Neuroscience and Behavioral Physiology, Vol. 44, No. 4, May, 2014
“Microcyclic” Changes in Brain Bioelectrical Activity at Different Stages of Natural Sleep in Humans A. N. Shepoval’nikov, E. I. Gal’perina, and O. V. Kruchinina
UDC 612.821.7
Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 63, No. 1, 125–134, January–February, 2014. Original article submitted March 7, 2012. Accepted October 19, 2012. We report here data on a number of systematically recorded phasic EEG phenomena during the sleep–waking cycle, along with data on the heterogeneity of the EEG stages of sleep forming sequential sleep macrocycles in humans. These transient EEG changes quite often showed a tendency to periodicity, especially at the initial stages of sleep and in transitional states; characteristic sleep microcycles were observed. An attempt to identify the microstructures of EEG sleep stages during the sleep–waking cycle was made by identifying seven clusters reflecting changes in the biopotential field of the brain represented in n-dimensional factorial space. Most sleep stages were found to consist of three or four clusters, though some sleep periods (especially Loomis stage B and 1REM) were more homogeneous in structure. It is suggested that this heterogeneity of the microstructure of the spatial organization of oscillations in brain biopotentials in particular sleep stages reflects the dynamics of neurophysiological processes, promoting more effective performance of the repair and homeostatic functions of sleep. Keywords: sleep, sleep microcycles, EEG, spatial organization of brain biopotentials, heterogeneity of similar sleep stages.
The main directions in the development of contemporary somnology includes studies reporting detailed investigations of the microstructure of sleep, including measures reflecting transient changes in brain bioelectrical activity within particular sleep stages. These include studies of the phenomenon of the “cycling alternating pattern” (CAP) of the EEG [14, 16, 19, 22]. The CAP phenomenon is consistently recorded not only in a number of sleep disorders, but also in normal conditions; it is seen more often during shallow sleep and in transitional stages, though it can also be seen in delta sleep as a transient but generalized reorganization of the EEG. Depending on the dominant frequency of EEG components, three main subgroups of CAP are identified – A1, A2, and A3 – which differ in terms of duration, as well as the presence or absence of K complexes, delta bursts, vertex potentials, etc. In some periods of sleep,
especially phase I, the CAP phenomenon shows a tendency to quasirhythmicity. The nature of this phenomenon has received insufficient study, though existing data suggest that these “microstructural” changes in the systems activity of the brain, occurring within defined sleep stages, may have significant functional importance [22]. The heterogeneity of the functional state of the brain within a given stage of sleep is also supported by the existence of such electrographic phenomena as alpha-rhythm paroxysms (on waking), spontaneous K complexes (especially at the beginning of sleep stage II), generalized bursts of particularly high-amplitude slow waves (in deep sleep), and the appearance of an alphalike rhythm on the EEG (during paradoxical sleep), as well as the recording of other phenomena often combined with transient changes in autonomic processes (respiratory rhythm and rate, heart rate, and skin-galvanic reactions (SGR)). At all phases of sleep, especially the transitional states, these phasic processes would appear to reflect the processes of transient changes in the balance of interactions between diencephalic and brainstem centers responsible for switching the various phases of sleep and waking [21].
I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, St. Petersburg; e-mail:
[email protected].
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436 We present here our preliminary results from electroencephalographic studies of the structure of natural sleep in children and adults, providing evidence of significant EEG heterogeneity during similar stages of sleep. The aim of the present work was to identify “microcyclic” changes in the structure of the temporospatial relationships of brain biopotentials oscillations reflecting the existence of transient periods of reorganization of brain operation, providing evidence that cerebral structures can systematically, if only transiently, function during different sleep stages – deeper or shallower. METHODS Studies were performed using continuous electropolygraphic recording (EPG) of natural nocturnal sleep in children and adults of different ages (8–30 years). Nine healthy, right-handed subjects of both genders were studied, and provided written informed consent to take part. Recordings were made using a 24-channel computerized electroencephalograph with a bandpass of 0.3–40 Hz and a sampling frequency of 185 per sec per channel. Recordings were made continuously throughout the night (usually from 06:00 to 08:00) in a screened room in suitably comfortable conditions. Before going to sleep, electropolygrams were recorded in the state of calm waking with the eyes closed. EEG recordings were made using 19 channels. Electrode positions corresponded to the international 10–20 scheme. Leads were monopolar and combined reference electrodes on the earlobes were used. Recordings were also made of the electrocardiogram, electrooculogram, and electromyogram of the neck muscles. Analyses were performed using artifact-free standard 4-sec analysis epochs for all states apart from sleep stages III and IV, when 8-sec epochs were used. EEG sleep stages were classified as described in [20] on the basis of polygrams, though analysis of the EEG during going to sleep (i.e., sleep stage I) used the Loomis et al. classification [18], as a more differentiated evaluation was required for this transitional state between waking and sleep. During initial sleep stage I, we identified two states – I(A), with residual alpha-rhythm spindles, and I(B), with no rhythmic alpha activity and the presence of irregular theta- and delta waves with amplitudes of up to 80–120 μV. The dynamics of the dominant EEG periods were evaluated using an instrument limiting the amplitudes of EEG waves to values of +1 and –1 (“clipping” of the EEG). Information for periods between the time points at which the EEG crossed the baseline was reflected by recording each interval as a point positioned at the corresponding height above the count axis. The longer the period between the baseline-crossing points (i.e., the longer the period of the corresponding half-wave), the longer the path of this point and the higher it was on the monitor screen at the time of automatic display. Thus, each delta wave-associated period was marked by a point in the upper part of the frame,
Shepoval’nikov, Gal’perina, and Kruchinina while periods reflecting fast EEG waves were marked by a point in the lower part. The scale allowed precise evaluation of the number of waves in the corresponding EEG frequency range during the recording period at different points in the sleep–waking cycle. Most of the experiments were performed to identify the pattern of changes in the spatial structure of the brain biopotentials field. These experiments were based on an original method for EEG analysis in multidimensional space developed in our laboratory [8, 10, 11] and some modifications providing for quantitative evaluation of the spatial concordance of synchronized changes in the multichannel EEG (see appendix to [1] for a more detailed description of the mathematical approach). The EEG from each channel was represented in threedimensional factor space as a vector radius. The angle between these vector radii in the common factors space was inversely proportional to the level of statistical similarity between the corresponding EEG traces. Thus, 0° corresponded to a correlation coefficient CC = +1, 90° to CC = 0, and 180° to CC = –1. The volume occupied by the whole bundle of vector radii (in our case 19) in n-dimensional space will be the integral measure of the total correlatedness of all the EEG processes, which we term the “volumes parameter” (VOL). The magnitude of this parameter ranged from 0 to 1; when VOL = 0, processes had the greatest linear relationship with each other and when VOL = 1, the recorded processes were not statistically significantly related to each other (for CC assessment at τ = 0). Use of this measure provides a quantitative measure of the tightness of the spatial interaction of oscillations in brain biopotentials over the whole of the convex surface of the head and allows its dynamics to be followed over long periods of time. As our task was to identify EEG epochs with high levels of similarity within long recording periods, we used cluster analysis by the K-means method (run on Statistica 6) with division into seven clusters. Significant results were identified using Student’s t test, p ≤ 0.05. RESULTS The EEG is known to show significant amounts of heterogeneity not only in calm waking, but also during sleep. However, stereotypical EEG patterns nonetheless allow confident identification of 4–5 stages of orthodox and one phase of paradoxical sleep, on the basis of formal criteria [20]. At the same time, within each stage, EEG phenomena identifying the heterogeneous structure of bioelectrical activity were consistently recorded during different periods of sleep, especially going to sleep. This heterogeneity was apparent both as generalized and local changes in the EEG frequency-amplitude spectrum and as periodic signs of characteristic electrographic complexes, as well as in the reorganization of temporospatial relationships in EEG waves reflecting integral brain activity.
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Fig. 1. “Microcycles” of clipped EEG reflecting changes in the periods of alpha, theta, and delta waves on going to sleep. Example of the EEG of one subject. The ordinate shows EEG frequency bands; the abscissa shows time.
EEG changes within a given stage of sleep quite often show a tendency to form characteristic “microcycles,” which are apparent as the quasiperiodic occurrence of particular rhythms (alpha, sigma) and can even be detected in a single EEG lead. Figure 1 shows an example of typical changes in EEG periodograms recorded in an adult human going to sleep. Attention is drawn to the tendency to periodicity in increases in the amounts of alpha, theta, and delta waves with a period close to 60 sec. The distribution of the periods of these waves responded more finely to EEG changes than the power spectra traditionally used, and this convenient way of presenting clipped EEG traces for detailed analysis allows easy assessment, where needed, of the spread of the mean frequency of any rhythm. Levels of EEG homogeneity at different periods of waking and sleep at different ages were assessed by focusing on analysis of the dynamics of the temporospatial relationships of the EEG in terms of the VOL parameter, which provides a quantitative assessment of the overall interaction of simultaneously recorded EEG processes using a single numerical parameter. Figure 2 shows individual data for the age characteristics of the structure of the brain biopotentials field at different stages of sleep during the first two cycles. The degree of interaction of oscillations in cortical biopotentials, assessed in terms of the VOL parameter, differed at particular stages of sleep and changed significantly with age, reflecting increases in the statistical similarity of oscillations in brain biopotentials, which were particularly apparent at sleep stages III and IV. Thus, the greatest values for VOL, averaged for each state, were seen in children aged eight and nine years at the beginning of falling asleep (stages A and B [18]). In these children, the greatest degree of ordering of brain biopotentials (i.e., minimal VOL) was
seen in the slow-wave stages of sleep (Fig. 2), when VOL was close to the value in adults. All three adult subjects, whose VOL dynamics are plotted in Fig. 2, also showed relatively small changes in the level of ordering of brain biopotentials at different stages of sleep, though the range of these oscillations was significantly smaller than in children, and even at high levels of waking, VOL values were significantly smaller than in children. This points to greater stability in the organization of temporospatial relationships in the oscillations of brain biopotentials in adults. In an adolescent of 15 years of age, the spatial ordering in all the sleep stages studied showed essentially no difference from that in adults, though differences at this age were quite clearly apparent on waking [5]. This may be associated with the fact that “maturation” of the sleep EEG occurs earlier than “maturation” of the waking EEG, such that signs of EEG immaturity recorded at higher levels of consciousness persisted significantly longer than before age 14–16. Attention is also drawn to the marked increase in VOL in all age groups of subjects during sleep stage 1REM in both the first and second cycles. The data shown in Fig. 2 were obtained by averaging values of VOL within each sleep stage in individual subjects. For more detailed analysis of the fine structure of changes in the ordering of brain biopotentialswe used cluster analysis using the K means method (Statistica 6) throughout each stage, with division into seven clusters. Each point on the plot (Fig. 3), constructed using results from individual recordings of nocturnal sleep, corresponded to a single EEG analysis epoch. Comparing the sleep cyclogram (Fig. 3, below) with the separation of analysis epochs by clusters (from 1 to 7, Fig. 3, above), we found that the state of waking with the eyes closed and the initial
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Fig. 2. Changes in mean VOL, characterizing the level of overall concordance of the electrical processes of the brain biopotentials field in the first two sleep cycles in subjects of different ages. The ordinate shows VOL; the abscissa shows stages of the sleep–waking cycle [18, 20]. Details at right give subjects’ ages and gender.
stage of going to sleep (A in [18]) showed almost no difference in terms of measures of temporospatial organization. Both of these states were heterogeneous and unstable; they consisted of alternating clusters, mainly 1 and 5 (note that division into clusters was based on levels of similarity and not gradually). The period of going to sleep (stage B in [18]) was characterized by a particular, stable state of the organization of temporospatial relationships and consisted mainly of cluster 6. The EEG analysis epochs belonging to this cluster were present essentially only in this stage and the very beginning of stage II (as defined in in [20] or C as defined in [18]). Stage C (II), like stage A (I), was heterogeneous: EEG analysis epochs belonging to clusters typical of stage B were present at the beginning and were followed by clusters specific for stage C (cluster 3), and type 2 clusters, which are most commonly seen in the slow-wave stages (III–IV) started to be present from about half way through stage C. These stages of slow-wave sleep consisted mainly of epochs belonging to two clusters, 2 and 7, and, to a lesser extent, clusters 1, 3, and 4. Paradoxical sleep (1REM) consisted mainly of the dominant cluster 4, with inclusion of only occasional epochs belonging to clusters 2, 3, and 7. The example data presented in Fig. 3 varied significantly in different subjects, though the patterns described above persisted, i.e., most stages were dominated by two clusters,
though clusters typical of the preceding and succeeding sleep stages were “wedged in” in each stage. Stage B occupied a special position, playing a key role in reorganizing the systems activity of the brain during the sleep–waking cycle, and was linked with supporting of the intensive processes of the transition from waking to sleep, with inactivation of consciousness. Thus, the impression is created that within particular EEG sleep stages, there were identifiable differences in the levels of concordance of the systems interactions of oscillations of bioelectrical processes recorded in different parts of the cortex. Some stages (or, more precisely, stages B and 1REM) were characterized by a more homogeneous state, expressed as dominance of a specific cluster, while there was greater instability of systems interactions of the cortical fields at the beginning of going to sleep and during slow-wave sleep. DISCUSSION New and important data on the mechanisms regulating waking and sleep obtained by neurophysiologists in the last decade [4, 13, 21] have led to significantly deeper and multidisciplinary understanding of the nature of sleep and have opened the pathway to seeking as yet undiscovered patterns in the systems organization of brain activity at different depths of sleep and an answer to the question of the func-
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Fig. 3. Macro- and microstructure of the sleep–waking cycle in adult subject Ch during the first sleep cycle. Above – dynamics of the progression of clusters constructed by analysis of sequential standard EEG analysis epochs in conditions of continuous recording. Below – schematic illustration of the duration of sleep stages during the first sleep–waking cycle [18, 20]. The ordinate shows numbers of clusters; the abscissa shows sequential EEG analysis epochs. See text for explanation.
tional purposes of the various stages of sleep. Known facts on the heterogeneity of particular sleep stages and the multitude of data on phasic changes in the EEG, not infrequently accompanied by autonomic and motor phenomena, have acquired new significance in relation to their possible interaction with processes occurring as a result of competitive relationships between different centers in the rostral parts of the brainstem, diencephalic structures, and cortex at different stages in the sleep–waking cycle and on progression of individual sleep stages. Thus, attempts to perform detailed analysis of the CAP and other transient EEG changes which do not interrupt the ongoing sleep stage and to obtain a more differentiated evaluation of EEG structure and the spatial organization of interregional relationships between oscillations in cortical biopotentials may significantly aid more complete understanding of the mechanisms of cortical-subcortical interactions at different periods of the sleep–waking cycle in health and pathology. We can agree with Parrino’s view [19] that CAP and other EEG activatory complexes reflect the activity of the neurophysiological mechanisms of “sleep protection” by enhancement of the “door-locking” function formed by the thalamus and basal parts of the forebrain.
Our data showed that the greatest level of similarity in the magnitude of VOL between children and adults was seen during delta sleep and the greatest differences were seen during waking, during going to sleep, and during 1REM sleep (Fig. 2); these findings are consistent with previous results [10] showing that the neurophysiological mechanisms responsible for organizing systems activity in the brain during deep sleep mature faster in children than those operating during waking. It is of note that in adolescents, the degree of spatial co-organization at all sleep stages studied showed essentially no difference from that in adults, while there were clear differences during waking at this age [5]. Much remains unclear in relation to possible increases in the controlling role of the brain in relation to the internal organs [6] during sleep and the very probable compensatory significance of parasomnias in promoting switching between sleep phases when particular cerebral structures are insufficiently mature in children or when there are impairments to the neurophysiological mechanisms responsible for this important function [9]. The fact that our data obtained from evaluation of the level of concordance of EEG processes in particular periods
440 of sleep allowed immediate detection of several clusters characterized by different levels of total spatial co-organization of brain bioelectrical processes in these states may point to flexibility in the organization of interregional relationships, promoting more successful performance of the functional tasks of each of the periods of sleep. Clearly it can be suggested that the presence in particular sleep stages of clusters characteristic of previous, already completed stages may reflect “finishing the unfinished,” while the appearance of clusters characteristic of the next sleep stages is probably associated with the activity of neurophysiological mechanisms supporting readiness for switching to the next sleep stage. Thus, use of this method for analysis of the dynamics of the spatial organization of the EEG can facilitate clearer identification of the transitional sleep stages. The appearance of “microcycles” typical of going to sleep in the structure of the EEG (Fig. 1) would appear to be associated with activation of the processes of the systems reorganization of brain functioning needed for onset of sleep. Earlier studies in our laboratory, yielding data on the local contributions of each of the cortical areas studied to the organization of the general structure of the brain biopotentials field [12] noted that the greatest changes in all cortical zones were seen in stage B, i.e., going to sleep. Could this phenomenon reflect a functional state of the brain such as loss of its leading position by the cortex, i.e., cessation of consciousness, while coordination of brain systems activity transfers to subcortical structures? If so, this makes it easier to understand the special value of stage B, identified by cluster analysis (Fig. 3), which is characterized by an unusually high level of stability of one cluster. This cluster is barely represented in other stages, apart from the transitional period to stage C. It is also important to note that this cluster, No. 6, consists of EEG analysis epochs with a mean VOL of 0.07 – the minimum level during the whole of the study period. We note that such low VOL values reflect a high degree of spatial co-organization of all the EEG processes recorded. Considering data reported in [4, 15, 21], it is possible that during this period, corresponding to the intense transition to the state of sleep, weakening of activity in “waking centers” and increases in the activity of inhibitory neurons located mainly in the ventrolateral and median parts of the preoptic area of the anterior hypothalamus are accompanied by additional activation of inhibitory GABAergic neurons in cortical layers 1 and 2 [15], which leads to generalized cooperative rearrangement of the entire brain biopotential field. This total rearrangement is the dominant factor supporting the high level of concordance of processes, despite the polymorphous EEG pattern typical of this period of the sleep–waking cycle. Of particular relevance to assessment of the special functional role of the falling-asleep period (especially stage B) is the obvious contradiction between the stable persistence of one cluster (No. 6) and the repeatedly identified (including in our experiments [12]) instability of EEG patterns during
Shepoval’nikov, Gal’perina, and Kruchinina this initial period of sleep, which probably points to diversity and intensification of the activity of the processes supporting the “cessation” of consciousness. It is reasonable to suggest that on the background of the intensification of multiple processes supporting the process of falling asleep, cluster analysis based on the VOL parameter allowed characteristics reflecting key signs in the system reorganizing brain activity in this especially important period of the transition to sleep to be identified in sequential EEG epochs. It is of note that cluster 1 (Fig. 3), almost the only cluster seen in calm waking and the very beginning of the state of drowsiness, includes EEG epochs with relatively high mean VOL, 0.18, which provides evidence of differentiated functioning of the cortical fields, which continue to support a quite high level of activity characteristic of the brain state in which consciousness has yet to be “switched off” [2]. We have also observed similar processes on analysis of the temporospatial relationships between oscillations of brain biopotentials on the transition to light hypnotic sleep, though in this case the changes were mainly local, associated with processes in the anterior areas of the right hemisphere [12]. Thus, we will seek analogies with the recently described phenomenon of wavelike changes in the power of the alpha- and beta-rhythms during sessions of “animal hypnosis” in rabbits [7]. The authors regarded this process as resulting from increases in internal inhibition. It is of note that judging from plot 1 in this article, the period of the wavelike oscillations in mean EEG parameters is close to 60 sec, i.e., reminiscent of the “microcycle” shown in Fig. 1. With regard to the functional purposes of the various phasic phenomena, such as quasiperiodic “inclusion of activatory EEG fragments” in various sleep stages, we can agree with the view that these periods of short-term reorganization of brain activity support its ability to awaken urgently when significant changes in the surrounding environment or internal milieu occur [3], while the variance of components of the K complex in more or less deep sleep may reflect the phasic interaction of oscillations in the level of activity of the various elements of the neurophysiological systems responsible for sleep or waking [17]. CONCLUSIONS Considering the stability of the macrocyclic organization of sleep in humans and animals and the tendency of the body to show targeted compensation of delta sleep or paradoxical sleep in conditions of their selective artificial deprivation, we can suggest that each sleep stage has its own special functional purpose. Some data on the specific neurochemical and neurophysiological characteristics of the individual periods of sleep provide grounds supporting the correctness of this assessment and justify the search for EEG correlates which may reflect processes which also occur in deep brain structures at different stages of the sleep–waking cycle, on switching of sleep stages, and in
“Microcyclic” Changes in Brain Bioelectrical Activity at Different Stages of Natural Sleep transitional states. The functional importance of the cycling alternating pattern (CAP) and other microstructural changes in particular sleep stages remains insufficiently clear. However, these EEG phenomena evidently reflect the dynamics of neurophysiological processes which play significant roles in supporting lability in the organization of the brain’s systems activity during sleep and in the protection of sleep from unimportant stimuli (along with an increase in the readiness for urgent waking when required); these short-term processes may be important for supporting the switching of different sleep stages and for preparing the body for active activity in the stage of waking. This study was supported by the Russian Humanities Scientific Foundation (Grant No. 10-06-01000a).
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