Introduction. Overview

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1 Cyclic alternating pattern Liborio Parrino MD ( Dr. Parrino of the University of Parma has no relevant financial relationships to disclose. ) Antonio Culebras MD, editor. ( Dr. Culebras of SUNY Upstate Medical University at Syracuse received an honorarium from Jazz Pharmaceuticals for a speaking engagement.) Originally released February 9, 2014; last updated November 14, 2017; expires November 14, 2020 Introduction Overview Cyclic alternating pattern is an EEG marker of sleep instability that modulates the flexibility of sleep in both physiological conditions and in sleep disorders. Together with sleep duration, sleep intensity, and sleep continuity, cyclic alternating pattern represents a topical pillar of sleep quality. Key points Expressed by the physiological phasic events of NREM sleep, including arousals, cyclic alternating pattern represents the most complex arrangement of sleep microstructure. Cyclic alternating pattern is organized in sequences organized in successive cycles. Each cyclic alternating pattern cycle is composed of a phase A (phasic events) and a phase B (background rhythm). Cyclic alternating pattern involves not only cerebral activities during sleep, but paces, and is reciprocally influenced by ongoing autonomic and motor functions, with activation during phase A and deactivation during phase B. The interaction between cyclic alternating pattern, neurovegetative fluctuations, and motor events determines the pathophysiology of several sleep disorders and the effect of medication and continuous positive airway pressure (CPAP) treatment. Recent studies indicate the possibility of scoring cyclic alternating pattern automatically, opening new perspectives for a wider exploitation of this fundamental mechanism of the sleeping brain. Historical note and terminology Sleep staging: the conventional scoring rules. The standardized Rechtschaffen and Kales criteria for sleep staging have constituted the internationally accepted system of sleep scoring for 45 years (Rechtschaffen and Kales 1968). An enormous body of data has been produced and published in the scientific literature using this system. The American Academy of Sleep Medicine (AASM) manual introduced in 2007 revised Rechtschaffen and Kales sleep staging to address digital methodology as well as the scoring of arousals, respiratory events, sleep-related movement disorders, and cardiac abnormalities, with consideration of pediatric and geriatric age groups (Iber et al 2007). In particular, the new visual scoring of sleep expands the number of channels indicated in the Rechtschaffen and Kales rules (single central derivation), allowing a more extensive coverage of scalp areas where significant sleep-wake rhythms and waveforms are better represented. In the AASM system, the distinction between wakefulness (W), non-rem sleep (N), and REM sleep (R) is maintained, but the non-rem stages are reduced to 3: N1 (previous stage 1), N2 (previous stage 2), and N3 (previous stages 3 + 4). Unfortunately, the lumping of stages 3 and 4 in a single stage N3 downsizes the nocturnal development of the process S, characterized by decreasing peaks across the night, according to the decreasing power of the EEG delta band (Borbely 1982). Instead of providing additional information on the power of the EEG signal, the adopted solution is a reductive simplification of the previous sleep stages. Moreover, obliterating the exponential profile of process S, the restorative function of slow-wave sleep, and its direct relationship with waking activities are shadowed. In the Rechtschaffen and Kales rules, EEG arousals were excluded from conventional staging procedures. In 1992, the American Sleep Disorders Association (ASDA; later named the AASM) defined arousals as markers of sleep disruption representing a detrimental and harmful feature for sleep (American Sleep Disorders Association 1992). In the following years, however, a number of studies clarified that spontaneous arousals are natural guests of sleep and undergo a linear increase along the lifespan following the profile of maturation and aging (Boselli et al 1998). Moreover, the

2 spectral composition of arousals and their ultradian distribution throughout the sleep cycles reveal that arousals are endowed within the texture of physiologic sleep under the biological control of REM-on and REM-off mechanisms (Terzano et al 2005). Arousal scoring is now considered a fundamental process in staging classification as well as spindles and K-complexes. In the Rechtschaffen and Kales system, a K-complex was unmistakably a marker of sleep stage 2. According to the AASM manual, if a K-complex is associated with an arousal the epoch is scored as N1. In other words, the Rechtschaffen and Kales approach privileged the role of EEG synchrony in the scoring process, whereas the AASM manual enhances the impact of EEG desynchrony (Parrino et al 2009a). In spite of the differences regarding sleep stages, arousals, and K-complexes, the 2 scoring systems share the same cultural frameworks, ie, rigid epochs of 30 seconds, neglecting evidence that sleep is a continuous function that cannot be restricted to a static sequence of artificial segments. In effect, each 30-sec epoch encompasses several short-time events that carry important information that does not show up in the classical sleep staging reports. These features have been grouped under the general label of sleep phasic events and detailed as K-complexes, sleep spindles, delta bursts, arousals, and K-alpha. The most comprehensive method for their analysis and report is the so-called cyclic alternating pattern (Terzano et al 1985). Because cyclic alternating pattern spans across long periods of NREM sleep, it overcomes the boundaries of rigid epochs and offers a dynamic contribution to the static framework of conventional scoring. Scoring rules and significance of cyclic alternating pattern. Cyclic alternating pattern is a well-defined marker of the physiological cerebral activity occurring under conditions of reduced vigilance (sleep, coma), translating a state of arousal instability and involving muscle, behavioral, and autonomic functions (Terzano et al 2002b; Parrino et al 2006; Parrino et al 2012a). During NREM sleep, cyclic alternating pattern is organized in sequences. A cyclic alternating pattern sequence is composed of a succession of cyclic alternating pattern cycles. The cyclic alternating pattern cycle is composed of a phase A (lumps of sleep phasic events) followed by a phase B (return to EEG background). All cyclic alternating pattern sequences begin with a phase A and end with a phase B. Each phase of cyclic alternating pattern is 2 to 60 s in duration. This cut-off relies on the consideration that the great majority (about 90%) of A phases occurring during sleep are separated by an interval of less than 60 s (Terzano and Parrino 1991). The absence of cyclic alternating pattern for more than 60 s is scored as non-cyclic alternating pattern and coincides with a condition of sustained physiological stability (Terzano et al 1986). An isolated phase A, ie, a phase A separated from another phase A by more than 60 s, is classified as non-cyclic alternating pattern. The reactivity of cyclic alternating pattern. Cyclic alternating pattern and non-cyclic alternating pattern can be consistently manipulated by sensorial inputs. Applying separately the same arousing stimulus during the 2 EEG components of cyclic alternating pattern, phase B is the one that immediately assumes the morphology of the other component, whereas the inverse transformation never occurs when the stimulus is delivered during phase A. This stereotyped reactivity persists throughout the successive phases of cyclic alternating pattern with lack of habituation. In contrast, when the same stimulus is presented during non-cyclic alternating pattern, the EEG responses are generally brief, hypersynchronized (slow-waves), and proceed toward progressive habituation (Terzano et al 1990; Terzano and Parrino 1991). However, a robust or sustained stimulus delivered during non-cyclic alternating pattern induces the immediate appearance of repetitive cyclic alternating pattern cycles that display the same morphology and reactive behavior of spontaneous cyclic alternating pattern sequences. The evoked cyclic alternating pattern sequence may herald a lightening of sleep depth or continue as a damping oscillation before the complete recovery of non-cyclic alternating pattern. General rule. Cyclic alternating pattern cannot be measured without having scored sleep stages in advance; however, it is not limited to epoch fragmentation and spans over long periods of NREM sleep. An A phase is scored within a cyclic alternating pattern sequence only if it precedes and/or follows another phase A in the 2 to 60 s temporal range. At least 2 consecutive cyclic alternating pattern cycles are required to define a cyclic alternating pattern sequence. Consequently, 3 or more consecutive A phases must be identified with each of the first 2 A phases followed by a phase

3 B (interval <60 s), and the third phase A followed by a non-cyclic alternating pattern interval of more than 60 s. Cyclic alternating pattern sequence onset must be preceded by non-cyclic alternating pattern (a continuous non-rem sleep EEG pattern for >60 s), with the following 3 exceptions. There is no temporal limitation: (1) before the first cyclic alternating pattern sequence arising in non-rem sleep; (2) after a wake to sleep transition; and (3) after a REM to non- REM sleep transition (Terzano et al 2002b). Cyclic alternating pattern sequences have no upper limits for duration and number of cyclic alternating pattern cycles. In normal young adults, 2.5 min is the approximate mean duration of a cyclic alternating pattern sequence, containing an average of 6 cyclic alternating pattern cycles (Smerieri et al 2007). Stage shifts. Within non-rem sleep, a cyclic alternating pattern sequence is not interrupted by a sleep stage shift if cyclic alternating pattern scoring requirements are satisfied. Consequently, because cyclic alternating pattern sequences can extend across adjacent sleep stages, a cyclic alternating pattern sequence can contain a variety of different phase A and phase B activities. Cyclic alternating pattern in REM sleep. Cyclic alternating pattern sequences commonly precede the transition from non-rem to REM sleep and end just before REM sleep onset. REM sleep is characterized by the lack of EEG synchronization; thus, phase A features in REM sleep consist mainly of desynchronized patterns (fast low-amplitude rhythms), which are separated by a mean interval of 3 to 4 min (Schieber et al 1971). Consequently, under normal circumstances, cyclic alternating pattern does not occur in REM sleep. However, pathologic conditions characterized by repetitive A phases recurring at intervals less than 60 s (eg, periodic REM-related sleep apnea events), can produce cyclic alternating pattern sequences in REM sleep (Terzano et al 1996). Recording techniques and montages. Cyclic alternating pattern is a global EEG phenomenon involving extensive cortical areas. Therefore, A phases should be visible on all or most EEG leads. Bipolar derivations such as Fp1-F3, F3- C3, C3-P3, P3-O1 or Fp2-F4, F4-C4, C4-P4, and P4-O2 guarantee a favorable detection of the phenomenon. A calibration of 50 mv/7 mm with a time constant of 0.1 s and a high-frequency filter in the 30 Hz range is recommended for EEG channels. Monopolar EEG derivations (C3-A2 or C4-A1 and O1-A2 or O2-A1), eye movement channels, and submentalis EMG, currently used for the conventional sleep staging and arousal scoring, are also essential for scoring cyclic alternating pattern. Amplitude limits. Changes in EEG amplitude are crucial for scoring cyclic alternating pattern. Phasic activities initiating a phase A must be a third higher than the background voltage (calculated during the 2 s before onset and 2 s after offset of a phase A). However, in some cases, a phase A can present ambiguous limits due to inconsistent voltage changes. Onset and termination of a phase A are established on the basis of an amplitude/frequency concordance in the majority of EEG leads. The monopolar derivation is mostly indicated when scoring is carried out on a single derivation. All EEG events that do not clearly meet the phase A characteristics cannot be scored as part of phase A. Time limits. The minimal duration of a phase A or a phase B is 2 s. If 2 consecutive A phases are separated by an interval of less than 2 s, they are combined as a single phase A. If they are separated by an interval of 2 s or more, they are scored as independent events. The A phases of cyclic alternating pattern. Subtype classification. Phase A activities can be classified into 3 subtypes. Subtype classification is based on the reciprocal proportion of high-voltage slow waves (EEG synchrony) and low-amplitude fast rhythms (EEG desynchrony) throughout the entire phase A duration. The 3 phase A subtypes are described as follows (Terzano et al 2002b). Subtype A1. EEG synchrony (high-amplitude slow waves) is the predominant activity. If present, EEG desynchrony (low-amplitude fast waves) occupies less than 20% of the entire phase A duration. Subtype A1 specimens include delta bursts, K-complex sequences, vertex sharp transients, and polyphasic bursts with less than 20% of EEG desynchrony. Subtype A2. The EEG activity is a mixture of slow and fast rhythms with 20% to 50% of phase A occupied by EEG desynchrony. Subtype A2 specimens include EEG arousals and polyphasic bursts with more than 20%, but less than 50%, of EEG desynchrony. Subtype A3. The EEG activity is dominated by rapid low-voltage rhythms with more than 50% of phase A occupied by

4 EEG desynchrony. Subtype A3 specimens include K-alpha, EEG arousals, and polyphasic bursts with more than 50% of EEG desynchrony. A movement artifact within a cyclic alternating pattern sequence is also classified as subtype A3. Cyclic alternating pattern sequences include different phase A subtypes. The majority of EEG arousals occurring in non-rem sleep (87%) is inserted within the cyclic alternating pattern sequences and basically coincides with a phase A2 or A3. In particular, 95% of subtype A3 and 62% of subtype A2 meet the AASM criteria for arousals (Parrino et al 2001; Terzano et al 2002a). The broad overlap between arousals and subtypes A2 and A3 is further supported by their similar evolution in relation to age and to their positive correlation with the amount of light NREM sleep and negative correlation with the amount of deep NREM sleep. Spectral composition. Power spectral analysis of cyclic alternating pattern components shows that the different phase A subtypes in non-rem sleep are variants of a continuous 2-fold process: an initial high-voltage slow-wave component, which predisposes the cerebral cortex to a greater readiness and opens the way to the more rapid activity, correlated with strong activating effects (De Carli et al 2004; Ferri et al 2005a). What distinguishes the single event is the buildup and reciprocal distribution of the EEG components. In the A1 phases of cyclic alternating pattern, which exclusively host K-complexes and equivalent slow-wave activities (vertex potentials and delta bursts), the starting delta power increase is maintained and prevails throughout the entire activation process. A balanced representation of slow and fast EEG frequency bands is the main characteristic of A2 phases, whereas rapid EEG activities are the dominant feature of A3 subtypes and arousals. This does not mean that all activating complexes exert equivalent effects on sleep structure and on autonomic functions. A hierarchical activation from the slower EEG patterns (moderate autonomic activation without sleep disruption) to the faster EEG patterns (robust autonomic activation associated with visible sleep fragmentation) has been described in different studies (Guilleminault and Stoohs 1995; Ferri et al 2000; Sforza et al 2000; Halasz et al 2004; Togo et al 2006). If AASM arousal is a sign of transient sleep discontinuity, the finding of phasic EEG delta activities during enhancement of autonomic functions indicates the possibility of physiological activation without sleep disruption (Halasz 1993). In effect, non-visible sleep fragmentation induced by acoustic tones has been associated with increased daytime sleepiness, indicating that the processes of sleep consolidation may be impaired (in this case by sensorial stimulation) without evidence of sleep discontinuity (Martin et al 1997). Description Cyclic alternating pattern and the gating mechanisms of sleep. Despite their EEG differences, slow EEG events (K-complexes and delta bursts) and AASM arousals (fast rhythms) share functional properties and, therefore, may be included in the comprehensive term activating complexes. Such a variety of EEG manifestations relies on specific gates controlling the flow of internal and external inputs. The thalamic-basal forebrain gate is an ultimate step of resistance against arousing impulses. Initially the cortex tries to preserve sleep continuity with reinforcement of its gates that are indicated by the occurrence of K-complexes and delta bursts in sleep EEG. However, when the thalamic gate cannot control the afferent inputs, a cortical change is seen, translated by an alpha mixed or an alpha/beta frequency burst (Hirshkowitz 2002). The initial reaction of the cerebral cortex is a sleep-protective response as the majority of transient rapid activities are preceded by a slow highamplitude EEG burst (Halasz 1993). Slow EEG events during non-rem sleep are an emergent property of corticothalamo-cortical networks. In particular, they originate from the dynamic interplay of 3 cardinal oscillators: the synaptically based cortical oscillator and 2 thalamic oscillators, ie, thalamocortical neurons and nucleus reticularis thalami. The functional implications of this dialogue provide permissive windows for cellular excitability and network plasticity during slow-wave sleep (Crunelli and Hughes 2010). Cyclic alternating pattern sequences reflect the balance between sleep- and wake-promoting systems. Subtype A1 is a natural delta injection fueling the build-up and consolidation of deep NREM stages and defending sleep against perturbations. In contrast, cyclic alternating pattern subtypes A2 and A3 drive the sleeper toward more superficial vigilance states. Therefore, cyclic alternating pattern sequences represent a protective, short-term homeostatic mechanism of non-rem sleep in which the amount of slow-wave activity is buffered and sleep continuity preserved (Halasz 1998). When delta power increases in sleep after sleep deprivation, the cyclic alternating pattern system reacts with a robust decrease of cyclic alternating pattern rate (De Gennaro et al 2002). Supplementation of sleep with slow waves after deprivation counteracts the production of cyclic alternating pattern sequences, probably because the sleep promoting system is under saturation.

5 Cyclic alternating pattern and oscillation below 1 Hz. Besides cyclic alternating pattern, the other major EEG activity in the frequency range below 1 Hz is the so-called slow oscillation (Amzica and Steriade 1997). This 0.5 to 0.9 Hz EEG rhythm, which characterizes states of reduced tonic arousal, was outlined during anesthesia and NREM sleep in both animals (cats and rats) and human subjects. The slow oscillation is generated in cortical neurons and consists of phases of depolarization characterized by intensive neural firing, followed by long-lasting hyperpolarization. Hence, the 2 phases of the slow oscillation are characterized by opposite neural phenomena: cortical excitation made up of synaptic potentials and cortical inhibition mainly due to disfacilitation in the network. The excitatory component of the slow oscillation is effective in grouping the K-complexes and delta waves, which do not occur in isolation, but are grouped into complex wave sequences. The coalescence of slow rhythms is especially visible during NREM sleep. In particular, high-voltage slow waves rarely appear as isolated features during slow-wave sleep, but in most cases they converge into collectives resulting in the phase A1 subtypes of cyclic alternating pattern. As NREM sleep progresses from stage 1 to stage 4, the differences in morphology and voltage between phase A (clusters of K-complexes and delta bursts) and the successive phase B (sleep stage background) become gradually less evident until the EEG is dominated by the uniform pattern of non-cyclic alternating pattern with the high amplitude slow waves recurring in the frequency range of oscillation below 1 Hz. Neurophysiological investigation has ascertained that the slow cortical oscillation (<1 Hz) is absent at sleep onset but begins to organize in small territories, thereafter recruiting larger ones through coupling mechanisms as sleep deepens. Consolidated slow-wave sleep is characterized by a sustained oscillation below 1 Hz, which reflects a stable non-cyclic alternating pattern condition (Terzano and Parrino 2000; Parrino et al 2012a). Cyclic alternating pattern and the structure of sleep. Sleep architecture is based on the cyclic alternation of 2 major neurophysiological states: NREM and REM sleep. NREM sleep is composed of 3 or 4 stages in which EEG synchrony grows with the increasing depth of sleep. In contrast, EEG desynchrony is the dominant feature of REM sleep. The alternation of NREM and REM sleep constitutes the sleep cycle, and its recurrence during the night determines the classical stepwise profile of sleep macrostructure. The NREM portion of the sleep cycle starts with a slow descending branch sloping from the more superficial to the deeper NREM stages; continues with a central trough that represents the deepest stages of the sleep cycle; and ends with a rapid reverse ascending branch, expressed by the more superficial NREM stages that precede REM sleep. Accordingly, the NREM sleep architecture delineates a continuous pattern of build-up (descending branch), maintenance (trough), and resolution (ascending branch) of EEG synchrony. A detailed investigation has ascertained that the spontaneous EEG fluctuations centered on the 20 to 40 s periodicity of cyclic alternating pattern are involved in the subtle mechanisms that regulate the production and attenuation of slow-wave activities during sleep. There is evidence that the different components of cyclic alternating pattern have a sculpturing effect on the profile of the sleep cycle. Comparing spectral assessment and EEG visual scoring of NREM sleep in normal healthy subjects, the amount of slow rhythmic oscillations (spectral analysis) parallels the number of cyclic alternating pattern cycles (visual detection), with a striking agreement between spectral power gatherings and visually scored A phases (Terzano et al 2005). The regular EEG oscillations that accompany the transition from light sleep to deep stable sleep are basically expressed by the A1 subtypes (Terzano et al 2000). Within the sleep cycle, 90% of the A phases detected in the descending branches and 92% of the A phases detected in the troughs are subtype A1, whereas 64% of the A phases identified in the ascending branches are subtypes A2 (45%) or A3 (19%). These findings indicate that both slow and rapid EEG activating complexes are involved in the structural organization of sleep. The abundance of A1 subtypes in the descending branches and troughs can be the EEG expression of the cerebral mechanisms involved in REM-off activity, whereas the predominance of subtypes A2 and A3 (and arousals) in the ascending branches reflects the REM-on drive (Terzano et al 2005). Therefore, besides their manifold EEG features, activating complexes are also characterized by a non-random distribution across the night, which assumes a clear-cut periodicity during NREM sleep within the framework of cyclic alternating pattern. For this reason, cyclic alternating pattern is considered the main expression of sleep microstructure (Ferri et al 2006). The measures of cyclic alternating pattern: ontogenetic aspects. EEG features are highly sensitive markers of brain development. Accordingly, sleep reflects the physiological changes that accompany the different ages of the lifespan. In particular, slow-wave sleep dominates in the younger decades in contrast to superficial NREM sleep, which increases with the aging process. As well as conventional measures, cyclic alternating pattern parameters undergo dynamic changes across the maturational phases of life. The age-related values of cyclic alternating pattern have been measured and defined in order to establish the physiological ranges of normal sleep (Parrino et al 1998). Cyclic alternating pattern rate. Among the various cyclic alternating pattern parameters, cyclic alternating pattern rate is the most extensively used for clinical purposes. Calculated as the percentage ratio of total cyclic alternating

6 pattern time to non-rem sleep time, cyclic alternating pattern rate is the measure of arousal instability; it can be enhanced when sleep is disturbed by internal or external factors, and its variations correlate with the subjective appreciation of sleep quality with higher values of cyclic alternating pattern rate associated with poorer sleep quality (Terzano et al 1990). In normal sleepers, cyclic alternating pattern rate shows a low intraindividual variability from night-to-night, but undergoes a complex evolution during development (Terzano et al 1986). Cyclic alternating pattern rate is very low in the first months and then shows a gradual increase with a peak in adolescence (Bruni et al 2002; Bruni et al 2005; Lopes et al 2005; Miano et al 2009). The lowest amounts of cyclic alternating pattern rate appear in young adulthood, followed by a linear increase from older adulthood to senescence (Parrino et al 1998). Clinical applications Clinical applications of cyclic alternating pattern. Cyclic alternating pattern and sleep disorders in adults. The cyclic oscillations of cyclic alternating pattern are physiological components of sleep structure in which they act as dynamic segments, pacing the state transition between different NREM sleep stages according to homeostatic and ultradian processes. However, cyclic alternating pattern sequences can occur also in response to external stimuli of different sensorial modality (tactile, thermal, acoustic, painful, etc.). Accordingly, the amount of cyclic alternating pattern increases when sleep is achieved under conditions of noise stimulation or in situations of sleep disruption, such as insomnia, depression, eating disorders, upper airway resistance syndrome, sleep apnea syndrome, periodic limb movements, nocturnal frontal lobe epilepsy, primary generalized and focal lesional epilepsy, and Prader-Willi syndrome in adults (Terzano et al 1989; Terzano et al 1990; Terzano et al 1991; Terzano et al 1996; Terzano and Parrino 1992; Parrino et al 1994; Parrino et al 1996; Parrino et al 2000b; Parrino et al 2012b; Zucconi et al 2000; Farina et al 2003; Della Marca et al 2004; Priano et al 2006; Guilleminault et al 2007). It is lowered by sleep-promoting conditions such as narcolepsy, multiple system atrophy, drug administration, CPAP treatment in obstructive sleep apnea, and nighttime recovery sleep after prolonged sleep deprivation (Terzano and Parrino 1992; Parrino et al 1993; Parrino et al 1994; Parrino et al 1997; Parrino et al 2000b; 2005; De Gennaro et al 2002; Ferri et al 2005b; Terzano et al 2006; Vetrugno et al 2007; Svetnik et al 2010; De Paolis et al 2013). Cyclic alternating pattern is not only influenced by sleep disorders, but, in turn, modulates the occurrence and distribution of sleep-related events. In particular, phase A of cyclic alternating pattern triggers and paces the allocation of bruxism, sleepwalking, epileptic events, periodic limb jerks, and rhythmic movements during NREM sleep (Zucconi et al 1995; Parrino et al 1996; Parrino et al 2013; Macaluso et al 1998; Parrino et al 2000a; Manni et al 2004; Guilleminault 2006; Nobili et al 2006; Lavigne et al 2008). In contrast, phase B of cyclic alternating pattern is closely related to the repetitive respiratory events of sleep-disordered breathing, and only the powerful autonomic activation during the following cyclic alternating pattern A phase can restore post-apnea breathing (Terzano et al 1996). Like an alternatively opening (phase A) and closing (phase B) gate, cyclic alternating pattern phases act as periodic permissive windows in NREM sleep, offering a favorable background for phasic and/or repetitive sleep-related manifestations, ie, periodic limb movements, epileptic seizures, sleep apneas. Accordingly, a number of sleep disorders can be classified pathophysiologically on the basis of their relationship with cyclic alternating pattern and non-cyclic alternating pattern. In particular, periodic limb movement disorder, sleep bruxism, and epileptic manifestations can be considered as phase A-related disorders, whereas sleep apneas are a typical expression of a phase B-related disturbance (Terzano and Parrino 1993). The role of cyclic alternating pattern in primary insomnia. Primary insomnia appears to be the exaggeration of a physiological rhythm ordinarily involved in the sleep process. Previous studies have ascertained that acoustic stimuli enhance the physiological amount of cyclic alternating pattern rate and determine poor sleep and daytime dysfunction even without an increase of sleep fragmentation. In this perspective, cyclic alternating pattern operates as a doubleedged sword. Although limited quantities of cyclic alternating pattern mediate physiological effects, larger quantities reflect the brain's difficulties to consolidate and preserve sleep and, therefore, may be associated with detrimental consequences. Because primary insomnia is not supported by any other sleep, medical, psychiatric, or substanceinduced problem, there is no evidence of an organ disorder. In any case, whatever the nature of disturbance, the outcome is the amplification of an otherwise physiological process. Polysomnography investigation based on an extensive sample of Caucasian patients affected by primary insomnia

7 have demonstrated that cyclic alternating pattern parameters consistently reflect the reduced quality of sleep in insomnia complainers and can substantiate the efficacy of hypnotic medication (Parrino et al 2004). Discriminant analysis indicates that cyclic alternating pattern rate is the most sensitive sleep measure of effective drug treatment, whereas correlation analysis shows that cyclic alternating pattern rate is the polysomnography parameter that better reflects subjective sleep quality (Terzano et al 2003). Similar findings have been confirmed in non-caucasian subjects. In Japanese patients with psychophysiological insomnia, a randomized crossover comparative study with placebo showed that hypnotic treatment (zolpidem) increased sleep stability by significantly improving the overnight cyclic alternating pattern rate as well as subjective sleep quality (Ozone et al 2008). Cyclic alternating pattern parameters are also useful tools to monitor the effects of intermittent hypnotic treatment. In a double-blind study carried out on adults with primary sleep maintenance insomnia lasting longer than 1 month, polysomnography measures and perception of sleep quality were assessed at baseline and during the following 6 consecutive nights of alternating treatment with zolpidem (10 mg) or placebo. Compared to baseline values, cyclic alternating pattern rate, cyclic alternating pattern time, subtype A2, and subtype A1 were significantly reduced with zolpidem treatment and correlated with sleep quality, whereas they did not rebound above baseline with placebo (Parrino et al 2008). An intriguing aspect of insomnia is the established finding that people with this disorder often overestimate the time they take to get to sleep and underestimate the total amount of time they actually sleep. To investigate this mismatching phenomenon, a polysomnography study was carried out in 20 patients with a diagnosis of sleep state misperception or paradoxical insomnia (Parrino et al 2009b). Recruitment of misperceptors without coexisting neurologic, medical, or psychiatric disorders was based on objective total sleep time (TST) of at least 6.5 h, objective sleep latency shorter than 30 min, underestimated difference between objective and subjective TST of at least 120 min, and subjective estimation of sleep latency more than 20% of objective sleep latency. Polysomnography data of misperceptors were compared with those of 20 normal gender- and age-matched subjects (controls). Patients and controls presented nonsignificant differences in the amounts of objective sleep time (464 min vs. 447 min) and objective sleep latency (9 min vs. 8 min). However, compared to controls, misperceptors reported a significantly shorter time of perceived sleep (285 min vs. 461 min; p < ), and a significantly longer duration of perceived sleep latency (51 min vs. 22 min; p < ). Arousal index (32/hour vs. 19/hour; p < ) and total cyclic alternating pattern rate (58% vs. 35%; p < ) were significantly higher in insomniacs. In the sleep period between objective and subjective sleep onset, cyclic alternating pattern rate was 64.4% in misperceptors and 45.1% in controls (p < 0.002). Insomniacs showed significantly higher amounts of cyclic alternating pattern rate in stage 1 (62.7% vs. 37.5%; p < ) and in stage 2 (53.3% vs. 33.2%; p < ), but not in slow-wave sleep. The percentage of subtypes A2, which include both sleep promoting and wake-promoting EEG features, was significantly higher (p < 0.001) in misperceptors (31%) compared to controls (24%). Interestingly, misperceptors reported a limited amount of subjective awakenings (mean: 4) in contrast to objective findings (mean: 11). The mismatch could be explained in part by the high amounts of cyclic alternating pattern between successive awakenings, which were merged together in a single experience. In other words, if sleep between 2 successive awakenings is superficial (expressed by sleep stages 1 and 2), unstable (as reflected by increased amounts of cyclic alternating pattern), and fragmented (increased arousal index), time separating the 2 events is perceived as continuous wake. These findings suggest that difficulty to maintain consolidated sleep is interpreted as wakefulness in misperceptors. Sleep is a dynamic process with a self-regulating character. The nightly recurring sleep process is organized into consecutive cycles in which the sequence of NREM stages and the alternation between NREM and REM sleep show a quite stable tendency and a largely predictable pattern. These constraints produce the macrostructural development of sleep. However, transient EEG changes can interact with the expected development of sleep and ensure adaptation to both internal and external conditions. Cyclic alternating pattern and arousals represent rapid adaptive adjustments of vigilance during sleep. Failure of these compensatory processes conducts to non-restorative sleep. Therefore, assessment of sleep quality relies on a variety of polysomnography measures, including sleep duration (quantified by total sleep time and sleep efficiency), sleep intensity (reflected by stages 3 and 4), sleep continuity (altered by nocturnal awakenings and arousals), and sleep stability (impaired by excessive amounts of cyclic alternating pattern). These polysomnography measures are susceptible to deterioration in varying ways and proportions in accordance to the manifold clinical manifestations of insomnia. In the hierarchy of polysomnography measures, cyclic alternating pattern variables appear to be the most sensitive to any source of internal or external perturbation during sleep. Regardless of the specific characteristics of sleep alteration, insomnia ceases to be an indefinite mental disorder, but emerges as a subjective disturbance supported by measurable neurophysiological changes (Parrino et al 2004).

8 The importance of sleep instability. Objection could be raised on the usefulness of cyclic alternating pattern in routine clinical practice. Besides the consideration that the authors apply cyclic alternating pattern in their daily activities, attention should be focused on the philosophy that lies behind the cyclic alternating pattern scoring methodology. The main assumption of cyclic alternating pattern is that we must consider that during certain periods of the night the arousal level is unstable. The concept of instability is a basic issue of all complex systems and supports the dynamics of biological variability. Within certain ranges, instability warrants flexible and adaptive features to the complex system. In normal sleep, cyclic alternating pattern accompanies the stage transitions maintaining in-phase both the EEG and autonomic functions through regular fluctuations. This means that what we observe on the peripheral sensors (respiratory events, heart rate variations, blood pressure shifts, myoclonic jerks) has a consistent and synchronous expression on the EEG and vice versa. In other words, cyclic alternating pattern allows us to read sleep as a musical score where all the instruments play a coordinated harmony. This means that even without the EEG leads, the detection of an unstable cardiorespiratory pattern is certainly associated with an oscillatory behavior of the cerebral activities. To guarantee survival during prolonged unconsciousness (ie, sleep), a strong interaction among all the biological subsystems is mandatory. Accordingly, life-threatening events, eg, repetitive apneas, enhance the brain's necessity to increase the number of protective arousals determining a metronomic setting of oscillations expressed by prolonged cyclic alternating pattern sequences. Cyclic alternating pattern scoring incorporates all the conventional EEG arousals in NREM sleep and provides additional information supplied by the frontal EEG arousals (subtype A1), which are omitted by the official classification. All this remains unexplored using only the stepwise description of sleep stages. In particular, the new AASM rules have oversimplified the sleep process, merging stages 3 and 4 into stage N3 and overemphasizing the role of single arousals in stage scoring; the new AAMS rules are simply tools offered to accelerate scoring procedures in routine practice. However, it is a common experience that a number of patients remain inadequately diagnosed and treated in spite of an apparently normal sleep structure. A recent study carried out in patients with restless legs syndrome and periodic limb movements during sleep demonstrates that dopamine-agonist medication reduces the amount of leg jerks, but maintains high levels of cyclic alternating pattern with persistence of non-restorative sleep (Ferri et al 2010). Similar controversies happen daily in sleep labs that ascertain maintenance of excessive sleepiness in obstructive sleep apnea syndrome patients even when CPAP restores normal apnea-hypopnea index measures. An exploratory approach using the cyclic alternating pattern framework would probably shed light on these controversial cases. It is known that CPAP titration detached from the concomitant assessment of cyclic alternating pattern can jeopardize the effectiveness of ventilatory treatment (Thomas 2002). The persistence of cyclic alternating pattern and arousals even when respiratory events are controlled by autoadjust equipment indicates an inadequate titration procedure. Finally, the sensitivity of cyclic alternating pattern to medication in insomniac patients could be exploited in the evaluation of new sleep-promoting drugs. So far, hypnotic compounds have based their effectiveness on the paradigm of sleep-onset promotion, sleep-time enhancement, and wake after sleep onset (WASO) reduction. All the available hypnotic agents, especially those belonging to the gamma aminobutyric acid (GABA) family, share these features, which paradoxically offer a flat undifferentiated therapeutic scenario. The new sleep drugs in the pipeline, acting on alternative targets (melatonin, orexin, serotonin), need a new cultural framework that cannot be limited to sleep latency and sleep duration, but also needs to incorporate the issue of sleep and autonomic stability. So far, these objectives have been neglected by most clinical trials for drug approval. Comparing gaboxadol and zolpidem in a model of situational insomnia, cyclic alternating pattern parameters showed a significant independence from other electrophysiological measures (including spectral analysis), disclosed a strong association with subjective sleep quality, and allowed discrimination between the administered drugs. Zolpidem has also been used in a noninsomnia setting. In a recent trial, most patients with idiopathic central sleep apnea experienced a decrease in central apnea/hypopneas with zolpidem. They also had improved sleep continuity, reduced total number of arousals, and decreased subjective daytime sleepiness, without worsening of oxygenation or obstructive events. In obstructive sleep apnea, trazodone increased respiratory effort related arousal threshold in response to hypercapnia and allowed patients to tolerate a higher CO2 level. According to Younes, the percentage of time spent in instability is related to how far removed from stability the system is, relative to how much changes in the stability factors can occur spontaneously during the night. Some of the factors that determine stability change from time to time during sleep. Chief among these is arousal threshold (Younes 2008). A recent study established that trazodone increases the respiratory arousal threshold in obstructive sleep apnea patients with low arousal threshold indicating the potential inclusion of non-gabaergic agents in the management of sleep-related breathing disorders (Eckert et al in press).

9 Automatic analysis of cyclic alternating pattern. There is consolidated evidence that cyclic alternating pattern parameters provide more detailed information and are significantly more sensitive than conventional sleep measures. However, the visual scoring of cyclic alternating pattern is time-consuming, and this can compromise an extensive utilization of the method. In other words, only the availability of an adequate system for the automatic detection of cyclic alternating pattern can really make it an easily exploitable tool. The problem of automatic cyclic alternating pattern recognition has been addressed in several studies, with interesting results (Barcaro et al 2004; Ferri et al 2005c; Largo et al 2005). However, most proposed classification methods required the preliminary measurement of sleep macrostructure, making it necessary for the human scorer to visually classify the sleep profile. Moreover, they relied on the extraction of spectral parameters from the EEG signal to compute descriptors on time-windows and on the application of machine-learning algorithms for classifying each window as belonging to a phase A of cyclic alternating pattern or not. A preliminary study investigated a set of descriptors computed on 1-second-long windows with different techniques, and the information content of each descriptor was evaluated by means of ROC curves (Mariani et al 2011). Those descriptors were used in a subsequent work training 4 different classifiers for the classification of cyclic alternating pattern A phases, where the linear discriminant resulted to be the most accurate, with an average accuracy equal to 83% (Mariani et al 2012). However, due to the intrinsic characteristics of cyclic alternating pattern phases A, which consist of transient, non-stationary phenomena, it is likely that the signal will vary its characteristics over time, and it is not convenient to define a fixed window length. A dataset of 16 polysomnographic recordings from healthy subjects was employed, and the EEG traces first underwent an automatic isolation of NREM sleep portions by means of an Artificial Neural Network and then a segmentation process based on the Spectral Error Measure. The information content of the descriptors was evaluated by means of ROC curves and compared with that of descriptors obtained without the use of segmentation. Finally, the descriptors were used to train a discriminant function for the automatic classification of cyclic alternating pattern phases A. With respect to previous scoring methods, a significant improvement in terms of both information content carried by the descriptors and accuracy of the classification was obtained. Average cyclic alternating pattern rate obtained with automatic scoring was 44.83±12.41 vs ±10.13 measured with visual analysis. The correlation coefficient between the 2 series of cyclic alternating pattern rates was 0.73, and the P-value for the null hypothesis that the 2 populations had the same distribution was 0.62 (Mariani et al 2013). Although the sensitivity was still not very high (69.55±6.6), the specificity (90.49±2.8) and the accuracy (87.19±2.48) had a significant increase with respect to the previous study, indicating that EEG segmentation proves to be a useful step in the computation of descriptors for cyclic alternating pattern scoring (Mariani et al 2012). Conclusion. A certain amount of information remains uncovered if we limit our assessment to macrostructural events. As a marker of sleep instability, cyclic alternating pattern rate may be enhanced by a number of internal or external factors. Cyclic alternating pattern rate is a highly sensitive, poorly specific index as it cannot reveal the nature of perturbation. However, it certainly indicates that 1 or more factors are interfering with the processes of sleep consolidation and quantifies the magnitude of perturbation. Specificity of the disturbance factor is mainly supplied by the phase A subtype, which is differently modified by sleep disorders and medication. In contrast, the presence of noncyclic alternating pattern is closely related to a global condition of stability when all the subsystems that control and influence sleep mechanisms have achieved a reciprocally balanced interaction. From the cyclic alternating pattern/non-cyclic alternating pattern perspective, analysis of sleep microstructure is not limited to the finding of a single event (eg, an isolated arousal), but to the identification of a pattern (presence or absence of a cyclic alternating pattern sequence) that translates a physiological state involving cerebral activities, autonomic functions, and behavioral features. In other words, what happens upstairs (brain) is reflected at the lower levels (autonomic and muscle parameters) and vice versa. Once we attribute an activation relevance to all cyclic alternating pattern A phases (including K-complexes and delta bursts), we overcome the barren contrast between visible and non-visible arousals, which for years has frozen the possibility of understanding the flexible capacity of the brain to use different EEG features in different neurophysiological situations. 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