timing of sleep after slecp deprivation. during prolongcd bcdrcst. internal dcsynchronization during frcc run; variations of sleep onset and sleep termination; (2) level and time course of slow-wave activity in sleep after total or repeated partial sleep deprivation, after daytime naps; (3) variations of slcep latency. In contrast to the original two-process model, the change of S. not the IevcI of S, corresponds to slow-wavc activity. A KEM sleep oscillator triggers the decline of slowwave activity prior to REM sleep. Simulation of data: (1) Time course of slow-wave activity during normal sleep. after slecp deprivation, after daytime nap. aftcr selcctive slow wave deprivation. during extended slecp; (2) increasing duration of REM sleep episodcs, slcep onset REM sleep episode. skipped lirst REM slecp episode; (3) parameter estimation and simulation of indcpendcnt datasets. Parameters dcrived from rated slcepincss during sleep/wake manipuhtions. Simulation of data: Changes of sleepiness/alcrtness during shift-work schedules. Ncurobiological basis of slecp homeostat unknown. Possible basis of EEG slow-wave activity: Burst discharge pattern in the delta rangc of hyperpolarized thalamic neurons (Steriade et ul. 1991). Possible ncurobiological basis of intraepisodic changcs of slow-wave activity: Progressive hyperpolarization of thalamic neurons (see previous comment). Evidence for a REM sleep oscillator in the pons (see McCarley and Massaquoi 1992). Time constant of the homeostatic process similar to Process S in the two-process model (Daan ei ul. 1984). However. the homeostatic process and S change in oppositc directions. establish clear relations to physiological processes. Stochastic models were formulated to account for sleep architecture in humans (Kemp and Kamphuisen 19x6) and for circadian rest-activity rhythms (Dirlich 1984). A model of EEG slow wave generation has been recently published (Caekebeke et a/. 1991). Finally, Nakao et a/. (1990b) attempted to account for the dynamics of neuronal activity in specific sleep states in the cat by a neural network model. 2 EXPERIMENTAL VALIDATION OF MODELS IN HUMANS 2.1 r-y model In the first reports typical sleep/wake and body temperature patterns under conditions of temporal isolation were simulated (Kronauer et a/. 1982, fig. 1; Kronauer 1984). In the simulations, the x and y rhythms are initially synchronized. Then as the period of y lengthens an internal phase drift sets in. Finally, there is phase trapping that signals the transition from synchrony to desynchrony. Predictions were generated on the effects of zeitgebers of different strengths and periods (Gander ef a/. 1Y84a). Wever s data were used to simulate various patterns of partial and full entrainment which had been observed in subjects exposed to artificial zeitgebers (Gander el a/. l984b). In a further study, features of resynchronization after time zone shifts were simulated and compared to the data of four subjects (Gander et a/. 1985). The rate of adjustment was shown to depend on the rhythm being measured. the extent of the time zone shift, the flight direction, and the strength of the zcitgebers in the new time zone. Interindividual

6 ~~ ~ ~ ~~~ ~~ ~~ ~~~ ~ ~~ ~ 68 Table 4 A. A. Borbkly and P. Achermann Miscellaneous models. Designation Multiple relaxation oscillator model (Enright 1980) Multioscillator model of circadian rhythms (Wever , 1987) Three-oscillator model (Kawato et al. 1982) Interaction of recovery process with an oscillator (Win free 1983) Beats model and phase model of circadian rhythms (Strogatz 1986) Gated pacemaker model (Carpenter and Grossberg ) Model of sleep patterning (Lawder 1984) Stochastic model of sleep architecture (Kemp and Kamphuisen 1986) Stochastic models of circadian rest-activity cycle (Dirlich 1984) Model of EEG slow wave generation (Caekebeke et a/. 1991; Da Rosa et a/. 1991) Neural network model of neuronal transition dynamics during deep (Nakao et al. 1990b) Description /comment Circadian pacemaker assumed to consist of an ensemble of relaxation oscillators whose output feeds into an integrating element. Simulation of effect of light pulses on phase, and light intensity on period of circadian rest-activity rhythm. After-effects accounted for by the model, but not splitting. Coupled oscillators; dichotomy of rest and activity by applying a threshold; simulation of an ultradian slow-wave activity pattern by assuming an ultradian oscillator. Separate oscillators controlling sleep onset, sleep termination and body temperature; simulation of internal desynchronization. Analysis of 'forbidden zones' of awakening. Superposition (beats) or coupling (phase) of two oscillators; simulation of internal desynchronization. Assumption of interaction of on-cell and off-cell population in SCN whose feedback signals are gated by slowly accumulating transmitters; on-cells supposed to promote motor activity; fatigue factor acting on off-cells; simulation of phase-response curves, light effects in diurnal and nocturnal animals, AschoFf's rule. splitting, etc. Linearly declining variable interacts with periodic REM sleep process. Simulation of sleep stage distribution in normal nights and after sleep deprivation; sleep onset REM sleep episodes. Model based on sleep stage transition probabilities. Models based on state transition probabilities or a network of random processes. Frequency selective feedback circuit with variable coupling strength. Simulation of phasic (K-complexes) and tonic slow waves, and sigma activity. Simulation of neuronal activity of the white noise type in nonrem sleep and l/f type in REM sleep. differences in pacemaker periods were reported to be an important factor for explaining the observed variability in the response to time zone shifts. Originally it had been assumed that bright light acts on the circadian system via the y oscillator. Experiments with scheduled light exposure led to a modified model in which light has its main effect directly on x. In a first version, it was assumed that the change of brightness (db/dt) and not brightness itself affected x (Kronauer 1987b; Kronauer and Frangioni 1987). Experiments showing that it is difficult to completely suppress the amplitude of the core body temperature rhythm by scheduled light exposures (Jewett et al. 1991), led to a modified version of the model in which a cumulative effect of light on x and its complementary variable (xc.) is assumed, and a circadian variation of sensitivity to light is incorporated (Kronauer 1990). depression (bimodal distribution). They showed that sleep abnormalities in depressive patients can be accounted for by the decrease in the initial value of a single variable which can be taken to represent the extent of REM sleep inhibition. McCarley and Massaquoi (1986) showed with the limit cycle reciprocal interaction model that by varying one of the initial values, a bimodal distribution of REM sleep latency could be obtained. Forced awakenings in 4 subjects at various phases of their nonrem-rem sleep cycle had phase-dependent effects on cycle duration (Foret et al. 1990). These data were used to assess the effect of the excitatory input E on the cycle length in the model (Massaquoi and McCarley 1990). The results are interpreted in terms of 'weak' and 'strong' resettings, and it is concluded that a mixed type of resetting mode prevails. 2.2 Reciprocal interaction model Beersma el al. (1984) used the concepts of the model to account for the altered distribution of REM sleep latency in 2.3 Two-process model The basic assumptions of the model, and in particular of its homeostatic facet, has been subjected to extensive experimental verification.

7 Concepts und models of sleep regulution Experiments testing the original version of the model Independence of S and C. One of the basic assumptions of the model is the independence of the homeostatic process S and the circadian process C. It is supported by results from animal experiments showing that the homeostatic response to sleep deprivation persists even after the circadian rhythmicity has been disrupted or abolished by lesioning the suprachiasmatic nucleus (SCN) (Mistlberger et ul. 1983; Tobler et al. 1983). Evidence that one of the two processes can be independently manipulated, has been obtained in a study in which the circadian phase of Process C (as indexed by body temperature and plasma melatonin) was shifted by bright light in the morning, while the time course of slow-wave activity remained unaffected (Dijk et al. 1987c, 1989). Rise of Process S during waking. In the first, qualitative version of the model. it was inferred from the initial and terminal level of S in normal sleep and in recovery sleep after sleep deprivation, that S rises monotonically during waking according to a saturating exponential function (BorbCly 1982b). The time constant of this fuiiction was then estimated to be 18.2 h (Daan et ul. 1984). By determining slow-wave activity in daytime naps which were preceded by different durations of waking, direct evidence for a monotonic rise of S according to a saturating exponential function was obtained (Beersma et al. 1087; Dijk et ( a). The time constant of the function fitted through the extrapolated initial points of S in the naps was 18.7 h (Beersma et ul. 1987). In another study, slow-wave sleep and slow-wave activity was shown to be higher in afternoon naps than in morning naps, which is in accordance with the predictions of the model (Knowles et al. 1990a,b). Further confirmation for the monotonic rise of S during waking was obtained in an analysis of sleep studies in which the duration of prior waking differed (Dijk et al. 1990b). Again a close correspondence between the data points and a saturating exponential function with a time constant of 18.2 h was obtained. Effect of a nap on subsequent sleep. The two-process model predicts that due to the decline of S during daytime naps, the level of slow-wave activity in the subsequent slecp episode will be reduced. This prediction was confirmed (Feinberg et al. 1985; Daan et al. 1988; Knowles et ul. 1990b). Effect of shortened nighttime sleep on a subsequent duytitne nap. When the duration of nighttime sleep was shortened, slow-wave activity in a subsequent morning nap was enhanced (Akerstedt and Gillberg 1986; Gillberg and Akerstedt 1991). Effect of repeated partial sleep depriuation. Slow-wave activity was measured during two or four nights of shortened sleep (sleep duration 4 h) and two or three recovery nights, respectively (Brunner et al. 1990b, in preparation). The repeated curtailment of sleep duration induced only a minor rise in slow-wave activity whose magnitude did not differ significantly from the prediction of the model Experiments testing un elaborated version of the model allowing for disturbed sleep These experiments test the modified version of the model (proposed by Beersma et al and Dijk et al. 1987b. and formalized by Achermann and BorbCly 1990) in which the change of S, and not its level, is proportional to the momentary slow-wave activity. The latter model addresses not only the global changes of slow-wave activity as represented by Process S, but also the changes within nonrem sleep episodes. Intranight rebound of slow-wave activity. When slow-wave sleep was prevented by acoustic stimulation during the first 3 h of sleep, the magnitude and time course of the subsequent rebound of slow-wave activity was in accordance with the modified concept of the two-process model (Dijk et al. 1987b). Recently, a rebound of slow-wave activity was documented after a 5-h interval of selective slow-wave sleep deprivation in the same nighttime sleep episode, and after a 3-h interval of selective slow-wave sleep deprivation during daytime recovery sleep (Dijk and Beersma 1989). In another study daytime sleep episodes with and without slow-wave sleep deprivation were compared (Gillberg et al. 1991). The experimental suppression of slow-wave sleep during an interval corresponding to 90% of the undisturbed episode resulted in an increased accumulation of slow-wave sleep and an extension of sleep duration. Extended sleep ut different circadian phuses. To test the time course of Process S during extended sleep, long sleep episodes starting at the regular bedtime (00.00 hours; 17 h prior waking; Dijk et ul. 1991b), at a phase-advanced bedtime (19.00 hours, 12 h prior waking: Trachsel et al. 1900; 36h prior waking: Dijk et al. 1990a) and at 21 8-h phase-delayed bedtime (07.00 hours, 24 h prior waking: Dijk et al. 1991a) were studied. On average, slow-wave activity showed a decline over the first 3-4 nonrem-rem sleep cycles and then remained on a constant level. Occasional minor late peaks of slow-wave activity were observed which can be accounted for by the model (Achermann el al. in prep.) Parameter estimation of the model crnd tests on independent experimentul dutusets A further elaborated version of thr model was subjected to an optimization procedure based on the weighted least-square error method. and using the mean time course of empirical slow-wave activity from a large dataset (16 subjects, 26 nights) as a template (Achermann er d. in prep.). A sensitivity analysis showed the system to be

8 70 A. A. Borbdy and P. Achermann rather robust to moderate variations (f5%) of the parameter values. To further test the performance of the model, the estimated parameter values were used to simulate data from three different experimental protocols (prolonged waking followed either by sleep in the evening or by sleep in the morning; and extended sleep initiated at the habitual time). Empirical REM sleep data were used to activate the REM trigger parameter in the model. In general, a close fit was obtained between the simulated and empirical slow-wave activity data and their time course. In particular, the occurrence of late slow-wave activity peaks during extended sleep could be simulated. Minor discrepancies in later cycles of sleep initiated in the evening or in the morning could be due to indirect or direct circadian influences on slow-wave activity. The simulations demonstrate that the model can account in quantitative terms for empirical data and predict the changes induced by the prolongation of waking or sleep Emerging discrepuncies and shortcomings ; suggestions for eluborution of the model Independence of S and C. One of the basic assumptions of the model is the independence of its two constituent processes. However, a possible feedback of S on C has been noted (Daan et al. 1984) as the scheduling of sleep and waking may alter the timing of light exposure and thereby affect the phase of C. This effect has been assumed to account for the phenomenon of phase-trapping (Beersma et al. 1987). A further interaction of S and C may arise from the phase-shift of the circadian pacemaker by daytime motor activity. Such an effect has been demonstrated for animals (Mrosovsky and Salmon 1987) but not yet for humans. The incorporation of disturbing factors has allowed also for an indirect effect of C on S. Thus when sleep is initiated in the early morning, the circadian REM sleep propensity is high. This may impede the full manifestation of slow-wave activity in the first nonrem-rem sleep cycle and thus alter its time course (Dijk et al. 1991a; Lance1 and Kerkhof 1991). Upper and lower threshold of the somnostat. In the model the level of the upper threshold was assumed to be influenced during waking by external stimuli that promote or reduce arousal (Daan et al. 1984). No analogous provisions were made for sleep. This is a shortcoming, since prolonged sleep can be simulated only if sleep itself lowers the level of the lower threshold. Moreover, the brevity of brief waking bouts at night suggests that also the upper threshold is lowered during sleep. An experiment was designed to test the predicted prolongation of sleep duration when slow-wave sleep was suppressed in the first part of the night (Dijk and Beersma 1989). The manipulation did not prolong sleep. However, in another study in which slowwave sleep was suppressed selectively during 90% of a daytime sleep episode, a prolongation of sleep ensued (Gillberg er al. 1991). The divergent results of these studies indicate that a more detailed analysis of the interference with sleep is necessary (e.g. by recording brief arousals) before a satisfactory interpretation can be offered. Sleep inertia and wake inertia. Sleep inertia following awakening is reflected by the short sleep latency when subjects are allowed to return to sleep. An attempt was made (BorbCly et al. 1989) to incorporate a decaying sleep inertia process which had been proposed by Folkard and Akerstedt (1987, 1989). The possibility that a wake-inertia process may influence the initial part of sleep, was suggested by the declining trend of the tonic EMG level throughout the first nonrem sleep episode (Brunner er al. 1990a). A gradual dissipation of wake-inertia may impede the initial buildup of slow-wave activity and thereby shift its maximum from the first to the second cycle. Such a slow-wave activity pattern occurs occasionally in normal subjects (Feinberg et al. 1985) and has been reported for depressed patients (Mendelson et al. 1987). In the two-process model, wake-inertia could be simulated by raising the level of the lower threshold. This would allow to account for the brief duration of daytime naps. Multiple sleep latency. The multiple sleep latency test is a method for quantifying the propensity of sleep initiation. Usually, the test is applied to subjects who are fully aware of the time of day and who adhere at least partially to their habitual daytime activities (e.g. the timing of meals). Under these circumstances, sleep latency is not a monotonic function of the duration of prior waking as is the case for S. Apart from the frequently occurring midday dip (Carskadon 1989) the values remain at a fairly uniform level throughout the habitual waking period. If waking is prolonged, sleep latency declines rapidly. Qualitative simulations have been performed with the two-process model by assuming that sleep latency is represented by the difference between the upper threshold modulated by C, and Process S (BorbCly et al. 1989). By an appropriate skewing of the sinusoidal function representing the threshold, the typical features of sleep latency including the midday dip could be qualitatively simulated. However, these simulations are based on arbitrary adjustments of the model parameters and various features are rather sensitive to minor changes. Although the basic assumption that the propensity of sleep initiation depends on homeostatic and circadian processes is reasonable, a stringent comparison with a larger database is necessary. Whereas some authors emphasize the importance of the midday dip (e.g. Broughton and Mullington 1992), it should be noted that the phenomenon has not been invariably recorded, that its timing varies considerably, and that the extent of the change is much smaller than the circadian variation. In particular, it has not yet been demonstrated unambiguously that the midday dip is due to an endogenous process (see below). Only if its endogenous nature is confirmed will it be necessary to investigate whether it is a result of the interaction of S and C as postulated in the model, or of a bimodal variation in the upper threshold.

9 Concepts and models of sleep regulation 71 Change of Process S during REM sleep. The direction of the change of S during REM sleep episodes is still an open question. It could be argued that S declines since REM sleep is a part of the sleep process. However, this argument is not compatible with the recent version of the model in which the change of S is determined by slow-wave activity. Since there are practically no slow waves in REM sleep, S should remain unchanged. However, if the absence of slow waves is considered to be equivalent to waking, an increase of S would be expected. The latter assumption was incorporated in the most recent version of the model in which the rising component of S is permanently active and interacts additively with the declining component of S which is a function of slow-wave activity (Achermann er al in preparation). Due to the limited duration of REM sleep episodes, it is difficult to devise experiments for testing the different hypotheses. REM sleep homeostasis. In the first version of the model, REM sleep propensity was assumed to be predominantly determined by Process C (Borbely, 1982b). In addition, a reciprocally inhibitory interaction of the REM sleep controlling factor and Process S was postulated. The homeostatic regulation of REM sleep was recognized but not incorporated in the model. In the subsequent quantitative version, REM sleep was disregarded (Daan et al. 1984). One of the difficulties in modelling REM sleep regulation is the absence of an intensity measure in humans. A rise in REM sleep pressure manifests itself only in the increased duration of REM sleep. During recovery from total sleep deprivation, slow-wave sleep and EEG slow-wave activity exhibit an immediate rebound whereas the increase in REM sleep is delayed to subsequent nights or is not present at all (references in BorbCly 1982b). These results suggested that the intensity of nonrem sleep is much more finely regulated than REM sleep. Recent findings contradict this notion. A REM sleep deprivation in the first 5 h of sleep induced a REM sleep rebound in the subsequent 2.25 h (Beersma et al. 1990). A curtailment of sleep duration in 2 nights which induced a substantial REM sleep deficit, was followed by a REM sleep rebound in the two recovery nights (Brunner et al. 1990b). In both experiments, the REM sleep rebound occurred at a time when slow-wave pressure was either low at the end of sleep (Beersma et al. 1990) or was much less increased than REM sleep pressure (Brunner et a/. 1990b). These results suggest also that REM sleep is finely regulated but that the manifestation of REM sleep homeostasis is hampered by an elevated slow-wave pressure. In accordance with the notion of a mutual inhibitory interaction of the factors controlling slow-wave activity and REM sleep (BorbCly 1982b), not only REM sleep is inhibited by slow-wave pressure, but also slow-wave activity by REM sleep pressure. Thus there was a significant reduction in the low-frequency activity of the nonrem sleep EEG during selective REM sleep deprivation (Beersma et al. 1990). Also the rise in REM sleep pressure induced by partial sleep deprivation seemed to suppress the typical low-delta peak in the nonrem sleep spectrum (Brunner et al. 1990b). Before REM sleep homeostasis and its interaction with nonrem sleep homeostasis can be incorporated into a model, it will be important to determine whether the inhibitory influence of REM sleep on the nonrem sleep spectrum represents merely a suppression of the EEG manifestation of Process S, or if S itself is affected. Generation of the nonrem-rem sleep cycle. In the original version of the model, the generation of the nonrem-rem sleep cycle was not incorporated. In a recent version, an oscillating variable interacts with a REM sleep trigger threshold (Achermann et al. 1990). REM sleep occurs whenever slow-wave activity falls below a threshold. Quantitative simulations have not yet been performed. There is a convergence between the elaborated version of the twoprocess model and the limit cycle reciprocal interaction model: the ultradian oscillating variable of the former model can be substituted by the limit cycle variable of the latter model (Achermann and BorbCly 1992), and homeostatic processes can be incorporated in the limit cycle interaction model (Massaquoi and McCarley 1992). Is Process S affected by the intensity of waking? Whereas nonrem sleep intensity as indexed by slow-wave activity determines the time course of Process S during sleep, its rise during waking is assumed to occur invariably according to a saturating exponential function. This means that S depends exclusively on the duration of waking, and that the intensity of waking is irrelevant. There is some evidence to the contrary. Thus an increase in slow-wave sleep was observed after a hyperthermic episode several hours prior to sleep as well as after a day of exposure to intense and varied environmental stimuli (see Horne 1988, 1991, 1992 for references). Horne (1992) argues that the common effect of these procedures is the increase in brain metabolism. The notion that sleep is promoted by a cumulative effect of heat load has been incorporated in the model of Nakao et al. (1990a, 1991). Further experiments are needed to test this proposition. 3 CONTROVERSIAL ISSUES AND OPEN PROBLEMS 3.1 Two-per-day rhythm of sleep propensity: endogenous or exogenous? Broughton ( 1975) proposed an endogenous two-per-day rhythm to account for the biphasic time course of vigilance, showing reductions in the afternoon and evening. He proposed that this circasemidian sleep propensity rhythm might be an integral harmonic component of the circadian rhythm, and may constitute a fundamental rhythm of wakefulness rather than of sleep (Broughton 1989). The

10 72 A. A. Borbkly and P. Achermanri proposition was related by the author to the two circadian activity peaks in animals. Whereas there is ample evidence for a bimodal distribution of sleep propensity in subjects living in the usual 24-h world, it is less clear that this pattern is generated by an endogenous rhythm. A polymodal distribution of sleep is typical in early childhood. As development proceeds and both waking and sleep episodes increasingly consolidate, the polymodal pattern becomes bimodal and eventually monomodal. A rise in sleep propensity near the middle of the waking episode may represent a remnant of the early polyphasic sleep pattern. Midday sleep propensity may be enhanced by the relaxation following lunch (the post-lunch dip), social habits (siesta), and high environmental temperature. These factors may be responsible for the considerable variability in the occurrence and timing of this phenomenon. The bimodal sleep propensity observed in free-running schedules (Strogatz 1986, p. 92) could be equally attributed to the subjects tendency of structuring their days and maintaining a 3-meal-per-day pattern even under these conditions. Recent results cast doubt on the generality of the bimodal pattern. Thus the analysis of alertness and performance under a forced desynchronization schedule did not reveal the presence of a midday dip when the influence of prior wakefulness and sleep was assessed by folding the data at the 2X-h period of the subjects sleep/wake schedule (Dijk eta/. 1992). The constant routine protocol constitutes a stringent test for the endogenous origin of the midday rise of sleepiness. In this schedule, the body posture is kept constant, food and liquid intake occur at short regular intervals, lighting is uniform and information on the time of day is withheld. There was no clear evidence for a biphasic pattern of alertness and performance in young male subjects recorded under constant routine conditions (Dijk et al. 1992). Carskadon and Dement (19x5) reported in an abstract a midday reduction in multiple sleep latency from a test which was performed at 2-h intervals from morning to evening. It is unclear from the report whether factors such as the knowledge of the time of day or the anticipation of the end of the experiment in the evening may have influenced the results. Since the negative findings of the other groups were based on subjective sleepiness measures, it will be important to use objective measures of sleep propensity to resolve this important issue. With the two-process model a polymodal sleep/wake pattern can be simulated by narrowing the interval between the two thresholds (Daan et ul. 1984). Since the level of the upper threshold is assumed to be influenced by the arousal level, its reduction in sleep-promoting situations (e.g. heat, monotony under continuous bedrest; in some subjects with free-running rhythms, see Strogatz 1986, p. 82) is to be expected. Also the bimodal siesta pattern can be obtained without assuming an additional rhythmic process. The three-oscillator model (phase only) accounted in a different way for the bimodal sleep-nap pattern (Kronauer 1987a): it was proposed that the x oscillator must have an important second harmonic influence on the y oscillators. Recently Kronauer and Jewett (1992) described both a circadian and a hemicircadian* body temperature rhythm, and suggested that the latter may underlie the bimodal pattern of sleep propensity. Evidence is presented for independent changes of the two components under various experimental schedules. It is important to realize, however, that the conclusions are derived from a mathematical analysis in which harmonic sine waves are fitted to the temperature data. It has yet to be shown that the hemicircadian component identified by this procedure corresponds to a biological oscillation. 3.2 Multiple napping Campbell and Zulley (1985, 1989) pointed out that under prolonged bedrest conditions multiple naps per day can be observed. A four-per-day rhythm of sleep propensity has been described (Zulley 1988; Broughton et ul. 1990). A multiphasic sleep pattern is typical for the human infant (Kleitman and Engelmann 1953). A prominent ultradian sleep/wake pattern has been reported also for the rat during early development (Alfoldi et ul. 1990). In terms of sleep structure (i.e. slow-wave activity), each ultradian sleep/wake episode showed the characteristics of a mini-day. A straightforward explanation of the multiple napping pattern is the inability to sustain either sleep or waking for prolonged time periods under conditions of extreme monotony (e.g. prolonged bedrest) or in early development. An episodic rather than a rhythmic occurrence of sleep seems to be typical under such conditions. Lowering the upper threshold in the two-process model could simulate a multimodal sleep/wake pattern (Daan et al. 1984; Achermann 1988). 3.3 Wake maintenance zones The term wake maintenance zones was coined by Strogatz and Kronauer (1985) and designates the zones in the circadian cycle (defined by body temperature) in which sleep tends not to begin. Strogatz (1986, Fig. 4.11) described the position of these zones in internally desynchronized subjects at about 8 h before, and about 6h after the temperature minimum. The two zones thus precede the midday nap phase and the major sleep phase. The presence of the two zones of low sleep propensity or of the evening zone alone has been inferred by Strogatz from the reanalysis of ultradian sleep studies, the occurrence of unintended microsleep during a constant routine schedule, the record of a subject entrained to a 23.5-h schedule, and of another subject free running in society (Wollmann and Lavie 1985). Lavie has referred to the wake maintenance zones as * Hemicircadian is equivalent to circasemidian

11 Concepts and models of sleep regulation 73 'forbidden zones' for sleep, and supported this notion by data from ultrashort sleep/wake cycles (for recent reviews see Lavie, 1989; 1991). He refers to the phases of high sleep propensity as primary (i.e. night sleep) and secondary (i.e. midday sleep) sleep gates. In his studies, the evening zone preceding night sleep is characterized by the lowest sleep propensity. It should be realized that the terms 'wake maintenance zone' and 'forbidden zone' suggest an absolute inability to sleep during specific intervals, which is not born out by the data. There are at best intervals of reduced sleep propensity. However, even this proposition needs to be further examined for different schedules. In particular, the constant routine protocols are needed to demonstrate that factors such as structuring the day by three meals, knowledge of time of day, and sleep itself are not major determinants of the bimodal patterns observed. 3.4 Recurrence of slow-wave sleep Gagnon arid Dekoninck (1984) reported a recurrence of slow-wave sleep (SWS) during extended sleep, results that were interpreted in terms of a sleep-dependent 12.5-h rhythm (Broughton et al. 1990). Studies of extended sleep in which sleep was initiated at three different circadian phases. did not confirm a consistent recurrence of SWS (Dijk et al. 1990a, 1991b). EEG slow-wave activity was determined quantitatively in these studies to assess its extent in later parts of extended sleep. The occasional peaks in one of the studies (Dijk et a/. 1991a,b) may represent random variations or could be attributed to prior intermittent waking or to the variable episode durations of nonrem sleep and REM sleep (Achermann et ul., in prep.). The data do not appear to contradict the basic assumptions of the two-process model and do not constitute compelling evidence for the existence of a circasemidian rhythm. 3.5 Multiple circadian oscillators? Presently there is no strong evidence for the existence of multiple circadian pacemakers in the mammalian central nervous system (see 1.1). Daan and Beersma (1992) demonstrated that certain phenomena that have been attributed to multiple osci?lators, may be explained by a single oscillator. Nevertheless, there are observations that seem to defy an explanation on the basis of a single pacemaker. Thus under certain experimental conditions the circadian rest-activity cycle of rodents may split into two separate components that drift apart and may assume a new stable phase relation. In their dual pacemaker model, Beersma and Daan (1992) propose that two identical oscillators that are usually in phase, exhibit a change in their phase-relationship which gives rise to splitting. They suggest that the output of these oscillators interacts in a multiplicative way to determine the probability of activity. In humans, there is no clear evidence for a splitting of the sleep/wake cycle. Strogatz (1986, p. 120) presents records of subjects showing an alternation between split and consolidated modes of sleep, the former being defined as two naps bracketing the interval of the habitual consolidated sleep. According to Strogatz, the relative duration of a nap-sleep pair is more dissimilar than for split sleep. A biphasic sleep pattern has been recently described in humans who were exposed for several weeks to a short photoperiod (Wehr 1991, 1992). Strogatz (1986, p. 186) succeeded with the two-process model (Daan et a/. 1984) to simulate certain features of the alternations of the split and consolidated mode of sleep. Thus a model with a single circadian oscillator is able to generate a realistic bimodal sleep/wake pattern. Nevertheless, other possibilities must be considered. One alternative is the dual pacemaker model described in the preceding paragraph in which the duality of the oscillator manifests itself only under certain conditions. Another possibility is the hemicircadian component proposed by Kronauer and Jewett (1992) whose influence on the body temperature rhythm becomes manifest only under particular circumstances whereas its effect on sleep propensity is assumed to be permanently present. The study of Zulley and Carr (1992) indicates that a biphasic distribution of sleep can be generated in free-running subjects by restricting the main sleep episode below a critical duration. Further experiments, data analyses and simulations are needed to clarify this basic issue. 4 CONCLUDING REMARKS The present overview reflects the increasing importance of the modelling approach in sleep science. Models help delineate the regulating processes underlying such a complex and little understood phenomenon as sleep. and thereby offer a conceptual framework for the analysis of existing and new data. The major models have already inspired a considerable number of experiments. This approach has become particularly attractive by the possibility of using quantitative physiological measures in humans for testing the predictions of a model. Thus EEG slow-wave activity represents the key parameter in the investigation of nonrem sleep homeostasis, while the 'unmasked' core body temperature is being used as the indicator of the circadian process. The interactions between these two processes can be studied by 'conflict experiments' in which their usual phase relationship is experimentally altered. This has been achieved in animal studies (recovery from sleep deprivation during the activity period: Borbely and Neuhaus 1979; Trachsel et ul. 1986) as well as in hurnan experiments (e.g. forced desynchrony: Dijk et nl. 1992; sleep curtailment: Zulley and Carr 1992). Another positive feature of the modelling approach is the fact that the regulatory processes postulated are not restricted to a single species but probably represent basic mechanisms in mammalian sleep. The basic features of sleep

12 74 A. A. Borbdy and P. Achermann homeostasis and its interaction with the circadian process has been shown to be similar in many mammalian species including humans, and quantitative simulations in an animal species have been successfully performed (for a review see Tobler et a/. 1992). Human circadian research and the corresponding models have been continuously related to animal experiments, and in one case the same model was used for simulating human and rodent data (Kronauer 1987a). Again there is little doubt that a considerable part of circadian physiology is invariant across mammalian species. Finally, the first model of the ultradian process underlying the nonrem-rem sleep cycle has been derived from data obtained in the cat. Subsequently, the basic concepts of the model were applied to human sleep (see Table 2). The pervasiveness of the basic sleep regulating mechanisms in mammals raises the problem whether they may be present also in other classes of animals. The homeostatic regulation of the sleep or rest state has already been explored in a bird (Tobler and BorbCly 1988), a fish (Tobler and BorbCly 1985), and even in invertebrates (Tobler 1983; Tobler and Stalder 1988). One of the problems of the modelling approach is the abstract nature of the postulated processes. It is important to explore the structures in the brain in which these processes are generated as well as the underlying neurobiological mechanisms. There is conclusive evidence that the suprachiasmatic nuclei represent the site of the circadian pacemaker. However, it is still unknown how the information originating in this structure modulates the sleep/wake and rest-activity cycles. There is also strong evidence that nuclei in the pons are centrally involved in the periodic generation of REM sleep, although the precise mechanisms remain to be elucidated. The most recent evidence in favour of the reciprocal interaction hypothesis has been summarized by McCarley and Massaquoi (1992). It is still unclear, however, how the processes in the brain stem interact with the forebrain mechanisms of sleep and wakefulness. Also the structures involved in REM sleep homeostasis remain elusive. A final comment relates to the mechanisms underlying nonrem sleep homeostasis. It has been one of the shortcomings of the two-process model that Process S is based on an EEG parameter whose neurobiological origin is obscure. However, recent experiments revealed that hyperpolarized thalamic neurons exhibit fluctuations of membrane potential in the slow-wave frequency range (Steriade et a/ ; see also McCarley and Massaquoi 1992). If, as Steriade and his colleagues propose, these fluctuations are at the origin of EEG slow waves, the mechanisms underlying sleep homeostasis would be open for investigations at the cellular level. This overview focused on the role of modelling for investigating basic sleep regulating mechanisms. However, this work also has implications for applied and clinical sleep research. Models can be used to simulate the duration and intensity of sleep as well as the time course of daytime sleepiness during shift work (e.g. Daan et a/. 1984; Akerstedt and Folkard 1990) and to predict optimal schedules. Models describing the effect of light on circadian parameters (e.g. Kronauer 1990) need to be combined with models accounting for sleep homeostasis. There are preliminary attempts at such combinations (Achermann and BorbCly 1992; Massaquoi and McCarley 1992). These considerations are also relevant for the treatment of sleep disturbances in older people in whom both the homeostatic and circadian facet of sleep regulation may be impaired. The relevance of the modelling approach to sleep in depression has been recognized early. Thus the circadian coincidence hypothesis (Wehr and Wirz-Justice 1980) and the Process S-deficiency hypothesis (BorbCly and Wirz-Justice 1982; BorbCly 1987) have attempted to account in different ways for the altered sleep structure in depression as well as for the antidepressant effect of sleep deprivation. The two hypotheses gave rise to numerous studies which were designed to test their basic tenets. Recently, a related hypothesis has been advanced by Wu and Bunney (1990). One of the gratifying aspects of modelling sleep regulation is the fact that the postulated basic processes seem to be of a very general nature. This has been clear for a long time for the circadian process which, in addition to sleep propensity, determines to a large extent such diverse parameters as core body temperature, the secretion of certain hormones (e.g. cortisol and melatonin), retinal disk shedding, performance and memory. Ultradian changes are not restricted to the alternation of the two sleep states, but involve many other functions controlled by the autonomic nervous system (e.g. thermoregulation, cardiovascular parameters, penile erection). Plasma renin levels have been recently shown to be closely associated with the nonrem-rem sleep cycle (Brandenberger et al. 1988). The homeostatic facet of sleep regulation does not have many associated parameters as yet. The plasma level of TSH is affected by sleep deprivation (Sack et al. 1988), and GH secretion seems to be related to sleep onset (Born ef a/. 1988). Nevertheless, the basic concept of interacting circadian and homeostatic processes has been adopted to account for the regulation of hormones (Van Cauter 1990), body temperature (Nakao et a/. 1990a, 1991), and retinal sensitivity (two-process model of retinal rhythmicity: RemC et al. 1991). To define the relationship between these processes and sleep regulation will be one of the challenging future tasks. ACKNOWLEDGEMENTS We thank Drs Domien Beersma, Serge Daan and Irene Tobler for their comments on the manuscript. The comments of the workshop participants on a draft version of this overview are gratefully appreciated. We thank Ms Annette Kaltbrunner for her help in preparing the manuscript. The authors work was supported by the Swiss National Science Foundation, grant

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E-M model (Daan and Berde 1978) e, = tt - m, +A, sin 1