The process of falling asleep

Sleep Medicine Reviews, Vol. 5, No. 3, pp 247–270, 2001 doi:10.1053/smrv.2001.0145, available online at http://www.idealibrary.com on

SLEEP MEDICINE reviews

PHYSIOLOGICAL REVIEW

The process of falling asleep Robert D. Ogilvie Department of Psychology, Brock University, St Catharines, ON L2S 3A1, Canada KEYWORDS sleep onset, sleep onset period (SOP), Hori nine-stage system, QEEG, ERP, narcolepsy, insomnia, head injury, sleep apnoea

Summary The process of falling asleep can best be measured by considering a convergence of behavioural, EEG, physiological and subjective information. Doing so allows one to see sleep processes as they unfold, but relying on any single sleep index can bias the description of this complex process. The studies reviewed do not support the idea that sleep begins “in a moment”, but rather that entry into sleep is a continuous, interwoven series of changes which begin in relaxed drowsiness and continue through stage 1, often into the first minutes of stage 2. The transition from waking brain to sleeping brain is traced accurately by Hori’s nine-stage EEG system. Event-related potential (ERP) studies map complex changes in information processing as sleep begins, while quantitative EEG investigations have identified important spatiotemporal re-organisations of primary EEG frequencies which take place as one moves from waking to sleeping mode. To consider evidence from multiple levels of analysis, a three step electrophysiological model of central nervous system (CNS) regulation during sleep onset is proposed: initial processes appear to be alpha-related; intermediate processes, poorly studied to date, parallel the development of theta and vertex sharp wave activity, while the processes which terminate wakefulness are sigma sleep spindle-related. Clinical investigations of the sleep onset period in people with narcolepsy, insomnia, depression or sleep apnoea appear to indicate the presence of relatively unique electrophysiological signatures which may be of clinical significance.  2001 Harcourt Publishers Ltd

INTRODUCTION “Whereas it is easy to distinguish between the conditions of alertness, or being wide awake, and definite sleep, the passage from one to the other involves a succession of intermediate states, part wakefulness and part sleep in varying proportions – what is designated in Italian as dormiveglia, or sleep-waking.” (Kleitman, p. 71) [1]. “A succession of intermediate stages”, “part wakefulness and part sleep” – the opening sentences of Kleitman’s chapter on “The Onset of Sleep” have

Correspondence should be addressed to: Robert D. Ogilvie. Fax: (905)688-6922; E-mail: [email protected] 1087–0792/01/030247+24 $35.00/0

a great deal to offer current students of the sleeponset process or period (SOP). Those words were informed not only by Kleitman’s work and that of his students, but by the very earliest EEG-based work on sleep onset, that of Davis et al. [2, 3]. In those classic papers, Davis et al. asked their subjects to differentiate “floating” from “real sleep” by using button presses to identify when they had emerged from either state. They concluded that “real sleep” accompanied the spindles of stage C (modern stage 2), and developed from “floating” somewhere in stage B (low voltage modern stage 1, without alpha). In their 1938 paper, they likened the difficulty of pinpointing sleep onset to that of determining the moment of death. Perhaps it was that bleak comparison that persuaded most who followed them  2001 Harcourt Publishers Ltd

R. D. OGILVIE

248

to essentially ignore the wake–sleep (W/S) transition until well into the modern era (i.e. postAserinsky and Kleitman) [4].

DEFINING SLEEP: SLEEP AS A BEHAVIOUR Those who regularly use EEG machines or polysomnography to measure sleep parameters sometimes lose sight of the fact that, fundamentally, sleep is a behaviour. Prior to the development of the technology to amplify and record spontaneous human EEG activity and measure it during waking and sleeping [2], sleep was detected and defined solely on the basis of: (a) a species-specific body posture; (b) maintained behavioural quiescence; (c) elevated arousal threshold; and (d) state reversibility with stimulation (recapped beautifully by Flannigan) [5]. This definition is still useful today, even though we can refine descriptions of sleep by measuring the many changes in brain and body physiology that mark its onset and its intriguing path through the night [6]. Since the latter patterns are usually measured continuously, they are often more convenient and more accurate than casual observation based on Flannigan’s criteria. However, refined behavioural determination of waking or sleeping continues to be important.

“Active” behavioural detection of sleep Work by Ogilvie and Wilkinson [7], showed that randomly sequenced faint tones could be used effectively as simple probes, essentially asking, “Are you awake?” The person entering the sleep onset process could respond “yes” simply by pressing a microswitch, and failed responses were taken as evidence of entry into sleep. MacLean’s lab [8] at Queen’s University showed that vibrotactile stimuli could be used for essentially the same purpose. These papers were based on earlier studies which had established a relationship between lengthening reaction time and EEG changes as drowsiness increased [9, 10]. But concerns were expressed that such “active” behavioural systems might interfere with the initiation of sleep. As one might predict, these behavioural measures also correlated with physiological and performance indices quite well. Ogilvie and Wilkinson [7] showed that reaction time using the above active behavioural

tool correlated moderately with abdominal respiration amplitude during drowsy wakefulness and into stage 1 “sleep” (r=−0.43). These indications of frequent voluntary microswitch closures (“I’m awake”) during stage 1 sleep led those authors to question the long-standing notion [11] that stage 1 represents true sleep. They thought it more accurate to describe stage 1 as part of the transition into sleep or the sleep onset period (SOP), which is terminated by the appearance of stage 2, with its unique spindles, K-complexes and behavioural quiescence. Others have examined EEG and behaviour closely as people fall asleep. In a series of brilliant studies beginning in 1984, Dr Tadao Hori and associates found that reaction time correlated closely with EEG changes during the wake–sleep transition [12].

“Passive” behavioural measurement of sleep Other research showed that passive behavioural systems could be used for detecting sleep. Two approaches emerged: actigraphy and continuous pressure systems.

Actigraphy Wrist or body movement is much more dramatic and continuous during wakefulness than sleep. This fact underlies the development of a convenient, portable measure of waking and sleeping – the wrist actigraph. While generally useful in identifying large blocks of sleep, actigraphs are not precise enough to help us describe sleep onset, for they cannot distinguish between the quiescence which marks this period and actual sleep. Put another way, actigraphy generally overestimates sleep (by underestimating sleep onset latency) [13].

Continuous pressure systems Kleitman and his students examined the relationship between muscle relaxation and EEG activity, noting that a hand-held spool was dropped between 0.5 and 25 s after alpha had disappeared [14]. That would place the disappearance of finger muscle tone about midway through traditional stage 1 sleep. Essentially that idea was instrumented in the 1980s so that light continuous pressure on a microswitch could be used to signify ongoing wakefulness; involuntary release of the switch as muscles relaxed during sleep onset would provide another useful

THE PROCESS OF FALLING ASLEEP

behavioural index of sleep [15–17]. Ogilvie [15] found that there was generally a correlation between the active and passive behavioural measures of sleep onset. It was found, however, that “passive” release of the thumb switch typically occurred before response cessation to the “active” task, even when measured simultaneously. (However although these passive systems yield an apparently “digital” index, they are in fact the product of a more continuous decrement in EMG; when the decrease reaches a “set point”, the spool is dropped or the microswitch released.)

DEFINING SLEEP: EEG CHANGES AND SLEEP Sleep onset: the changing concept of sleep stages Since the present focus is upon defining and detecting sleep, the following descriptions will be limited to those questions. The very earliest sleep studies detected differences in the cortical rhythms accompanying wakefulness and sleep. Loomis et al. [18, 19] were the first to conduct extensive comparisons of waking and sleeping EEG. From their examination of 84 recordings made during sleep and 116 obtained in wakefulness, they differentiated five types of cortical electrical activity which spanned most of the wake–sleep continuum. Their categories are very similar to those in use today. The three relevant stages follow:

Loomis, Harvey and Hobart (1937) “Stage A – Alpha. Alpha rhythm appearing in trains of various lengths. The eyes may be slowly rolling, under closed eyelids, . . . (this stage would contain both waking and early sleep activity by more current criteria). Stage B – Low voltage. A quite straight record, with no alpha rhythm and only low voltage changes of potential. Rolling of the eyes may occur. Stage C – Spindles. Line slightly irregular with 14 per second spindles of 20–40
V every few seconds.” Loomis et al. [20] (pp. 133–134).

Dement and Kleitman (1957) The first widely accepted changes to the Loomis stage system were proposed in 1957. The new stage 1 essentially combined the earlier stages A and B (thereby actually reducing the ability to distinguish

249

waking from sleep). The first spindles marked the beginning of Dement and Kleitman’s stage 2 sleep [21].

Rechtschaffen and Kales (1968) The publication of the now famous 57-page scoring manual, edited by Rechtschaffen and Kales [11], formalised their immediate predecessor’s “wake – stage 1 – stage 2” treatment of the sleep onset process. Their description of these stages was more detailed: Stage W: Alpha activity and/or low voltage, mixed frequency EEG; blinks and rapid, darting eye movements. Relatively high EMG. Stage 1: Low voltage, mixed frequency EEG, predominance of 2–7 Hz activity; less than 50% alpha activity. No spindles, K-complexes or REMs found; rolling eye movements and vertex waves often present. Stage 2: Sleep spindles and K-complexes; low voltage, mixed frequency EEG; may contain up to 20% delta waves (Delta >75
V, and <2 Hz activity).

Sleep onset: finer gradations The Rechtschaffen and Kales [11] scoring system, with its 30 s scoring epochs, works quite well when one is interested in characterising the macro-structure of a typical 7–8 h of nocturnal EEG activity, but is unsatisfactory for determining the microstructure of the SOP. The EEG dynamics shift much too rapidly as someone falls asleep, and important moment-by-moment changes are lost in the “averaging” process which takes place when one must find one label to describe the more than half-dozen oscillations that sometimes transpire within 30 s as sleep is approached. A common example is the waxing and waning of alpha activity just before and after stage 1 is entered. By just checking to see whether the percentage of alpha activity is over or under 50% (as required to distinguish stage W from stage 1), the scorer ignores potentially interesting micro-oscillations along the arousal continuum.

Roth (1961) Roth [22] described four stages of reduced vigilance and their EEG correlates. Most noteworthy was his validation of these stages by studying responses to photic stimulation. Building on the Loomis et al. stages A and B, Roth described them thus: Stage 1, disintegrated alpha, corresponds to the earlier stage A, or wakefulness; photic stimulation produces alpha blocking.

250

In stages 2(a), (b) and (c), (approximate subdivisions of the Loomis stage B) Roth’s patients were reactive to adequate stimulation, but were unresponsive in stage 3. Stage 2(a) characterised by EEG flattening; alpha blocking decreases. Stage 2(b) 5–6 Hz waves, 10–40
V in amplitude; photic stimulation produces paradoxical effects – alpha increases. Stage 2(c) 3–4 Hz, 50–80
V; occasional vertex waves; more paradoxical alpha increases with visual stimulation. Stage 3 sleep spindles and “clinical sleep”; some vertex waves. Unresponsive to photic stimulation (although K-complexes can be exogenously triggered at this time). Although these substages represented a clear advance over existing classifications, they were not widely accepted.

Hori, Hayashi and Morikawa, (1994) These authors [12] subdivided standard stages W, 1 and 2 into nine EEG-based sequential stages which are passed through as one travels down the SOP from waking to unmistakable sleep. There are two subdivisions of stage W, six sequences within stage 1, and their stage 9 corresponds to the beginning of standard stage 2 sleep. Because these stages are fleeting, the epoch length chosen for scoring them is only 5 s long. The Hori et al. stages are identified by the following characteristics: “Stage 1. Alpha wave train: Epoch composed of a train of alpha activity with minimum amplitude of 20
V. Stage 2. Alpha wave intermittent (A): Epoch composed of a train of more than 50% of alpha activity with a minimum amplitude of 20
V. Stage 3. Alpha wave intermittent (B): Epoch contained less than 50% alpha activity with a minimum amplitude of 20
V. Stage 4. EEG flattening: Epoch composed of suppressed waves less than 20
V. Stage 5. Ripples: Epoch composed of low-voltage theta wave (20
V<0<50
V) burst suppression. Stage 6. Vertex sharp wave solitary: Epoch contained one well-defined vertex sharp wave. Stage 7. Vertex sharp wave train or bursts: Epoch contained at least two well-defined vertex sharp waves. Stage 8. Vertex sharp waves and incomplete spindles: Epoch contained at least one well-defined vertex sharp wave and one incomplete spindle (duration <0.5 s, amplitude <20
V, >10
V). Stage 9. Spindles: Epoch contained at least one welldefined spindle at least 0.5 s in duration and 20
V in amplitude.” (Hori et al. [12]).

R. D. OGILVIE

These stages are also exemplified by the EEG samples in Figure 1, which is reproduced here with permission. The Hori et al. [12] system is particularly useful to those interested in precise descriptions of the SOP because it has been validated in a number of ways. The Hori group found the EEG changes to be very systematic throughout the SOP. Hori et al. made this many distinctions because they found good behavioural, subjective and physiological evidence for doing so. They found a prefect ordinal relationship between reaction time and their nine stages; mean reaction time increased as each of stages H1 through H9 was entered. Similarly, when asked if they had been awake or asleep immediately prior to arousals from each stage, 82.5% said “awake” in stage H1, decreasing linearly to 26.2% during stage H9. “Asleep” responses also preserved the ordinal relation of the stages perfectly, increasing from 7.2% in stage H1 to 43.7% in stage H9. (Note the high levels of perceived wakefulness and corresponding low levels of perceived sleep throughout traditional stage 1 “sleep”! More than 50% of their subjects still perceived themselves to be awake as H9 – standard stage 2 sleep – was entered!) They also found interesting differences in the type of hypnagogic activity that accompanied entrance into several of their sleep stages, and found that the amount of hypnagogic activity peaked in the middle of the SOP, in stage H5. Unfortunately, no scoring system is perfect. Accurate detection of Hori’s stages H1, H2, and H3 may be hampered in people in whom alpha levels are suppressed, either naturally or because of disease or medication. (Similar problems also exist when using the R and K system.) Also, while the 5 s epoch used in the Hori system is far superior to 30 s epochs in detecting brief fluctuations in arousal, any fixed epoch length is somewhat artificial and the signals within epochs are not completely stationary. This can cause frequency bias during automated analyses, especially if an adequate number of samples (epochs) is not averaged. And of course scoring 5 s epochs is extremely time consuming and is therefore impractical in many situations. At present, we would suggest using Hori scoring only when there is good experimental or clinical reason for doing so. It is presently being adapted for clinical use by our group, but it is too soon to say whether the unique electrophysiological “signatures” we have seen for several clinical groups (see pp. 42–48) have diagnostic significance.

THE PROCESS OF FALLING ASLEEP Sleep stages

251

EEG Stages 1: Alpha wave train

Wake 2: Alpha wave intermittent (>50%)

3: Alpha wave intermittent (<50%)

4: EEG flattening

5: Ripples Stage 1 6: Vertex sharp wave solitary

7: Vertex sharp wave bursts

8: Vertex sharp wave and incomplete spindles Stage 2

9: Spindles

50 µV 1 sec

Figure 1 The Hori nine-stage EEG-based system for identifying successive EEG changes throughout the sleep onset period. Reproduced with permission.

That being said, in our opinion the Hori ninestage system represents the greatest advance in understanding the SOP yet achieved. In our experience, these nine stages are almost invariably passed through in the order specified, although H4 – flattened EEG – sometimes does not last a full 5 s and is therefore difficult to score (Doerfling et al. [23]), and individual differences, found in EEG as in other physiological and psychological dimensions, can make scoring difficult. Discontinuities are rarely encountered, even following brief awakenings in good sleepers, but there is as yet no work on whether disruption of the stage sequence or factors such as sleep deprivation influence subsequent instances of any stage. There is insufficient data at present to judge the clinical efficacy of these classifications, but they appear to provide a very promising way to examine sleep in those for whom sleep onset processes may be disrupted, i.e. for those suffering from narcolepsy or insomnia.

Comparing the above EEG-based “stage” systems Since all these not-so-blind men were describing the same “elephant”, it may be useful to consider

the similarities and differences among their descriptions of the process of falling asleep. The common elements are that every system begins with descriptions of (declining) alpha activity being associated with increasing sleepiness or drowsiness and ends by calling the appearance of spindle activity the end of the sleep transition period or the unquestionable beginning of sleep. Alpha is associated with wakefulness and relative alertness is described essentially in terms of the relative amount of alpha present in the EEG tracings. However, Dement and Kleitman [21] and Rechtschaffen and Kales [11] are content to equate falling alpha (<50%) with the beginning of sleep, while earlier [20], contemporary [22, 24] and later [12] descriptions prefer a more conservative or later starting point (spindles) for locating the true beginning of sleep. Two opposite perspectives can be identified in the above comparison of classification systems. First, one’s choice of sleep onset definition may well depend upon whether it is most important to detect early signs of sleep or to be quite certain that sleep has arrived. Second, those who attend to the EEG microstructure of standard stage 1 and correlated behavioural changes (Roth and Hori’s group) tend

252

to prefer to call the appearance of spindles definitive evidence of sleep. It is the clear and systematic description and validation of the EEG microstructure of stage 1 “sleep” that makes Hori’s contribution such an important one. This latter position is also supported by a number of papers from our laboratory and others [7, 8, 25–27].

Computerised EEG analysis: non-stagebased exploration of the SOP EEG spectral analysis Power spectral analysis is a quantitative method of EEG analysis which decomposes the complex waveforms into their constituent frequency components. Lubin et al. [28] the first to use spectral analysis to study the sleeping and waking transition, found that delta and sigma frequencies were most helpful in differentiating between these states, while the inclusion of other frequencies did not improve the discrimination. Hori [29] reported spatiotemporal changes in EEG activity during the SOP. He reported on average power spectral values following the beginning of stage 1 sleep, and found that variability in delta, theta and alpha power increased as stage 1 was entered and that the increases continued for approximately 10 min into sleep. Regional differences in power across the cortex were maximal 7 min after stage 1 had begun. Ogilvie et al. [25] using their active behavioural system to identify increasing drowsiness (increasing response times) and sleep (response failure), detected significant changes in each of the standard five EEG frequencies as sleepiness increased and sleep was entered. Theta power increased, alpha and beta decreased during the SOP, and power in all frequencies increased at behavioural sleep onset, indicating the beginning of the cortical synchronisation usually attributed to the onset of slowwave sleep. This broad-band power increase was most likely due to the use of behavioural rather than EEG criteria for identifying sleep onset. Badia et al. [30, 31] chose to examine a narrower slice of the SOP. They used brief, consecutive 5 s epochs and single Hz bands from 3 to 25 Hz to study the transition from wakefulness into stage 1 sleep. For this purpose, they chose a 3 min sample of EEG to be of interest; specifically, they defined the W/S transition as being with the last 1.5 min of wakefulness and the first 1.5 min of stage 1. (This would correspond to Hori’s H2–H3 transition.)

R. D. OGILVIE

They reported that the largest single-Hertz decreases in relative spectral power (from greatest to lesser) were at 10, 9 and 11 Hz, respectively, and were greatest in the occipital cortex. The greatest increases in power were for the 3 and 4 Hz bands in frontal and central recording sites. These changes occurred within a context of fluctuations rather than smooth increases or decreases, with the fluctuations being most obvious in the delta frequency range. The above analyses suggest that there are dramatic, dynamic, and systematic processes to be found within the relatively brief time it takes one to fall asleep. Whether measured in single Hz or by the traditional five EEG bands, significant changes in power are seen in virtually all frequencies, often within a few moments. Topographic or spatiotemporal analyses and the study of coherence patterns will help us understand these dynamic systems even more clearly.

EEG spatiotemporal changes throughout the SOP Once again, Dr Hori and colleagues at Hiroshima University have conducted most of the work in this field. Following Hori’s [29] paper, Hayashi et al. [32] were the first to report that the distribution of alpha activity changed over the cortex (measured at FP1,2, Fz,3,4,7,8, C3,4, Pz,3,4, T5,6 and O1,2), as sleepiness increased. They stated that variations in slow and in fast alpha frequencies give a good indication of level of sleepiness. Tanaka et al. [33] reported that alpha dominance moves from posterior occipital sites to anterior ones (FP1, FP2, Fz, F7, F8) during the hypnagogic or SOP. They noted that their central/peripheral sigma index changed sharply during the Hori stage H7 EEG. Morikawa et al. [34, 35] used a factor analytic approach to describe EEG changes during a similar time period. In both studies, they recorded from the following sites: F7,8, C3,4, Pz, FP1,2, T5,6, O1,2. In the first, they found that spatiotemporal changes began before stage 1 had started and continued several minutes into stage 2 sleep. Morikawa et al. [35] separated the alpha band into three sub-bands (alpha 1, 7.7–9.4 Hz; alpha 2, 9.6–11.4 Hz; alpha 3, 11.6–13.4 Hz) which they compared to delta, sigma and theta activity during the SOP. Principal component analysis validated their trisection of the alpha band. The alpha-2 band showed the frontal dominant and occipital dominant pattern, while the alpha-3 band showed a frontal and parieto-occipital dominant pattern. The shift

THE PROCESS OF FALLING ASLEEP

from waking to sleep-like spatial patterns occurred 2 min after stage 1 began for the alpha-1 band and 3 min after stage 1 for the alpha-3 and sigma bands. The delta, theta and alpha-1 bands displayed only a single frontal-dominant pattern in the hypnagogic period. They interpreted their data as suggesting that sleep begins before stage 2 (spindle) activity. Also in 1994, when the papers from a Sleep Onset Conference in Niagara-on-the-Lake, Canada, were published, Hori’s group [12] and Hasan and Broughton [36] independently confirmed that alpha dominance switched both location (posterior to anterior) and frequency. Hasan and Broughton, recording from FP1,2, Fz,3,4,7,8, T3,4,5,6, Cz,3,4, Pz,3,4, O1,2, reported that the frontal alpha of drowsiness was slower (8 Hz) than the alert alpha seen more posteriorly (9.5 Hz) and that the two types of alpha have different dipole locations and orientations. Tanaka et al. [37] confirmed and extended their earlier-noted differences in spatiotemporal distribution of the three alpha bands. They also linked the anterior dominance of the alpha-3 band to the appearance of vertex waves (Hori stage H6). These hypnagogic space–time changes (which they equated with the beginning of the SO process) began even before standard stage 1 sleep had begun. In the above study, Tanaka et al. [37] recorded from FP1,2, Fz,7,8, C3,4, Pz, T5,6 and O1,2. In their most recent paper, Hori et al. [38] examined topographic and EEG coherence functions simultaneously using FP1,2, Fz,7,8, C3,4, Pz, T5,6 and O1,2. They confirmed their earlier finding that with increasing sleepiness leading to sleep, the dominant alpha area moved along the midline from posterior to anterior cortical areas. Two major and different patterns of coherence were detected. One posterior coherence pattern was associated with wakeful EEG, while a different, anterior coherence pattern was found for sleep EEG activity. Sleep spindle components had a third, parietal focus. Focusing specifically on spindle frequency activity, Werth et al. [39] detected a bimodal distribution within the 10 to 15 Hz range during stage 2 sleep in some individuals. Recording from F3, C3, P3, O1 and A2, they found the 11–12 Hz peak to be maximal at F3–A2, while the 12.5–13.5 peak was more widely distributed and dominant at C3–A2, P3–A2, and O1– A2. Using an automatic spindle detection system while recording from Fp1, Fz,3,4,7,8, T3,4,5,6, Cz,3,4, Pz,3,4, and O1,2, Zeitlhofer et al. [40], also noted individual variability and described slow (11.5–14 Hz) and fast

253

(14–16 Hz) spindles as having quite different cortical distributions. Their slow spindles were predominant at frontal electrodes, while the faster ones were seen most clearly at midline central and parietal sites. While defining spindle activity [39] and spindles [40] differently, both papers agreed generally on the cortical location of the fast and slow activity and both suggested that the differences in cortical distribution were due to unique generators for these subdivided sigma frequencies.

EEG coherence analysis in the SOP During the same time frame, coherence analysis has been used effectively to help summarise the immense amount of information made available by quantitative EEG techniques (coherence or correlation analysis between two EEG sites provides information about the functional association between brain sites, by calculating the degree to which signals covary within a given frequency range). In an early study, Morikawa et al. [41] (using combinations of F3, C3, P3, and O1 electrodes) found that delta band coherence between occipital and frontal, and central and parietal sites dropped sharply just before alpha disappeared. They interpreted the finding as indicating that the hypnagogic state may begin before alpha vanishes. The same researchers [42], using 12 scalp electrodes (Fp1, Fp2, F7, F8, Fz, C3, C4, Pz, T5, T6, O1, O2) referenced to A1–A2, reported decreases in alpha coherence 2–3 min before the onset of standard stage 1, and detected significant increases in Fast Fourier Transform (FFT)-based sigma coherence beginning before stage 2 and continuing to increase for several minutes after spindles lasting 0.5 s were found. The latter finding suggests that sigma dynamics (which include both rhythmic and non-rhythmic components) develop over a number of minutes starting when, by visual criteria, the rhythmic (spindle) components are barely perceptible (late in stage 1, or in Hori’s stage H8), and ending in stage 2 when they are fully developed in amplitude and duration. Thus coherence analyses are seen to track sigma frequency dynamics at the traditional stage 1–2 transition quite precisely. Tanaka et al. [43, 44], using Hori’s nine stages as a reference, reported that delta and theta coherence rose during Hori stage H6 in anterior-central areas. This indicates that these slow wave cortical coherence increases are associated with the onset of vertex wave activity. For the alpha-2 frequency,

254

there were two principle components, one associated with frontal, the other with posterior regions – both of which were maximal during wakefulness. Alpha-3, however showed a rise in coherence in stage H6 and rose higher in stage H9, when spindles were maximal. As they suggest, their alpha-3 might better be considered a low frequency spindle component rather than fast alpha activity. Their coherence data show more clearly than do spectral power data, that anterior synchronous activity is related to sleep EEG while posterior components reflect waking EEG processes.

Summary What do these papers tell us? Using a variety of analytical and statistical procedures, a consistent spatiotemporal pattern has emerged. Perhaps most important is the finding that the changes in alpha activity which have been known in outline for so long, are much more complex than Loomis et al. could have ever imagined. We now know that the occipitally prominent resting 8–12 Hz alpha of relaxed/drowsy wakefulness is better examined in at least two sub-bands which show different topographic foci and dipoles (generators?) as sleepiness increases (in late stage W, through stage 1) and turns to definite sleep (evidenced by the spindle activity of early stage 2). So while alpha frequencies can truly be said to show spatial and temporal dynamic change during sleep onset, the slower frequencies only vary temporally. Delta and theta activity, while increasing dramatically in power as the SOP continues, do not appear to show topographic variation. Spindle frequencies also do not move spatially, appearing most prominently in the parietal area. These impressive spatiotemporal changes also underscore the importance of using multiple electrode sites to study the micro-structure of sleep. C3 and C4 give a good overview of W/S/W progression, but by summing anterior and posterior activity, for instance, provide no hint of the important dynamic shifts in alpha activity as sleep begins.

DEFINING SLEEP: EVENTRELATED POTENTIAL (ERP) CHANGES AND SLEEP There is yet another very fruitful way to examine the electrical activity of the brain during sleep onset.

R. D. OGILVIE

By averaging about 1 s of EEG activity immediately following the onset of each of a series of identical (or very similar) stimuli, the time-locked eventrelated potentials (ERP) – the signal – can be extracted from uncorrelated or background brain activity – the noise. ERPs are usually used to study changes in information processing during waking activity, but the technique can be easily adapted to depict cognitive changes as we fall asleep. ERPs consist of a series of peaks labeled according to their order and polarity. P1-N1-P2-N2 are the so called long-latency ERPs which are most affected by changes in arousal and attention. The first people to look at auditory evoked responses during sleep were Williams et al. [45]. They discovered that the potentials they obtained were quite distinctive for different sleep stages. But it was Ornitz et al. [46] who first examined clickinduced potentials at the wake–sleep transition. They reported large increases in N2 amplitude late in stage 1 or at the onset of stage 2 sleep; changes which were in fact greater than any seen as a result of stage shifts within sleep. They interpreted this finding as evidence for a facilitatory or decreased inhibition phenomenon; one that might also reflect a change of state. Wilkinson et al. [47] found that changes in auditory evoked responses during wakefulness were related to changes in vigilance; they noted that significant increases in N2 amplitude were associated with lowered vigilance (undetected signals). The data are consistent with the Ornitz [46] data if one assumes that lowered responsivity is reflected in both. More recently, Noldy et al. [48] examined changes in the N1-P2-N2 components of the ERP at sleep onset. They found that N1 decreased while P2 and N2 increased in amplitude as stage 1 was traversed. Reviewing their work, Campbell, Bell and Bastien [49] concluded that the amplitude of the N1 component consistently reflected sleep onset tendencies, or changes in the degree of arousal or “consciousness”. In the same laboratory, de Lugt et al. [50, 51] showed that during stage 1, as response time slows, N1 amplitude is attenuated. When the subject fails to respond in stage 1, N1 is at baseline. Ogilvie et al. [25] used response rate to faint tones to separate an individual’s responses into five bins – alert, relaxed, drowsy, very drowsy, and asleep. Compiling ERPs for each bin, they detected changes in mean amplitude for six of seven components measured. N1 and P300 decreased significantly from alertness to drowsiness, and again

THE PROCESS OF FALLING ASLEEP

255

P300 BR BIN 1 (FAST)

BR BIN 2

BR BIN 3

P1 N1 N2

P3

P2

BR BIN 4 (SLOW) N3 BR BIN 5 (SLEEP)

55 µV

0

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.79 1.99 Time (in seconds)

Figure 2 ERPs averaged across subjects as a function of lengthening response latencies [or decreasing behavioural responses (BR)] [four categories of increasing sleepiness, from fast to slow, and response failure (sleep)]. Note the similarity between slow (or deeply drowsy) and early sleep ERPs. Reproduced with permission [12]. n=9.

as sleep (response failures) began. The huge increases in N2 amplitude detected earlier [46, 47] were confirmed again, and N3 and P3, which tended to increase with increasing drowsiness, showed significant increases as sleep began. As Figure 2 shows, the overall shape of the very drowsy ERP more closely resembles that of sleep than of alertness. Assuming that waking and sleeping ERPs both reflect cognitive activity or information processing, it is clear that tremendous changes in cognition are to be found at this time. It is equally clear, however, that information continues to be processed during entry into sleep – albeit very differently. Harsh et al. [26], used an odd-ball ERP paradigm, where the sleepy volunteer is asked to respond to the infrequent (odd-ball) target stimuli while ignoring the more frequent ones. This technique is known to generate large P300 waves to odd-ball stimuli during wakefulness. (P300 is an attentiondependent ERP component that is only elicited

when the subject actively detects a rare “target” stimulus. It will not be elicited if the subject fails to detect the stimulus, either because they are instructed to ignore it, or if they are distracted by a secondary task.) Harsh and colleagues found that the P300 wave largely disappeared as responsiveness decreased and ceased, and noted that a new component, the N350, appeared at this time, bracketed by positive peaks (P220 and P450). They found that this new complex, the P220-N350-P450 system, dominated the late ERP activity as sleep began. They postulated that the N350 represents an inhibitory process related to the onset of sleep. Harsh and Peszka [52] reported on a series of four experiments using variants of the odd-ball paradigm wherein they again confirmed that P300 is largely absent by the time alpha has completely disappeared (end of the Loomis et al. stage 1a sleep) and suggest that the active monitoring associated with waking P300 activity is not possible during sleep. So while work in Campbell’s and Ogilvie’s labs focused on changes in waking ERPs as sleep begins, Harsh’s work points towards a different type of information processing, beginning in the wake-tosleep transition zone. Cote [53] studied changes in the amplitude and scalp distribution of P300 odd-ball activity during wakefulness and stage 1 sleep. She also used changes in response rates to the odd-ball stimulus to “bin” the obtained ERPs according to behavioural response latency or sleepiness. P300 amplitude remained high to detected (responded to) stimuli during stage 1 sleep, but the scalp distribution of maximal activity shifted laterally from the parietal area during wakefulness to lateral parietal and occipital areas in stage 1. No P300 was observed in stage 1 for undetected tones. The significant attenuation of frontal ERPs during stage 1 was interpreted to mean that frontal contributions to consciousness are the first to disappear as sleep is entered. Thus if P300 odd-ball responses provide some evidence of consciousness or awareness of the external world, the latter can be seen to detected stimuli during stage 1 “sleep” – but not to undetected ones. During stage 2, where stimuli were very seldom detected, P300 was essentially absent and very similar to ERP plots for undetected tones from stage 1. This study extends and supports earlier behavioural findings suggesting that stage 1 is a transitional stage: Cote has provided evidence that ERP “signatures” – physiological evidence of

R. D. OGILVIE

256

information processing – fluctuate in stage 1 between waking and sleeping (stage 2) profiles depending upon whether or not the subject is in touch with his/her environment enough to respond to a simple stimulus. Cote and Campbell [54] also showed that P300 activity, absent in stage 2 and slow-wave sleep, returned during REM sleep, suggesting perhaps that something approximating waking information processing accompanies REM sleep. The work confirms and extends that of Niiyama et al. [55], and Bastuji et al. [56] who concluded that sensory discriminations are possible in REM and stage 1 sleep. Winter et al. [57] also used an odd-ball paradigm to study another form of information processing during sleep, this time calculating Naatanen’s [58] mismatch negativity (MMN), a difference waveform calculated by subtracting the ERP to the frequent stimulus from that of the odd-ball stimulus. This results in a negativity from about 100 to 250 ms following stimulus onset. If this waveform measures sensory information processing [58], it should be interesting to see if and how it changes as sleep begins. Winter et al. [57] found that the MMN of drowsiness (stage 1) was different from that observed in wakefulness, and again from stage 2, concluding that in stage 1, N1 and MMN summate. Again, using a different ERP phenomenon, the MMN, it would seem that information processing continues, perhaps, as Winter suggests, as a hybrid of waking-specific and stage two-specific processes. Recent work by Nittono and colleagues [59] at Hiroshima have broken down the MMN process into smaller SOP sequences using Hori’s nine-stage system. They found a clear but progressively decreasing MMN to deviant tones during Hori’s stages H1, H2 and H3, but noticed that the negativity had disappeared (and was actually replaced by positivity) during Hori’s stages H4 through H9.

Summary From the above evidence, it seems that MMN vanishes with the disappearance of the last traces of alpha activity, relatively early on in the SOP. This contrasts to the time-course for the disappearance of P300 activity, representing conscious attention [52, 53], which does not disappear until response cessation – usually found during Hori stages H4 to H8. This discrepancy in timing suggests that these major measures of waking cognitive processing are

dissociated during traditional stage 1 sleep. This temporal dissociation has significant implications for understanding waking cognitive functioning as well as cognitive activity as sleep begins. Niiyama et al. [55], and Cote and Campbell [54] have shown that wake-like P300 potentials return in REM. From this we can infer that attentional processes are alive and well in stage 1, as sleep is entered, and return during REM sleep. We have also seen new ERP wave-form components appear which seem to be sleep-specific (e.g. Harsh’s N350), but the cognitive correlates of these potentials are more difficult to establish.

DEFINING SLEEP: OTHER PHYSIOLOGICAL INDICES While this review is mainly aimed at integrating behavioural, EEG and ERP studies of the transition into sleep, there are a number of other physiological markers which must be briefly examined to round out the discussion.

Respiratory changes Many years ago Magnussen [60] noticed the rapidity with which respiratory patterns changed as sleep approached and arrived. Timmons et al. [61] studied drowsiness and found that when alpha and mixed frequency EEG changes were seen to alternate, breathing patterns also changed. Ogilvie and Wilkinson [7] discovered that behavioural, EEG and respiratory activity changed as sleep began, but the measures seldom did so at precisely the same time. We also know from clinical evidence that the disordered breathing of sleep apnoea often begins to appear before sleep spindles develop. Going several steps further, Trinder and colleagues [62] recently conducted an important series of experiments on changes in respiration during the sleep onset period. Knowing that ventilation drops as sleep begins, they examined a number of factors as the dominant EEG pattern shifted from alpha to theta and back to alpha. They confirmed that decreases in ventilation accompanied alpha-to-theta EEG (Hori’s H3 to H5 transition) and found complex patterns of upper airway resistance changes in the normal males they studied. Increases in airway resistance were coincident with the reductions in ventilation, both of which reversed rapidly when theta-to-alpha dominant EEG shifts indicated a return to wakefulness. Their most recent paper

THE PROCESS OF FALLING ASLEEP

[63] showed that the activity of the diaphragm and the tensor palatini muscles, high during alpha dominance, dropped significantly when theta waves took over, indicating that the drop in ventilation seen as EEG-defined sleep begins is the result of the combined decrease in output of respiratory pump and upper airway muscles. All of this is consistent with Naifeh and Kamiya’s [64] conclusion that the sleep onset process is closely linked with the neural control mechanisms which regulate respiration.

257

activity (summarised above) could equally well be mentioned here, for their dependent measures most often involved EMG activity from respiratory system muscles. Since they demonstrated an intimate relationship between respiratory EMG activity and EEG changes in the sleep onset period, it would appear to be good practice to record from these muscles when attempting to characterise EMG (or respiratory) activity as sleep begins.

Slow eye movement (SEM) activity Cardiovascular activity Heart rate is generally thought to decrease gradually as sleep begins, but Pivik and Busby [65] found decreases in heart rate about 30 s before stage 1 sleep was entered by preadolescents and saw further decreases 30 s after stage 2 sleep had begun. In a fascinating paper, Spielman and colleagues [66] used infrared spectroscopy to study intracerebral hemodynamics as sleep began and ended. They found that a significant drop in oxygenated hemoglobin very frequently paralleled the disappearance of alpha activity soon after traditional stage 1 sleep would be scored, or using Hori’s system, at the H3-H4-H5 transition. This change was quite sudden and short-lived (about 3.6 s) but the authors think it may represent a change in set point of cerebral blood flow. They found reciprocal effects at sleep offset. Their findings tie in nicely with the timing noted by the Trinder group for EEG and respiratory activity one paragraph above.

Electromyographic (EMG) changes EMG changes as sleep begins are not as well characterised as one might imagine, given that virtually all research and clinical sleep studies record EMG from at least one site. Submental EMG, most commonly used to help identify REM sleep, is useful for that purpose but does not contribute much to the understanding of the wake–sleep transition, for decreases in submental muscle tone appear too gradual to provide a reliable direct index of sleep onset. That said, the reader is reminded that gradually decreasing muscle tonus provides the mechanism for passive behavioural sleep devices. Kleitman’s spool was dropped when the thumb and forefinger muscles reached a critical level of relaxation sometime during stage 1. Work from Trinder’s lab [62, 63] on respiratory

Although SEMs are known to begin in drowsy wakefulness, to continue through stage 1, and to end about the time spindles appear, only a limited number of investigations have examined their potential to detect drowsiness and sleep. Ogilvie, McDonagh and Stone [67] were interested in the relationships among SEM, EEG, respiration and behavioral evidence of wakefulness or sleep. They found that SEM density peaked in the last minute prior to behavioral sleep (response failure), and that it was a useful co-indicator of drowsiness when examined with the above indices in multivariate analyses. They concluded that SEMs, when combined with EEG and behavioural measures, increase one’s confidence of identifying the sleep onset period and defining the process of sleep onset. In the same year, Torsvall and Akerstedt [68] sought to use EEG and SEMs to establish criteria for extreme sleepiness while people were engaged in monotonous work. They discovered that SEMs were most frequent within a minute prior to dozing off episodes, and concluded that both SEMs and increases in alpha and delta power were useful predictors of impending task failure. In a third study using EEG, behavioural and SEM measurements, Hiroshige and Miyata [69] also reported that SEMs were prominent at the transition between wakefulness and stage 1 sleep and were associated with button presses which signified sleepiness. They noted that SEMs began to disappear as sleep was entered, and were absent when slow wave sleep commenced. More recently, Kinnari et al. [70] found that SEM activity correlated more highly with visual reaction time than with any of their EEG measures when studying drowsy OSA patients. One might begin to wonder whether every physiological system interacts with all others during sleep onset, for, as if to complete the circle, Rittweger and Popel [71] detected coherence

258

among the cycle times and phases of SEMs and respiratory rhythms during the sleep onset period. They concluded that these rhythms were not identical, but thought that their temporal similarity might be explained by a common brainstem mechanism.

DEFINING SLEEP: MENTAL ACTIVITY AT SLEEP ONSET In a classic study of this issue, Foulkes and Vogel [72] found that mental activity changes over the SOP as a function of changes in stage or physiological activity. Awareness of being in the laboratory and of having control of the thought process declined as people moved from alpha activity concurrent with rapid or waking eye movements, through a phase where alpha was accompanied by slow eye movements, through the equivalent of stage 1 and into stage 2 sleep. Dreamlike mentation increased over the same time frame. Hayashi et al. [73] contrasted hypnagogic mentation with five of Hori’s EEG stages during sleep onset. People were interrogated every minute as they moved towards sleep. Whether anything was recalled and the recalled content were recorded and classified as “hypnagogic imagery” if visual, auditory or kinesthetic images were reported; as “thinking” if no sensory imagery was verbalised. They found significant, progressive decreases in thinking from 50.5% during H1 and 2, through H4, H5, H6 and 7, to 2.5% in H9. Surprisingly, hypnagogic imagery remained essentially constant (22–36%) throughout this period. Images of people, objects and colored patterns increased linearly, while dream-like experiences changed in a non-linear manner, being maximal in H4 and minimal in H6 and H7. They concluded that the content of hypnagogic mentation changes as a function of hypnagogic EEG (or Hori sleep onset stages). In the same lab, Michida et al. [74] compared ERPs with and without hypnagogic imagery. They found that reaction times and EEG amplitudes were similar between conditions, suggesting that there were no differences in arousal level. They did find a significant decrease in N3 negativity when imagery was present, suggesting that there was reduced attention to the tone at that time.

Sleepiness Up to now, we have been concerned with describing the sleep onset period and the process

R. D. OGILVIE

of falling asleep using behavioural and physiological measures. However, sleepiness is most often defined subjectively and is operationalised by such tools as the Stanford Sleepiness Scale (SSS) (Hoddes et al. [75]) or the Visual Analogue Scale, (Akerstedt and Gillberg [76]). There are also a number of well-established correlations between subjective sleepiness and performance and EEG measures [77]. For simplicity here, the poles of the seven-point SSS scale may suffice to describe sleepiness. By circling a “1”, the respondent indicates “feeling active and vital; alert; wide awake” while circling a “5” means “fogginess; beginning to lose interest in remaining awake; slowed down”, “6” indicates “sleepiness; prefer to be lying down; fighting sleep; woozy”, and a “7” signifies that the person is “almost in reverie; sleep onset soon; lost struggle to remain awake”. Interestingly, when Ogilvie, Wilkinson and Allison [77] had people vocalise their SSS rating whenever faint tones were sounded as sleep approached, people often drifted into sleep while rating themselves at “5”, or “6”. Such people knew they were nearing sleep, but were not aware just how close behaviorally and EEG-defined sleep really was. This confirms the Bonnet and Moore [78] finding that the perception of sleep often does not begin until the sleeper has been in stage 2 for several minutes. So if subjective sleepiness and sleep perception were to define the end of the sleep onset period, sleep onset would often continue well past the first sleep spindles of stage 2.

SUMMARY: INTEGRATING THE BASIC FINDINGS: THERE IS NO “MOMENT” OF SLEEP ONSET Figure 3 was constructed in an effort to help draw together much of the discussion to this point. On the far left, all measures indicate wakefulness, and on the right, each indicates sleep. But from first glance it is apparent that the behavioural, EEG and other physiological indices, viewed simultaneously, do not line up so as to indicate a “moment” or “point” when sleep begins. On the contrary, several measures suggest graded changes during this time. Active behavioural responses, several EEG measures, at least five ERP components, many physiological indicators, and evidence of mental activity in

THE PROCESS OF FALLING ASLEEP

the SOP all indicate gradual changes as one moves through Hori’s nine EEG stages. Although frontal hemodynamics, respiratory ventilation, perhaps skin potential and some additional physiological measures do change abruptly, they seldom change at the same point on this arousal continuum. So a “pointof-onset” analysis, however parsimonious, simply does not fit the facts as well as the more complex approach of recognising that at sleep onset, a number of semi-independent, yet interactive forces are at play. Even the measures of changes in conscious processes (or CNS activity, if you prefer) exhibit considerable variability. Some mental processes and their correlates appear to change continually (thinking, dreaming), as do behavioural responding, Hori’s EEG stages and ERP components (e.g. N1, P300). Other variables do suggest a more discontinuous process (passive behavioural response, mismatch negativity) but also fail to agree as to just when the discontinuity takes place. A number of the physiological indices represent autonomic system functioning as sleep begins, but here too, there is a lack of uniformity. However, more early signs of sleep (i.e. changes as stage 1 begins) can be seen. Respiratory ventilation [79], the transitory drop in oxygenated hemoglobin, some heart rate decrease [65], lowered EMG [80] and changes in skin potential activity [81] line up roughly with the disappearance of alpha, or more accurately, occur within a fairly narrow range of Hori stages – stages H2 to H4. However slow eye movements (SEMs) begin in waking, peak near behavioural SO (usually after alpha has vanished), and disappear early in stage 2 [67].

DEFINING SLEEP ONSET AND SLEEP The above discussion is a review of the systems and processes which are altered as one moves from relaxed wakefulness to definite sleep. Quite simply, everything changes. There is not a single human physiological, cognitive, subjective or behavioural system which is unaltered by the process of entering sleep. And many of these profound changes occur before sleep actually begins. This being so, it is no exaggeration to state that understanding sleep onset processes may be as important as understanding sleep itself.

259

The need to re-define stage 1 “sleep” as part of the sleep onset period Where does all this lead us? Sleepiness precedes sleep. No argument there. Sleepiness begins gradually during wakefulness and ends with the beginning of sleep. No argument there, either. So why is it so difficult to also conceive of the entry into sleep as a gradual process? (Thirty years of using the Rechtschaffen and Kales (1968) Scoring Manual notwithstanding.) The evidence which has been presented suggests unequivocally that waking and sleeping processes overlap as one enters sleep. Figure 3 shows 23 important wake–sleep indices (more could be identified), most of which disagree with each other if each is used to identify a “point” at which sleep begins. Yet all 23 provide useful tools for describing the process of falling asleep. The compelling evidence above leaves little doubt that sleepiness continues through stage 1 and does not end until spindles establish what we all agree is sleep. In view of the fact that behaviours, physiological signs and subjective data in stage 1 all show what we have come to consider “sleep-like” characteristics, our preference is to think of the sequence of changes beginning even before stage 1 is traversed and ending about the time spindles are seen, as representing the gradual, progressive entry into sleep which we perceive as ever-deepening sleepiness. Early signs of sleepiness begin well before stage 1 is detected and continue until spindles define undisputed sleep. That being so, more formal description and definition of this sleep onset period is long overdue. In fact, it could also be argued that sleep itself cannot be adequately defined until the processes leading to it are better understood. (The interested reader is referred to the following books relevant to sleep onset processes: Sleep Onset: Normal and Abnormal Processes [82]; Sleep, Arousal and Performance [83]; Sleep, Sleepiness and Performance [84]: The EEG of Drowsiness [85].)

Re-defining the sleep onset period To recap briefly, entry into the SOP can be identified by any of a number of contiguous changes from waking mode. One may begin to notice early signs of sleepiness or off-peak performance, or a slowing of cognitive functions. If one were in a sleep lab or clinic, concurrent appearance of alpha activity, increased SEMs and blinking, and slower reaction

260

R. D. OGILVIE

Figure 3 Simultaneous theoretical plot of behavioural, EEG, ERP, physiological, and mental activity as sleep begins. Referring to Hori’s nine stages, note that changes between definite wakefulness and definite sleep appear organised around changes in the proportion of alpha, around the appearance of theta and vertex sharp waves, and finally, around the appearance of sigma-spindle activity. (The Hori system can be seen in Figure 1.)

THE PROCESS OF FALLING ASLEEP

time (among other indicators) would be seen. As Hori’s nine stages are traversed, a number of indices correlated with the disappearance of alpha activity would identify early signs of sleep, but gradual changes in many others would signify that the process of entering sleep was continuing. By the time sleep spindles and K-complexes appeared, only the enduring subjective perception of being awake would suggest a link to wakefulness. With the arrival of spindles, then, sleep could be said to begin.

Defining sleep Since sleep is a behavior, it seems logical to begin with its behavioral characteristics and then discuss other aspects of a working definition of sleep. You will recall that four criteria were given by Flannigan [5] for identifying sleep: (a) species-specific behaviour; (b) maintained behavioural quiescence; (c) elevated arousal threshold; and (d) state reversibility. Adopting a sleep-compatible posture (a) and reducing activity (b) are generally preconditions for sleep and sleep onset and so do not help us distinguish one from the other [86]. Similarly, state reversibility (d) takes place during both states. Only (c), elevated arousal threshold, is directly relevant to distinguishing sleep from the sleep onset period. Bonnet and Moore [78] showed that auditory arousal thresholds rose sharply by 30 dB within 1 min of the commencement of spindle activity, indicating that criteria (c) is met soon after H9 or stage 2 sleep is established. Thus elevated arousal threshold would seem to provide another way of distinguishing the entry into sleep from sleep itself. Electrophysiologically, sleep begins after the SOP has been terminated by the appearance of sleepspecific sleep spindles and K-complexes, oscillates with a 90-min periodicity through non-REM and REM stages, and ends with a return to wakefulness. Like the sleep onset process, the return to wakefulness is a gradual process – that is, the sleep inertia period (SIP) is in some ways analogous to the SOP, through the SIP is by no means a simple reversal of the path traced during sleep onset. Describing sleep inertia in detail is beyond the scope of this paper, and to date the SIP has not received as much attention as the sleep onset period has. However, we do know enough to be able to support the idea that it is roughly analogous to the SOP. For instance, it is known that sleepiness takes up to 90 min to be replaced by full waking alertness,

261

and that reaction time improves steadily over the same time course [87]. Following this reasoning, defining sleep, or what we will call “true sleep”, for sake of argument, amounts to the business of distinguishing sleep from sleep onset and sleep offset. With “true sleep” thus bracketed by the SOP and the SIP, a daunting problem can be solved, and solved very satisfactorily. Sleep stages 2, 3, 4 and REM compose “true sleep”, for none has a waking parallel, and all meet the strict behavioural requirements of Flannigan’s fourpoint definition. The gradual transitions to and from true sleep are best left to discussions of sleep onset and sleep inertial processes.

A three step electrophysiological model of CNS regulation during the sleep onset period Although sleep is a behaviour, the key to the CNS activity which results in sleep onset can best be understood by simultaneously studying the correlates of this process across many levels of investigation. In that regard, Hori’s nine stages have been particularly important by providing an orderly framework with which to study the SOP, but it might be useful to condense the sleep onset interval into three phases for present purposes. The alpharelated changes (H1–H4) describe the beginning of the SOP and of course also bracket the transition into the traditional stage 1 “sleep”. Theta and vertex sharp waves (H5–H8) dominate the middle portion of the onset period. Sigma sleep spindle-related changes (H8–H9, and also signaling standard stage 2) are the most important markers of the end of wakefulness. (Please refer again to Figure 3 during the elaboration of this model.)

(A) Initial processes: alpha-related changes Earlier discussions confirm that important changes take place early in the SOP (stages H1–H4), in every domain measured, as alpha levels fall from maximal and disappear. Changes correlated with diminishing alpha include; behavioural, EEG, ERP, physiology, mental activity and subjective reports. As occipital alpha dominance decreases, a slower frontal predominance becomes visible [36]. Yet rather than marking the beginning of sleep, the foregoing review suggests that these changes represent the beginning of an ongoing transformation.

262

(B) Intermediate processes: theta and vertex sharp waves Somewhere in the middle of the sleep onset period, both active and passive behavioural responding cease for most people. In the ERP domain, P300 activity vanishes, and the sleep-related N350 waveform appears. Decreased ventilation is associated with theta activity and SEMs are maximal at this time. These important events are paralleled in the EEG by a predominance of theta frequencies, (H5) followed by the arrival of vertex sharp waves (H6– H8) (the latter being the first unique waveform as sleep is approached and entered). Yet the correlates of theta and vertex sharp wave activity during the SOP remain the most infrequently studied of any major wave forms. Consequently, while the early and late periods of the SOP can be described with some certainty, intermediate processes are not presently as well understood. There is a clear need for expanded basic and clinical research studying these central processes of the SO transition.

(C) Terminal processes: sigma sleep spindle-related changes and the beginning of real sleep Late in the SOP, spindle-related changes (H8–H9) have been observed across many levels of measurement. The EEG becomes relatively non-reactive to photic stimuli [22], although exogenously-triggered K-complexes can be generated. Soon, endogenous K-complexes or the precursors thereof are visible. By the time incomplete spindles are beginning to form (H8), both active and passive responding is virtually suspended. Waking ERP indices have been greatly transformed, SEMs drop, heart rate decreases further, and reports of thinking are drastically reduced. As spindles and K-complexes begin to dominate the EEG record, delta frequencies appear (though often below scorable 75
V amplitudes). Within moments, auditory thresholds rise rapidly [6, 7, 15, 76, 77], giving us perhaps the clearest behavioural indication that sleep has begun.

Sleep onset mechanisms Since the study of neurophysiological, neuroanatomical and neurochemical sleep mechanisms is an ongoing one, it follows that attempts to characterise CNS processes during sleep onset at this point in time will need to be provisional and subject to periodic updating. Indeed, since sleep and sleep onset are so effectively studied at many

R. D. OGILVIE

levels of analysis, one might wonder where on the macroscopic – reductionistic dimension the greatest explanatory power is to be found. It is the author’s belief that at present a very clear picture can in fact be painted of the physiological and behavioural dimensions reviewed so far. That said, however, it may be useful to briefly review some of the neural correlates of the progression from wakefulness to sleep. In general terms, the movement from wakefulness to sleep can be described at the level of EEG analysis as a move from desynchronised towards more synchronised EEG patterns. EEG synchronisation is thought to result when excitatory and inhibitory post-synaptic potentials become activated with increasing simultaneity, resulting in the appearance of an EEG containing a more limited variety of frequencies. As a consequence of increasingly simultaneous activation, characteristic increases in amplitude are soon detectable. Although synchronisation attains its highest levels during slow wave sleep, it begins much earlier. Ogilvie et al. [25] found that delta and theta amplitudes increased significantly through the SOP and at the same time, alpha decreases were seen during most time periods. Of course the advent of another synchronised waveform, spindle activity, is the hallmark of the beginning of sleep and the end of the wake– sleep transition. Thus, we will consider the cortical and subcortical generators which influence alpha, theta, delta and sigma sleep spindle activity as representing at least in outline, the neuroanatomical bases and neurophysiological influences on these developing cortical rhythms and hence upon the onset of sleep. Although the alpha rhythm was detected over 70 years ago, its links with brainstem systems remain poorly understood [88]. There is good agreement that occiptal alpha rhythms are generated in the posterior cerebral cortex [89, 90]. This has been confirmed more recently by neuromagnetic imaging [91], but links with lower CNS centers are less clear. Moreover, the recent discovery of more anterior alpha activity during drowsiness [36] or during the transition from H3 to H4 activity [12], adds to that complexity. Hasan and Broughton [36], using source dipole analysis, identified different foci for these alpha phenomena. The well-known posterior 9.5 Hz alpha rhythm has its dipole beneath the occipital region, while the slower (8 Hz), drowsiness-related activity is more anterior, with its

THE PROCESS OF FALLING ASLEEP

dipole located deep in the brain under central cortex. The different behaviours, frequencies and generators associated with these alpha phenomena suggest different mechanisms and functions for these activities. While much research has been directed at understanding the regulation of posterior alpha, no experiments, other than the dipole analyses of Hasan and Broughton, have been directed at explaining the onset of the slower, more anterior alpha – which is clearly very important to an understanding of sleep onset processes. In a related paper, Inouye et al. [92] distinguished an endogenous (primarily occipitally focused), posterior alpha from an anterior, more widely distributed (exogenous) alpha activity in waking subjects. Inouye found endogenous and exogenous alphas to have separate generators. The disappearance of the occipitally focused alpha rhythm, dominant in drowsiness and disappearing early in the sleep onset process is an important milestone in the transition into sleep, but the waxing of a slower, more frontal alpha activity, just as occipital alpha rhythms are waning, may be even more critical. Only more research will fully unlock the correlates of H3–H4 anterior-moving alpha changes. Michel et al. [93] used an FFT dipole approximation to localise the generators of beta, alpha, theta and delta frequencies in 13 resting participants. They found the delta source to be most anterior and deepest, while theta was more posterior and shallower than the delta generating area. In agreement with papers mentioned above, alpha was located most posteriorly and originated higher in the cortex than did other generators. Two beta frequencies appeared to emanate anterior to the posterior alpha source. So it seems reasonable to conclude that the generators of the primary sleep-related EEG frequencies are distributed two-dimensionally over different cortical areas and that their sources also appear to differ in depth. We will now examine each in more detail. Borrowing heavily from Steriade and McCarley [88], a summary of what is known about the origins of theta waves in animals may illuminate the study of theta activity in the sleep onset period. Hippocampal origins of theta activity have been known at least since the 1980s [94, pp. 206], but more recent work suggests that many limbic structures contribute theta frequencies, indicating that coupled limbic oscillators may be responsible for these frequencies

263

during waking and sleep. In rats and cats, strongly synchronised hippocampal theta is an important index of REM sleep, and less rhythmic theta is a dominant frequency in human REM EEG as well. More work is needed on the emergence of theta activity midway through the SOP. As already mentioned, increases in delta activity begin in the SOP, but understanding delta has been complicated recently by evidence that this slow frequency band actually contains three distinct sets of oscillations within it [95]. These authors report on the existence of a <1 Hz slow, cortically generated wave form, a “clock-like” 1–4 Hz thalamic oscillation, and another cortical rhythm oscillating at 1–4 Hz. The slow delta waves appear in the EEG as sequences of surface-negative waves brought about by hyperpolarisations from neurons at lower cortical levels. Surface-positive waves take the form of K-complexes, which are generated by the coordinated actions of large numbers of cortical neurons. Most interestingly, these slow oscillations appear to have the ability to trigger and organise temporally, the thalamically generated spindles and other delta oscillations. They found the slow oscillation only during slow-wave sleep, with no traces of it during waking or REM, and found it to be widely distributed across the cortex. Amzica and Steriade [95] hypothesise that the wide spread distribution of delta frequencies over the cortex argues for delta being an intrinsic property of cortical neurons, particularly of large, pyramidal neurons. They reason that the clock-like delta frequency oscillations in thalamo-cortical neurons is distinct both anatomically and probably functionally from the other delta oscillations, and further, that the activity of thalamo-cortical neurons may not produce measurable changes in cortical EEG activity. The genesis of spindle waves, whose appearance marks the end of the SOP, is quite well understood [88]. Thalamo-cortical links have been established for some time and spindle activity has been linked with the blockage of ascending input from the thalamus to the cortex (equipping them nicely to be the decoupling mechanism between the cortex and the environment which is so essential to the onset of true sleep). Steriade’s work [88 (p. 210)] shows the reticular thalamic nucleus to be the spindle pacemaker. Waxing and waning depolarisations in the reticular thalamic nucleus match simultaneous thalamocortical hyperpolarisations showing the characteristic spindle frequencies.

264

In sum, it appears as though the typical waking organisation of CNS and ANS activity changes dramatically within the few moments needed for normal sleepers to fall asleep. The first stage of studying this process is well underway. We are already able to describe the SOP in great detail. How these changes are brought about is quite well known, but not yet fully determined. Work tracing source dipole movement in the SOP [36] is very promising in that regard, as are the studies of changing EEG concordance patterns and both the EEG and ERP spatiotemporal re-alignments which occur at this important transition. More animal studies are needed in which cortical and subcortical sleep systems are monitored as sleep begins. And even more exciting is the prospect of functional magnetic resonance imaging (fMRI) investigations of sleep onset. There, one could look at anatomical and functional interrelationships among brain areas as sleep begins. Although we cannot fully characterise the mechanisms controlling these interwoven processes, work has begun evaluating the clinical significance of the SOP.

THE SLEEP ONSET PERIOD: CLINICAL FINDINGS While far from fully understood, there is enough basic information about the SOP presently available to make it reasonable to look to see if the process of falling asleep is fundamentally different in people with a variety of sleep-related difficulties. The following analysis will be limited to papers which specifically focus on the clinical significance of the process of entering sleep.

Sleep onset and narcolepsy It has been known for a very long time that people suffering from narcolepsy have ultra-short sleep latencies and SOPs which frequently contain REM sleep. This is usually confirmed by both nocturnal and daytime – Multiple Sleep Latency Test (MSLT) – monitoring in a sleep disorders clinic. Unfortunately, however, the microdynamics of this process are seldom studied. A good beginning was made by Broughton and Aguirre [96], when they reported that two types of sleepiness could be observed prior to MSLT naps in people with narcolepsycataplexy. This position was supported by the following evidence. They determined that their

R. D. OGILVIE

patients were sleepier (shorter SO latencies, and higher Stanford Sleepiness Scale scores) prior to REM onset naps compared to NREM naps. This was supported by ERP evidence wherein for REMcontaining naps, there was an increase in P2 amplitude and a suppression of contingent negative variation (CNV) amplitude compared to naps not containing REM sleep. Alloway et al. [97] studied the sleep onset process in 10 drug-free patients with narcolepsy-cataplexy and 10 normal sleepers matched on age and sex. They were interested in whether or not the sleep onset process (defined as patterns of EEG activity occurring in successive quadrants of the SOP) would differ when various nap types were compared. Nap types were defined as naps ending in stage 1, 2 or REM sleep. In other words, the research question was: can we predict nap type by examining EEG patterns during the transition into sleep? Following all-night polysomnographic investigation, participants were given five 20-min nap opportunities the next day in an MSLT paradigm. As expected, nocturnal sleep latencies to stage 1 were significantly shorter for narcoleptics than normals, though no differences to stage 2 onset were observed. In the MSLT testing, narcoleptics again had shorter latencies to stage 1 and spent more time asleep during the 20-min opportunities than normals did. The main FFT/EEG analysis of the MSLT data was a series of nap type by quartile ANOVAs, wherein predicted sleep onset processes would be indicated by significant interactions between the two independent variables. (Time of day effects, examined over successive naps, were not significant in any analysis.) Comparing narcoleptic REM to normal stage 2 naps, significant interactions were found for delta, theta, alpha and sigma frequencies. When narcoleptic REM versus normal stage 1 naps were examined, theta and alpha interactions were significant, and when narcoleptic stage 2 and normal stage 1 naps were studied, interactions were found for delta, theta and alpha frequencies. This indicates very clearly that entry into sleep differs quite dramatically depending on the end state achieved in a brief nap opportunity. Examined another way, Alloway [97] calculated mean amplitude in
V for each of the five standard EEG frequencies for each quartile of the SOP. When normal stage 1, normal stage 2, narcoleptic stage 2 and narcoleptic REM naps were compared, there were no overall differences in amplitude for any

THE PROCESS OF FALLING ASLEEP

frequency when summed across the entire nap period, but significant interactions (again suggesting process differences) were found for delta, theta, alpha and sigma frequencies. Delta amplitudes were roughly similar in the first quartile for all nap types, but rose to the highest level in narcoleptic stage 2 naps. In normal sleepers, alpha was relative high in the first quartile as both stage 1 and stage 2 naps were approached, but remained high during stage 1 naps, while dropping continually across successive quartiles as stage 2 naps were approached. Narcoleptics tended to have lower alpha to begin with, and alpha dropped further over time (quartiles) and did so similarly for both types of narcoleptic naps. Sigma levels for narcoleptic REM naps were lower than those for the other three groups by the second quartile, and remained low, while for the narcoleptic stage 2 naps, sigma levels were higher than all others in the last quartile. The interaction for theta frequencies was significant but presented no clear picture of differences in sleep onset process. Taken together, these data also provide support for the notion that even before it is fully established, sleep begins differently for narcoleptics than for normal sleepers.

Sleep onset and insomnia Freedman [98] was the first to examine quantified EEG activity as insomniacs fell asleep. He looked at 12 sleep-onset insomniacs and age-matched normal sleepers. When wakefulness and stage 1 were compared, he saw that insomniacs had more beta and less alpha than controls and that higher beta persisted into early stage 1 sleep. He found no differences for the other stages. Merica et al. [99] performed a discriminant analysis on the EEG of the SOP, finding that the most useful elements in the analysis were beta and delta components and a beta/delta index. Beta and the beta/delta index were most important for discriminating wakefulness from stage 1, while delta measures were more useful as stage 2 was entered. Lamarche and Ogilvie [100] were interested in the electrophysiological changes which accompany sleep onset, and predicted that there would be different patterns of EEG activity when the entry into the nocturnal sleep of psychophysiological (learned) insomniacs, psychiatric insomniacs and control sleepers were compared. Specifically, they predicted that there would be more sign of hyperarousal in insomniacs

265

than in normal sleepers. After screening 50 potential participants, they reported analyses from six people from each of the three categories who had each spent two consecutive nights in the sleep lab. (The insomniacs were drug-free for a minimum of one week prior to testing.) EEG measures were analysed across successive quartiles of the SOP. There were significant interactions between group and quartile, for delta, alpha and beta frequencies. Generally, there was less change over time (quartiles) in the psychophysiological insomniacs than for the other two groups. This was particularly clear for alpha activity, which was essentially unchanged in the insomniac group, but which was initially much higher in the other groups, dropping by the fourth quartile to a level approximating that of the psychophysiological insomniacs. As expected, delta activity rose as the SOP continued, but did so significantly more rapidly for the psychiatric and normal groups than for the psychophysiological group. Beta activity was initially higher during wakefulness in the psychophysiological group on night 2, decreasing in stage 1 and further in early stage two at a sharper rate than seen in the other groups. In most analyses, the psychiatric insomniacs were between psychophysiological and normals, but their EEG activity was much closer to that of the normal sleepers. So those with psychophysiological insomnia take a different route when falling asleep than do normals or psychiatric insomniacs. Lamarche and Ogilvie [100] speculated that those with psychiatric insomnia may have near-normal sleep onset mechanisms because their insomnia is secondary to their psychiatric abnormalities, rather than being the primary disorder as in the psychophysiological group. The route taken by those with severe learned insomnia also appears different from that described earlier for narcoleptics and for depressed people, though the data for depressed individuals are not directly comparable. Bonato’s [101] dissertation examined issues similar to those just discussed. He studied 15 drugfree chronic psychophysiological insomniacs and 15 matched controls for three successive nights. Period-amplitude analyses detected significant decreases in beta, sigma and alpha activity and increases in theta and delta activity. Beta values were found to be higher in the first 15 min of stage 2 in the insomniac group. Unlike Lamarche and Ogilvie [100], Bonato found higher alpha throughout the SOP, and suggested that alpha intrusion may be an

266

indicator of an impaired sleep onset mechanism. Insomniacs were also seen to produce lower amounts of delta activity and to show more stage reversals prior to stage 2 onset. He found an interesting dissociation between EEG amplitude and incidence such that neuronal activity begins to slow down before significant increases in EEG amplitude can be observed. (This separation of amplitude from frequency cannot be detected by the more familiar FFT analyses.) This EEG slowing appears to be a good measure of sleepiness leading directly to sleep.

Falling asleep and depression Armitage and her laboratory [102] were among the first to look at the microstructure of sleep onset in a clinical population. They used digital period analysis (another form of quantitative EEG analysis) to study sleep onset EEG in eight normal sleepers and 28 people with major unipolar depression. They defined the moment of sleep onset as being, “the 2 s epoch in which the delta half-wave zero-cross and power showed a sharp sustained 30% or greater increase over baseline . . .” (p. 190). With this definition, they found that the moment of sleep was more clearly detected in normal than depressed individuals, and that delta and theta power were significantly higher in the 30 s surrounding SO in normal sleepers. The depressed people’s delta waves were also significantly reduced in amplitude when compared with controls. They also discovered that both fast and slow wave activity were elevated in the right hemispheres of their depressed patients during sleep onset.

Sleep onset in the sleep clinic Another group of Canadian researchers has begun to study sleep onset in people with sleep apnoea in an effort to distinguish their sleep onset parameters from those of other common clinical groups. So far, the work has been a retrospective analysis of a large data base from a sleep clinic. Chilcott et al. [103] attempted to differentially characterise the SOP of 77 people who had had both nocturnal and MSLT sleep examinations. This included 20 with sleep apnoea, 10 with periodic limb movement (PLM), 10 with upper airway resistance syndrome (UARS), eight with narcolepsy, 18 with non-specific excessive daytime sleepiness (EDS), and 18 normal sleepers. They chose to study latency and density measures patterned after those suggested by Hori’s

R. D. OGILVIE

scoring system. They were surprised to discover few group differences in nocturnal sleep parameters, but discovered that a number of MSLT discriminations could be made. Group differences were found for SEM latency, alpha density, vertex latency and number of epochs of stage 1. The EDS and sleep apnoea groups had reduced SEM latency compared with normal sleepers; alpha density was higher in narcoleptics than in those with sleep apnoea or EDS, and PLM people had higher alpha density than those with sleep apnoea and EDS. Also, normal sleepers had shorter latencies to the first vertex wave than did those with sleep apnoea, while people with UARS had longer vertex latencies than those in all but the sleep apnoea group. In general, different patterns of sleep onset fragmentation could be seen among these subgroups, and when combined with the above variations in latencies to physiological milestones, these parameters could increase the versatility of the MSLT as a diagnostic tool for a wider range of sleep disorders. At the same time, such assessments should continue to improve our understanding of the SOP.

Sleep onset and mild head injury (MHI) Williams [104] looked at the electrophysiological properties of the SOP in nine people with MHI and an equal number of matched controls. Two models were tested and rejected: that MHI people would show sleep patterns similar to those with idiopathic insomnia, or that their entry into sleep would resemble that of those with psychophysiological insomnia. Instead, Williams found significantly lower beta (or cortical arousal) throughout the SOP, but more interestingly, found that when FFTs were calculated on all frequency bands, there were strong group differences in the variability of power over the SOP. Since increased variability in power suggests increased magnitude of oscillation in arousal as sleep entry is attempted, the findings may well explain why those with MHI have trouble falling and staying asleep. Large oscillations in arousal are incompatible with entering sleep rapidly and with maintaining sleep after it is finally entered.

Summary: clinical SOP studies: what’s known so far Clinical studies of the SOP have just begun, but already there are a number of promising findings.

THE PROCESS OF FALLING ASLEEP

First and foremost, it appears likely that people with different sleep disorders may enter sleep differently, i.e that there may be relatively distinct “SOP signatures” for many common sleep disorders. If continued investigations confirm this pattern, it may be that studying this transition carefully (rather than discarding it as a “noisy” period before important sleep parameters can be obtained) may have real sleep-diagnostic value. Perhaps it already has. Practice Points Look for any and all of: stage 1, 2, or REM onset. Different processes regulate each of those stages, therefore different potentially useful clinical information may be extracted from each. Latencies to stage 2 may have diagnostic value, for stage 1 and stage REM latencies are already known to be important markers of disordered sleep.

Research Agenda Vertex sharp waves are the first waves seen as sleep begins that are not seen during wakefulness. Why is it that these waves have been so little studied? As the harbinger of sleep, is it not just possible that they could have something important to tell us about the process of falling asleep? Similarly, theta genesis and correlates are poorly understood . . .

ACKNOWLEDGEMENTS I would like to thank Dr Kimberly Cote and Dr Tomoka Takeuchi for reading earlier drafts of the paper. Support was provided by NSERC Canada.

REFERENCES ∗1. Kleitman N. Sleep and Wakefulness. Chicago: University of Chicago Press 1963. 2. Davis H, Davis PA, Loomis AL, Harvey EN, Hobart G. Changes in human brain potentials during the onset of sleep. Science 1937; 86: 448–450.

∗ The most important references are denoted by an asterisk.

267

∗3. Davis H, Davis PA, Loomis AL, Harvey EN, Hobart G. Human brain potentials during the onset of sleep. J Neurophysiol 1937; 1: 24–38. 4. Aserinsky E, Kleitman N. Regularly recurring periods of eye mobility, and concomitant phenomena, during sleep. Science 1953; 118: 273– 274. 5. Flanigan WF. Behavioral states and electroencephalograms of reptiles. In: Chase MH (ed.). The Sleeping Brain. Los Angeles: UCLA Brain Information Service/Brain Research Institute 1972. 6. Ogilvie RD. Sleep onset. In: Carskadon M, Rechtschaffen A, Richardson G, Roth T, Siegel J. (eds). Encyclopaedia of Sleep and Dreaming. New York: MacMillan 1993. 7. Ogilvie RD, Wilkinson RT. The detection of sleep onset: behavioral and physiological convergence. Psychophysiology 1984; 21: 510–520. 8. MacLean AW, Arnedt T, Biedermann H, Knowles JB. Behavioural responding as a measure of sleep quality. Sleep Res 1992; 21: 105. 9. Morell LK. EEG frequency and reaction time – a sequential analysis. Neuropsychologia 1966; 4: 41–48. 10. Liberson WT, Liberson CW. EEG records, reaction times, eye movements, respiration and mental content during drowsiness. Rec Adv Biol Psychiat 1966; 8: 295–302. 11. Rechtschaffen A, Kales A. (eds). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Los Angeles: UCLA, Brain Information Service/Brain Research Institute 1968. ∗12. Hori T, Hayashi M, Morikawa T. Topographic EEG changes and the hypnagogic experience. In: Ogilvie RD, Harsh JR (eds). Sleep Onset: Normal and Abnormal Processes. Washington: American Psychological Association 1994. 13. Hauri PJ, Wisbey J. Wrist actigraphy in insomnia. Sleep 1992; 15: 293–301. 14. Blake H, Gerard RW, Kleitman N. Factors influencing brain potentials during sleep. J Neurophysiol 1939; 2: 48–60. 15. Ogilvie RDD. Behavioural and Physiological Investigations of Sleep Onset. Unpublished Ph.D. dissertation, University of Cambridge, UK 1985. 16. Perry TJ, Goldwater BC. A passive behavioral measure of sleep onset in high-alpha and lowalpha subjects. Psychophysiology 1987; 24: 657– 665. 17. Viens M, De Koninck J, Van den Bergen H, Audet R, Christ G. A refined switch-activated time monitor for the measurement of sleeponset latency. Behav Res Ther 1988; 26: 271–273.

R. D. OGILVIE

268

18. Loomis AL, Harvey E, Hobart GA. Potential rhythms of the cerebral cortex during sleep. Science 1935a; 81: 597–598. 19. Loomis AL, Harvey E, Hobart GA. Further observations on the potential rhythms of the cerebral cortex during sleep. Science 1935b; 82: 198–199. 20. Loomis AL, Harvey E, Hobart GA. Cerebral states during sleep, as studied by human brain potentials. J Exper Psychol 1937; 21: 127–144. 21. Dement WC, Kleitman N. The relation of eye movements during sleep to dream activity: an objective method for the study of dreaming. J Exper Psychol 1957; 53: 339–346. 22. Roth B. The clinical and theoretical importance of EEG rhythms corresponding to states of lowered vigilance. Electroenceph Clin Neurophysiol 1961, 13: 395–399. 23. Doerfling P, Ogilvie RD, Murphy T, Lamarche C. Applying the Hori sleep scoring system to the examination of the sleep onset process in insomniac and normal sleepers. Sleep Res 1996; 25: 123. 24. Johnson LC. A psychophysiology for all states. Psychophysiol 1970; 6: 501–516. ∗25. Ogilvie R, Simons IA, Kuderian RH, MacDonald T, Rustenburg J. Behavioral, event-related potential (ERP), and EEG/FFT changes at sleep onset. Psychophysiol 28: 1991; 54–64. 26. Harsh JR. Event-related potentials during the wake-to-sleep transition. In: Ogilvie RD, Harsh JR (eds). Sleep Onset: Normal and Abnormal Processes. Washington: American Psychological Association 1994. 27. Cote KA, Ogilvie RD. Identifying sleep and wakefulness: a comparison of behavioural and polysomnographic methods. J Psychophysiol 1994; 8: 305–313. 28. Lubin A, Johnson LC, Austin MT. Discrimination among states of consciousness using EEG spectra. Psychophysiol 1969; 6: 122–132. ∗29. Hori T. Spatiotemporal changes of EEG activity during waking-sleeping transition period. Inter J Neurosci 1985; 27: 101–114. 30. Badia P, Wright KP Jr, Wauquier A. Fluctuations in single-hertz EEG activity during the transition to sleep. In: Ogilvie RD, Harsh JR (eds). Sleep Onset: Normal and Abnormal Processes. Washington: American Psychological Association 1994. 31. Wright KP Jr, Badia P, Wauquier A. Topographical and temporal patterns of brain activity during the transition from wakefulness to sleep. Sleep 1995; 18 (10): 880–889. 32. Hayashi M, Morikawa T, Hori T. Topographic mapping of EEG with the variation of sleepiness.

33.

34.

35.

∗36.

37.

38.

39.

40.

41.

42.

43.

44.

Memoirs of the Faculty of Integrated Arts and Sciences III Hiroshima Univ 1989; 13: 61–71 (Japanese; English abstract). Tanaka H, Hayashi M, Hori T. Topographical Analysis of the Hypnagogic EEG. Memoirs of the Faculty of Integrated Arts and Sciences III Hiroshima Univ 1993; 19: 111–122. Hori et al., 1991. (Japanese; English abstract.) Morikawa T, Hayashi M, Hori T. Spatiotemporal changes of background EEG activity during hypnagogic period. Japanese Journal of Electroencephalography and Electromyography 1993; 21: 328–336 (Japanese; English abstract). Morikawa T, Hayashi M, Hori T. Spatiotemporal variation of EEG topograms in hypnagogic period. Japanese Journal of Electroencephalography and Electromyography 1994; 22: 360–368 (Japanese; English abstract). Hasan J, Broughton R. Quantitative topographic EEG mapping during drowsiness and sleep onset. In: Ogilvie RD, Harsh JR (eds). Sleep Onset: Normal and Abnormal Processes. Washington: American Psychological Association 1994. Tanaka H, Hayashi M, Hori T. Topographical characteristics and principal component structure of the hypnagogic EEG. Sleep 1997; 20 (7): 523–534. Hori T, Tanaka H, Hayashi M. Topographic mapping of EEG spectral power and coherence in the hypnagogic period. In: Koga Y, Nagata K, Hirata K (eds). Brain Topography Today. Elsevier: Amsterdam 1998. Werth E, Achermann P, Dijk D-J, Borbely A. Spindle frequency activity in the sleep EEG: individual differences and topographic distribution. EEG Clin Neurophysiol 1997; 103: 535– 542. Zeitlhofer J, Gruber G, Anderer P, Asenbaum S, Schimicek P, Saletu B. Topographic distribution of sleep spindles in young healthy subjects. J Sleep Res 1997; 6: 149–155. Morikawa T, Hayashi M, Hori T. Coherence analysis of the EEG in hypnagogic period. Memoirs of the Faculty of Integrated Arts and Sciences III Hiroshima Univ 1989; 13: 81–89 (Japanese; English abstract). Hori T, Hayashi M, Morikawa T. Topography and coherence analysis of the hypnagogic EEG. Sleep ’90 1990 Bochum: Pontenagel Press: 10–12. Tanaka H, Hayashi M, Hori T. Topographic mapping of electroencephalography coherence in hypnagogic state. Japanese Soc Sleep Res 1998; 52 (2): 147–148. Tanaka H, Hayashi M, Hori T. Topographic characteristics of slow wave activities during

THE PROCESS OF FALLING ASLEEP

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

55.

56.

57.

the transition from wakefulness to sleep. Clin Neurophysiol 1999; 110: 1–11. Williams HL, Tepas DI, Morlock HC. Evoked responses to clicks and electroencephalographic stages of sleep in man. Science 1962; 138: 685–686. Ornitz EM, Ritvo ER, Carr EM, Panman LM, Walker RD. The effect of sleep onset on the auditory averaged evoked response. EEG Clin Neurophysiol 1967; 23: 335–341. Wilkinson RT, Morlock HC, Williams HL. Evoked cortical response during vigilance. Psychonom Sci 1966; 4: 221–222. Noldy N, McGarry P, Campbell K. Late auditory evoked potentials as indicators of sleep onset. In: Koella W, Obal F, Schulz H, Visser P (eds). Sleep ’86 Stuttgart: Gustav Fischer Verlag 1988. Campbell K, Bell I, Bastien C. Evoked potential measures of information processing during natural sleep. In: Broughton RJ, Ogilvie RD (eds). Sleep, Arousal and Performance: A tribute to Bob Wilkinson. Boston: Birkhauser 1992. de Lugt DR, Loewy DH, Campbell KB. The effect of sleep onset on event related potentials with rapid rates of stimulus presentation. EEG Clin Neurophysiol 1996; 98: 484–492. de Lugt DR, Cote K, Lee W, Campbell K. Topographical analysis of the N100 auditory event-related potential during the transition of sleep. J Sleep Res 1996; 5 (Suppl. 1): 46. Harsh J, Peszka J. N350: an ERP indicator of cognitive processing during the sleep onset period? Paper presented to the Society for Psychophysiological Research, Toronto 1995. Cote KA. Event-related potential evidence of consciousness during wakefulness, sleep onset, and sleep. Doctoral dissertation, University of Ottawa 1999. Cote KA, Campbell KB. P300 to high intensity stimuli during REM sleep. Clin Neurophysiol 1999; 110: 1345–1350. Niiyama Y, Fujiwara R, Satoh N, Hishikawa Y. Endogenous components of event-related potential appearing during NREM stage 1 and REM sleep in man. Int J Psychophysiol 1994; 17: 165–174. Bastuji H, Garcı´a-Larrea L, Franc C, Mauguie`re F. Brain processing of stimulus deviance during slow-wave and paradoxical sleep: a study of human auditory evoked responses using the odd-ball paradigm. J Clin Neurophysiol 1995, 12: 155–167. Winter O, Kok A, Kenemans JL, Elton M. Auditory event-related potentials to deviant stimuli during drowsiness and stage 2 sleep. EEG Clin Neurophysiol 1995; 96: 398–412.

269

58. Naatanen R. Attention and Brain Function. Hillsdale, NJ: Lawrence Erlbaum 1992. 59. Nittono H, Momose D, Hori T. Gradual changes of mismatch negativity during the sleep onset period. Sleep Res Online 1999; 2 (Suppl. 1): 287. 60. Magnusson G. Studies on the Respiration During Sleep. London: Lewis & Co. 1944. 61. Timmons B, Salamy J, Kamiya J, Girton D. Abdominal-thoracic respiratory movements and levels of arousal. Psychonom Sci 1972; 27: 173– 175. 62. Trinder J, Whitworth F, Kay A, Wilkin P. Respiratory instability during sleep onset. J Appl Physiol 1992; 73: 2462–2469. 63. Worsnop C, Kay A, Pierce R, Kim Y, Trinder J. Activity of respiratory pump and upper airway muscles during sleep onset. J Appl Physiol 1998; 85: 908–920. 64. Naifeh KH, Kamiya J. The nature of respiratory changes associated with sleep onset. Sleep 1981; 4: 49–59. 65. Pivik RT, Busby K. Heart rate associated with sleep onset in preadolescents. J Sleep Res 1996; 5: 33–36. 66. Spielman AJ, Zhang G, Yang C-M, D’Ambrosio P, Serizawa S, Nagato M, von Gizycki H, Alfano RR. Intracerebral hemodynamics probed by near infrared spectroscopy. Brain Res 2000; 22: 1–13. 67. Ogilvie RD, McDonagh DM, Stone SN, Wilkinson RT. Eye movements and the detection of sleep onset. Psychophysiology 1988; 25: 81–91. 68. Torsvall L, Akerstedt T. Extreme sleepiness: quantification of EOG and spectral EEG parameters. Int J Neurosci 1988; 38: 435–441. 69. Hiroshige Y, Miyata Y. Slow eye movements and transitional periods of EEG sleep stages during daytime sleep. Shinrigaku Kenkyu 1990; 60: 378– 385 (Japanese; English abstract). 70. Kinnari K, Peter JH, Pietarinen A, Groete L, Penzel T, Varri A, Laippala P, Saastamoinen A, Cassal W, Hassan J. Vigilance stages and performance in OSAS patients in a monotonous reaction time task. Clin Neurophysiol 2000; 111: 1130–1136. 71. Rittweger J, Popel A. Respiratory-like periodicities in slow eye movements during sleep onset. Clin Physiol 1998; 18: 471–478. ∗72. Foulkes D, Vogel G. Mental activity at sleep onset. J Ab Psychol 1965; 70: 231–243. 73. Hayashi M, Katoh K, Hori T. Hypnagogic imagery and EEG activity. Perceptual Motor Skills 1999; 88: 676–678. 74. Michida N, Hayashi M, Hori T. Comparison of event related potentials with and without hypnagogic imagery. Psychiat Clin Neurosci 1998; 52: 145–147.

270

75. Hoddes E, Zarcone VP, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiol 1973; 10: 431–436. 76. Akerstedt T, Gillberg M. Subjective and objective sleepiness in the active individual. Int J Neurosci 1990; 52: 29–37. 77. Ogilvie RD, Wilkinson RT, Allison S. The detection of sleep onset: behavioural, physiological and subjective convergence. Sleep 1989; 12: 458–474. 78. Bonnet M, Moore SE. The threshold of sleep: perception of sleep as a function of time asleep and auditory threshold. Sleep 1982; 5: 267–276. 79. Colrain IM, Trinder J, Fraser G. Ventilation during sleep onset in young adult females. Sleep 1990; 13: 491–501. 80. Mezzanotte WS, Tangel DJ, White DP. Influence of sleep onset on upper-airway muscle activity in apnea patients versus normal controls. Am J Resp Crit Care Med 1996; 153 (6 Pt 1): 1880–1887. 81. Hori T. Electrodermal and electro-oculographic activity in a hypnagogic state. Psychophysiol 1982; 19: 668–672. ∗82. Ogilvie RD, Harsh JR. (eds). Sleep Onset: Normal and Abnormal Processes. Washington: American Psychological Association 1995. 83. Broughton RJ, Ogilvie RD. (eds). Sleep, Arousal and Performance. Boston: Birkhauser 1992. 84. Monk TH. (ed.). Sleep, Sleepiness and Performance. New York: Wiley 1991. 85. Santamaria J, Chiappa KH. The EEG of Drowsiness. New York: Demos 1987. 86. Weier L, Ogilvie R, Schirmuly M. Sleep onset: comparison of observational, behavioral and electrophysiological measures. Sleep Res 1994; 23: 167. 87. Stewart C. Sleep inertia: Subjective, behavioural and electrophysiological measurements. 2000 MA thesis submitted to Brock University, Canada. ∗88. Steriade M, McCarley RW. Brainstem Control of Wakefulness and Sleep. New York: Plenum 1990. 89. Henderson CJ, Butler SR, Glass A. The localization of equivalent dipoles of EEG sources by the application of electrical field theory. EEG Clin Neurophysiol 1975; 39: 117–130. 90. Koles ZJ, Kasmia A, Paranjape RB, McLean DR. Computer radial-current topography of the brain: patterns associated with the normal and abnormal EEG. EEG Clin Neurophysiol 1989; 72: 41–47. 91. Tesche C, Kajola M. A comparison of the localization of spontaneous neuromagnetic activity in the frequency and time domains. EEG Clin Neurophysiol 1993; 87: 408–416.

R. D. OGILVIE 92. Inouye T, Shinosaki K, Yagasaki A, Shimizu A. Spatial distribution of generators of alpha activity. EEG Clin Neurophysiol 1986; 63: 353–360. 93. Michel CM, Lehmann D, Henggeler B, Brandeis D. Localization of the sources of EEG delta, theta, alpha and beta frequency bands using the FFT dipole approximation. EEG Clin Neurophysiol 1992; 82: 38–44. 94. Vanderwolf CH, Robinson TE. Reticulo-cortical activity and behavior: a critique of the arousal theory and a new synthesis (with commentaries). Behav Brain Res 1981; 4: 459–514. 95. Amzica F, Steriade M. Electrophysiological correlates of sleep delta waves. EEG Clin Neurophysiol 1998; 107: 69–83. 96. Broughton RJ, Aguirre M. Differences between REM and NREM sleepiness measured by eventrelated potentials (P300, CNV), MSLT and subjective estimate in narcolepsy-cataplexy. EEG Clin Neurophysiol 1987; 67: 317–326. ∗97. Alloway CED, Ogilvie RD, Shapiro CM. The electrophysiology of the sleep-onset period in narcoleptics and normals. Sleep 1999; 22: 191– 203. 98. Freedman RR. EEG power spectra in sleeponset insomnia. EEG Clin Neurophysiol 1985; 63: 408–413. 99. Merica H, Gaillard J-M. The EEG of the sleep onset period in insomnia: a discriminant analysis. Physiol Behav 1992; 52: 199–204. 100. Lamarche C, Ogilvie RD. Electrophysiological changes during the sleep onset period of psychophysiological insomniacs, psychiatric insomniacs and normal sleepers. Sleep 1997; 20: 724–733. 101. Bonato RA. Electroencephalographic correlates of sleep onset in chronic psychophysiological insomniacs and normal sleepers. Doctoral Dissertation, Carleton University 1997. 102. Armitage R, Hudson A, Fitch T, Pechacek P. Period analysis of sleep onset in depressed outpatients and normal control subjects. In: Ogilvie RD, Harsh JR (eds). Sleep Onset: Normal and Abnormal Processes. Washington: American Psychological Association 1994. 103. Chilcott L, Ogilvie RD, Carll DJ, Powles ACP. Physiological markers observed during the MSLT define clinical subgroups. Paper presented to the APSS meeting in Orlando 1999. 104. Williams BR. Electrophysiological analysis of the sleep onset period: a comparison between subjects with long term insomnia complaints associated with mild traumatic brain injury and matched controls. Unpublished Master’s Thesis, Brock University 1999.