SEARCH

SEARCH BY CITATION

Keywords:

  • development;
  • quantitative analysis;
  • REM sleep without atonia;
  • submentalis electromyogram

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

The current definition of rapid eye movement (REM) sleep without atonia has no quantitative character, and cut-off values above which the level of electromyographic tone can be considered to be ‘excessive’ are unclear. The aim of this study was to analyse the characteristics of chin electromyographic amplitude by means of an automatic approach in a large group of normal controls, subdivided into different age groups. Eighty-eight normal controls were included, subdivided into six age groups: preschoolers (≤6 years); schoolers (6–10 years); preadolescents (10–13 years); young adults (24–40 years); middle-aged (58–65 years); and old (>65 years). The average amplitude of the rectified submentalis muscle electromyographic signal was used for the computation of the REM sleep Atonia Index. Chin muscle activations were detected, and their amplitude, duration and interval analysed. REM sleep Atonia Index showed a progressive and rapid increase from the preschool age to school and preadolescent age, reaching the maximum in the young adult group; after this age a small decline was observed in the middle-aged and old subjects. Conversely, the number of movements per hour in REM sleep showed a ‘U’-shaped distribution across these age groups, with the minimum in the preadolescent group and the two extremes (preschool age and old) showing similar average levels of activity. Our results show that REM sleep atonia develops continuously during the lifespan, and undergoes complex changes with different developmental trajectories for REM atonia and electromyographic activations during REM sleep. Different mechanisms might subserve these two phenomena and their differential developmental dynamics.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

Rapid eye movement (REM) sleep behaviour disorder (RBD) is a parasomnia where the physiological atonia during REM sleep is absent or greatly diminished, and which is characterized by dream-enacting behaviour associated with nightmares (Boeve, 2010). The main polysomnographic feature of RBD, REM sleep without atonia (RWA), needs to be defined better because its current description has no quantitative character (American Academy of Sleep Medicine, 2005). Without well-established normative values for the physiological electromyographic (EMG) atonia, it is impossible to indicate cut-off values above which the amount of intermittent or sustained elevation of EMG tone can be considered to be ‘excessive’ (American Academy of Sleep Medicine, 2005).

In recent years some methods have been proposed to automatically quantify the amplitude of the chin EMG signal during REM sleep (Burns et al., 2007; Ferri et al., 2008a,b, 2010; Mayer et al., 2008), with the aim to use them as a marker of its alteration and, eventually, to support the diagnosis of RBD. Recently, we have also reported that our quantitative method is able not only to disclose differences between normal controls and patients with RBD, but also between nosologically different groups of patients who seem to present with different types of REM sleep-related motor disturbances (Ferri et al., 2008a,b). What today is lumped together under the term of RBD, might in the future be revealed to have different underlying neurochemical and neurophysiological mechanisms, depending on whether it is observed in its idiopathic form or in patients with synucleinopathies (e.g. multiple system atrophy; Ferri et al., 2008b) or narcolepsy (Ferri et al., 2008a).

The characteristics of this automatic approach need now to be described better in a normal population in order to be used more widely for clinical reasons. In particular, developmental aspects of REM sleep atonia have not received much attention, although RBD can occur in children and adolescents (Sheldon and Jacobsen, 1998; Stores, 2008) in whom it might be underdiagnosed, especially in association with narcolepsy. Virtually no data on automated quantitative chin EMG amplitude are currently available in children and, given the increasing interest in the diagnosis of RBD in the paediatric population (Maski and Kothare, 2009; Nevsimalova et al., 2007; Vendrame et al., 2008), the aim of this new study was to analyse the characteristics of chin EMG amplitude by means of our automatic approach in a large group of normal controls, subdivided into different age groups, covering a large part of the lifespan.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

Subjects

Eighty-eight normal controls were included in this study, whose recordings were available in the lab database because they had participated in various research protocols (ethics committee approval and informed consent had been obtained for each of the protocols). The subjects were subdivided into six age groups: preschoolers (age ≤ 6 years); schoolers age (>6, <10 years); preadolescents age (≥10, <13 years); young adults (age > 24, <40 years); middle aged (age > 58, ≤65 years); and old (age > 65 years); their demographic features are detailed in Table 1. None of these subjects had any physical, neurological or psychiatric disorders or history of sleep problems, and none was taking medication at the time of recording. Exclusion criteria included: (i) a sleep disorder diagnosis (including RBD, sleep apnoea, restless legs syndrome/periodic leg movement disorder); (ii) a major mental illness; (iii) a significant history of cognitive difficulties; (iv) prior (within 1 year) or current use of a neuroleptic agent or selective serotonin reuptake inhibitors, venlafaxine, duloxetine; (v) history of alcohol or other substance abuse.

Table 1.   Demographic features of the subject groups
GroupMalesFemalesTotalAge
MeanSD
Preschool77144.91.14
School97167.30.83
Preadolescent771410.40.76
Young12132529.73.57
Middle-aged461062.22.17
Old36973.46.30

Nocturnal polysomnography

Nocturnal polysomnography was carried out after a night of adaptation in a standard sound-attenuated (noise level to a maximum of 30 dB nHL) sleep laboratory room. Subjects were not allowed caffeinated beverages the afternoon preceding the recording, and were allowed to sleep-in until their spontaneous awakening in the morning. Lights-out time was based on individual habitual bed time, and ranged between 21:30 and 23:30 h. The following signals were recorded: electroencephalogram (at least two channels, one central and one occipital, referred to the contralateral earlobe); electrooculogram (electrodes placed 1 cm above the right outer cantus and 1 cm below the left outer cantus, and referred to A1); electromyogram (EMG) of the submentalis muscle (bipolar derivations with two electrodes placed 3 cm apart and affixed using a collodion-soaked gauze pad), impedance was kept <10 kΩ (typically <5 kΩ); EMG of the right and left tibialis anterior muscles; and electrocardiogram (one derivation). Sleep signals were sampled at 200 or 256 Hz, and stored on hard disk for further analysis. The sleep respiratory pattern of each patient was monitored using oral and nasal airflow thermistors and/or nasal pressure cannula, thoracic and abdominal respiratory effort strain gauge, and by monitoring oxygen saturation (pulse-oxymetry). Subjects with an apnoea/hypopnoea index > 5 were not included (>1 in the paediatric ages). Sleep stages were scored following standard criteria (Rechtschaffen and Kales, 1968) on 30-s epochs; epochs containing technical artefacts or extremely elevated muscle activity causing saturation of amplifiers were carefully detected and marked for exclusion from the subsequent quantitative EMG analysis.

Quantification of the submentalis muscle EMG amplitude

Similarly to the method we have previously reported (Ferri et al., 2008a,b, 2010), the submentalis muscle EMG signal was digitally band-pass filtered at 10–100 Hz, with a notch filter at 50 Hz, and rectified; subsequently, all REM sleep epochs were included in the analysis and subdivided into 30 1-s mini-epochs. The average amplitude of the rectified submentalis muscle EMG signal was then obtained for each mini-epoch. Then a noise reduction method was implemented (Ferri et al., 2010) by subtracting from the average rectified EMG amplitude in each mini-epoch the minimum value found in a moving window of ±30 mini-epochs (assuming that the minimum value in this 60-s period is a good estimate of the local level of noise affecting all sampled signal values in such a period). The values of the average noise-reduced EMG signal amplitude in each mini-epoch were used to draw distribution histograms of the percentage of values in the following 20 amplitude (amp) classes (expressed in μV): amp ≤ 1, 1 < amp ≤ 2, …, 18 < amp ≤ 19, amp ≥ 20. The REM sleep Atonia Index was then computed as:

  • image

which reflects the degree of preponderance of the first column in these graphs (amp ≤ 1) in relation to the cumulative value of all columns with amp > 2 and amp ≥ 20 (the column 1 < amp ≤ 2 is not taken into account because it might reflect both atonia and EMG activation). Mathematically, this index can vary from 0 (absence of mini-epochs with amp ≤ 1), which means complete absence of EMG atonia, to 1 (all mini-epochs with amp ≤ 1) or continuous stable EMG atonia during REM sleep.

Subsequently, we also counted all sequences of consecutive mini-epochs exceeding the value of 2 μV (which we will also call ‘movements’) and calculated their number per hour of REM sleep. The duration of these movements was classified within the following 20 duration (dur) classes (expressed in s): dur = 1, dur = 2, …, dur = 19, dur > 19. Distribution histograms of the duration of movements, normalized to the number per hour of REM sleep, were drawn for each age group. Lastly, the intervals between these movements were measured and plotted as distribution histograms, within the following 49 interval (int) classes (expressed in s): int = 2–4, int = 4–6, …, int = 98–100.

Statistical data analysis

Overall-comparisons between age groups were carried out using the non-parametric Kruskal–Wallis anova for independent data sets. Differences and correlations were considered significant when they reached a P < 0.05 level. In addition, a polynomial (cubic or quartic) interpolation line was computed to depict the course of REM sleep Atonia Index and total number of movements per hour across age groups by using ungrouped individual data. The data analysis software system STATISTICA (StatSoft, 2004, version 6. http://www.statsoft.com) was used for statistical analysis.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

Table 2 reports, in detail, the descriptive statistics for sleep architecture in the different age groups. Age-related changes were as expected and, in particular, a normal amount of REM sleep was observed in all subjects.

Table 2.   Descriptive statistics of sleep architecture of the subject groups
 Preschool (n = 14)School (n = 16)Preadolescent (n = 14)Young (n = 25)Middle-aged (n = 10)Old (n = 9)
MeanSDMeanSDMeanSDMeanSDMeanSDMeanSD
  1. AWN h−1, awakenings per hour; REM, rapid eye movement sleep; S1, S2, sleep stages 1 and 2; SE, sleep efficiency; SPT, sleep period time; SS h−1, stage shifts per hour; SWS, slow-wave sleep; TIB, time in bed; TST, total sleep time; WASO, wakefulness after sleep onset.

TIB, min563.750.79543.441.01553.960.48438.541.59526.692.76487.761.39
SPT, min531.761.76510.744.02514.856.41422.343.28493.671.29457.177.90
TST, min518.672.63496.845.99482.160.07393.736.51410.847.06345.177.71
Sleep latency, min27.118.6030.726.5919.615.2112.713.5129.037.8318.718.20
REM latency, min90.246.25110.147.79122.457.21106.274.9181.919.77107.6109.11
SS h−18.01.918.83.085.11.309.82.5913.13.3512.24.88
AWN h−10.60.830.80.950.60.632.62.146.03.377.53.67
SE, %91.87.0191.55.5687.27.4490.06.3879.211.6171.014.71
WASO, %2.74.762.72.516.27.316.55.6916.19.8024.612.91
S1, %2.31.362.82.305.73.773.34.006.84.727.24.11
S2, %47.46.6543.97.2846.58.7448.87.4046.87.5144.412.54
SWS, %25.34.9127.04.4121.33.9321.37.4813.87.0011.28.66
REM, %22.44.3423.54.8520.34.3120.16.6416.55.3612.61.86

The average REM sleep Atonia Index showed a progressive and rapid increase from the preschool age (mean 0.811 ± 0.101 SD) to school (mean 0.879 ± 0.065 SD) and preadolescent age (mean 0.925 ± 0.042 SD), reaching the maximum in the young adult group of subjects (mean 0.962 ± 0.021 SD); after this age a small decline was observed in the middle-aged (mean 0.936 ± 0.034 SD) and old (mean 0.929 ± 0.042 SD) subjects (Fig. 1). Conversely, the number of movements per hour in REM sleep showed a ‘U’-shaped distribution across these age groups of subjects, with the minimum in the preadolescent group and the two extremes (preschool age and old) showing similar average levels of activity.

image

Figure 1.  Rapid eye movement (REM) sleep Atonia Index (top panels) and number of movements per hour in REM sleep (bottom panels) in the different groups of subjects. In the left panels, values are shown as mean (columns) and standard error (whiskers). In the right panels, the polynomial interpolation line of individual data is shown (continuous line), together with the 95% confidence intervals (dotted lines).

Download figure to PowerPoint

In accordance with the results for the REM sleep Atonia Index described above, the corresponding distribution histograms of mini-epochs amplitude (Fig. 2) show a progressive increase in the percentage of mini-epochs with amplitude ≤1 μV (first column on the left, Fig. 2) from the preschool age to school-age children and preadolescents, reaching a maximum in young adults; similarly to the REM sleep Atonia Index, after this age a small decline was observed in the middle-aged and old groups. Note that for each age group the left first column of each graph represents the percentage of atonic mini-epochs, and the sum of the third to last column represents the percentage of mini-epochs contributing to RWA.

image

Figure 2.  Distribution histograms of the average EMG amplitude in 1-s mini-epochs during REM sleep. The percentage of values in 20 amplitude (amp) classes is reported (1 amp ≤ 1, 2 = 1 < amp ≤ 2, …, 19 = 18 < amp ≤ 19, 20 = amp ≥ 20 μV); values are shown as mean (columns) and standard error (whiskers).

Download figure to PowerPoint

The normalized distribution histograms of duration of movements (consecutive mini-epoch sequences exceeding 2 μV in amplitude; Fig. 3) show the same trend across age groups seen for the total number of movements per hour in REM sleep, with a ‘U’-shaped course and a minimum in the preadolescent group. Remarkably, in all age groups there is a progressive mono-modal decline from the shortest to the longest chin EMG activations.

image

Figure 3.  Distribution histograms of consecutive mini-epoch sequences with EMG amplitude exceeding 2 μV. The number per hour of REM sleep in 20 duration classes is shown. All values are shown as means and standard errors (whiskers).

Download figure to PowerPoint

Finally, Fig. 4 depicts the normalized distribution histograms of between movements’ intervals. For none of the age groups does the graph show any indication for the presence of a periodic activity in the chin EMG during REM sleep.

image

Figure 4.  Distribution histograms of intervals between consecutive mini-epoch sequences with EMG amplitudes exceeding 2 μV. All values are shown as mean and standard error (whiskers).

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

The main result of this study is represented by the increasing level of atonia during REM sleep with age, during a long period of life from childhood to young adulthood, and by the possible reversal of this trend in the later stages of life, without returning to levels as low as during childhood. It is difficult to compare these results with previous data in the literature because of the novelty of the method and the scarcity of this type of analysis in humans. However, Kohyama et al. (1999) reported a ‘tonic inhibition index’– the ratio between short (<2 s) and total (short + long) chin EMG activations during REM sleep, believed to reflect the mechanism of muscle atonia – that increased significantly with age, reaching an adult level at preadolescence.

Our results show that REM sleep atonia develops continuously during the lifespan, and undergoes complex changes with different developmental trajectories for REM atonia and movements during REM sleep. These two parameters move in opposite directions because sleep architecture and microstructure change with age, with older subjects having a more fragmented sleep (Bonnet and Arand, 2007; Boselli et al., 1998; Parrino et al., 1998, 2001) and, consequently, an increased number of movements is found both during non rapid eye movement (NREM) sleep and REM sleep. In the case of RBD, our results suggest that a pathological process disrupts the age-related physiological development, with the appearance of an increased number of movements and decreased REM sleep atonia (Ferri et al., 2008a,b, 2010). It is known that REM sleep atonia can be detected as early as just after 40 weeks gestational age (Parmelee and Stern, 1972; Schulte et al., 1977), and additional data are required to fill up the age gaps between our subject groups. However, the trend evident for both grouped and ungrouped data (left and right panels of Fig. 1) is clearly visible and significant, and shows that REM sleep atonia develops over a long period up to adulthood, before showing a small but visible decline in middle-aged and older individuals. There is also some indication of increased variability of REM sleep atonia in the oldest age groups, but this has to be confirmed in larger groups. Our findings of low and rapidly evolving atonia in the paediatric groups could point to the possibility that RBD can occur at preschool and school age. However, recognizing the phenotype in this age group might be quite difficult as it might be mistaken for NREM-associated parasomnias or nightmares.

Several mechanisms seem to determine and modulate the signal outflow to skeletal muscles during REM sleep, including increased glycinergic and γ-aminobutyric acid (GABA)A-mediated inhibition (Chase et al., 1989; Morrison et al., 2003), decreased noradrenergic and serotonergic excitation (Chan et al., 2006; Fenik et al., 2005), and hypocretinergic state-dependent modulation (Yamuy et al., 2010). Thus, REM atonia is most probably not mediated by a single or leading pathway, but rather by a complex network including many different neurotransmitter mechanisms (Boeve, 2010; Brooks and Peever, 2008). This complexity and the interactions occurring between multiple and different neurotransmitter pathways, probably with different developmental speed and features, could account for the complex and non-linear course of REM sleep muscle atonia observed in our study.

Animal studies suggest that there might be two separate motor systems playing a role in normal REM sleep, one involved in the generation of muscle atonia and another for the suppression of motor activity (Boeve, 2010); the latter is very effective in suppressing elaborate motor activity, while phasic oculomotor (REMs) and locomotor activity (low-amplitude muscle twitches) are allowed and occur as normal distinctive phenomena of REM sleep (Mahowald and Schenck, 2000). Our data show that the course of the amount of chin EMG activations during REM sleep (movements) across the age range is different from that of REM atonia. This might be interpreted as an effect of the different mechanisms underlying these two phenomena and their differential developmental dynamics.

It should be noted that, as already reported earlier (Ferri et al., 2008a,b, 2010; Mayer et al., 2008), also in this study and at all ages, we have found a mono-modal distribution for the duration of chin EMG activations with a progressive decrease from the shortest to longer durations. This seems to indicate that the classical distinction between ‘phasic’ and ‘tonic’ activations needs to be reconsidered on a statistical background and possibly corrected. This is because the form of the distribution argues for a single underlying process, and we would have expected at least two peaks in the distribution of movement durations if different mechanisms were at operation. One might argue that this can be interpreted as being in contrast with the above-mentioned separation of mechanisms underlying REM atonia and motor suppression in REM sleep. However, it is important to consider that the parameters obtained with our approach have previously shown that a severe alteration of the REM sleep atonia can coexist with a relatively low number of EMG activations in patients with multiple system atrophy (Ferri et al., 2008b). This reinforces the idea that continuous functions characterize the distribution of number and duration of the increased chin tone events, and that considering them in a single model can disclose different patterns of abnormality in different conditions. The data collected so far indicate that our approach is able to take this into account and to describe reliably these phenomena.

Finally, similarly to our previous studies (Ferri et al., 2008a,b), we have found that at all ages considered, the distribution of the inter-movement intervals is always mono-modal, with a maximum for the shortest intervals (2–6 s) and rapidly decreasing values for longer intervals. This type of distribution is very similar to that expected for a noise with spectral density 1/f or 1/f2 (Brownian); Brownian noise, as an example, is generated by adding a random offset to each sample to obtain the next one. These considerations support our notion that the time structure of EMG activations during REM sleep does not display periodicity but is rather generated randomly.

One possible limitation of this study is the gap between the different age groups that was determined by the availability of recordings in our database; however, the homogeneity of results within each age group makes it likely that the age-related trajectories will be confirmed in larger studies. In any case, one of the main messages of this study is to consider age carefully when evaluating chin EMG tone during REM. Another possible limitation resides in the fact that we did not separate genders (because we would have needed a significantly higher number of subjects to perform reliable statistical analyses) but, again, the relatively small standard error of the average values found in the different age groups and the relatively narrow range of the 95% confidence intervals of the polynomial function fittings indicate that a gender effect can be expected to be small. However, careful analysis of the eventual gender differences is needed and will certainly be the focus of future analyses. In addition, the correlation between characteristics of chin tone during REM sleep and other movement-related parameters, such as REM activity or leg movements during sleep, should be the topic of additional studies on the generators of the different movement activities recorded during REM sleep.

In conclusion, this study supports the robustness of the quantitative measurement of the chin EMG amplitude during REM sleep provided by our method, demonstrates that important and non-linear age-related changes occur in these measures across different age groups, and provides new information potentially useful for the understanding of the complex mechanisms underlying muscle atonia and motor suppression during physiological REM sleep.

Acknowledgement

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

This study was partially supported by the Italian Ministry of Health (‘Ricerca Corrente’ and ‘Cinque per Mille’).

Conflict of Interest

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References

None of the authors has a potential conflict of interest to disclose with the content of this manuscript.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Conflict of Interest
  9. References
  • American Academy of Sleep Medicine. International Classification of Sleep Disorders, 2nd edn: Diagnostic and Coding Manual. American Academy of Sleep Medicine, Westchester, IL, 2005.
  • Boeve, B. F. REM sleep behavior disorder: updated review of the core features, the REM sleep behavior disorder-neurodegenerative disease association, evolving concepts, controversies, and future directions. Ann. N Y Acad. Sci., 2010, 1184: 1554.
  • Bonnet, M. H. and Arand, D. L. EEG arousal norms by age. J. Clin. Sleep Med., 2007, 3: 271274.
  • Boselli, M., Parrino, L., Smerieri, A. and Terzano, M. G. Effect of age on EEG arousals in normal sleep. Sleep, 1998, 21: 351357.
  • Brooks, P. L. and Peever, J. H. Unraveling the mechanisms of REM sleep atonia. Sleep, 2008, 31: 14921497.
  • Burns, J. W., Consens, F. B., Little, R. J., Angell, K. J., Gilman, S. and Chervin, R. D. EMG variance during polysomnography as an assessment for REM sleep behavior disorder. Sleep, 2007, 30: 17711778.
  • Chan, E., Steenland, H. W., Liu, H. and Horner, R. L. Endogenous excitatory drive modulating respiratory muscle activity across sleep-wake states. Am. J. Respir. Crit. Care Med., 2006, 174: 12641273.
  • Chase, M. H., Soja, P. J. and Morales, F. R. Evidence that glycine mediates the postsynaptic potentials that inhibit lumbar motoneurons during the atonia of active sleep. J. Neurosci., 1989, 9: 743751.
  • Fenik, V. B., Davies, R. O. and Kubin, L. REM sleep-like atonia of hypoglossal (XII) motoneurons is caused by loss of noradrenergic and serotonergic inputs. Am. J. Respir. Crit. Care Med., 2005, 172: 13221330.
  • Ferri, R., Franceschini, C., Zucconi, M. et al. Searching for a marker of REM sleep behavior disorder: submentalis muscle EMG amplitude analysis during sleep in patients with narcolepsy/cataplexy. Sleep, 2008a, 31: 14091417.
  • Ferri, R., Manconi, M., Plazzi, G. et al. A quantitative statistical analysis of the submentalis muscle EMG amplitude during sleep in normal controls and patients with REM sleep behavior disorder. J. Sleep Res., 2008b, 17: 89100.
  • Ferri, R., Rundo, F., Manconi, M. et al. Improved computation of the atonia index in normal controls and patients with REM sleep behavior disorder. Sleep Med., 2010, 11: 947949.
  • Kohyama, J., Tachibana, N. and Taniguchi, M. Development of REM sleep atonia. Acta Neurol. Scand., 1999, 99: 368373.
  • Mahowald, M. W. and Schenck, C. H. REM sleep behavior disorder. In: M. Kryger, T. Roth and W. Dement (Eds) Principles and Practice of Sleep Medicine. WB Saunders, Philadelphia, PA, 2000: 724741.
  • Maski, K. P. and Kothare, S. V. Searching for marker of REM sleep behavior disorder: submentalis muscle EMG amplitude analysis during sleep in patients with narcolepsy/cataplexy. Sleep, 2009, 32: 137.
  • Mayer, G., Kesper, K., Ploch, T. et al. Quantification of tonic and phasic muscle activity in REM sleep behavior disorder. J. Clin. Neurophysiol., 2008, 25: 4855.
  • Morrison, J. L., Sood, S., Liu, H. et al. Role of inhibitory amino acids in control of hypoglossal motor outflow to genioglossus muscle in naturally sleeping rats. J. Physiol., 2003, 552: 975991.
  • Nevsimalova, S., Prihodova, I., Kemlink, D., Lin, L. and Mignot, E. REM behavior disorder (RBD) can be one of the first symptoms of childhood narcolepsy. Sleep Med., 2007, 8: 784786.
  • Parmelee, A. H. and Stern, E. Development of states in infants. In: C. D. Clement, D. P. Purpura and F. E. Mayer (Eds) Sleep and the Maturing Nervous System. Academic Press, New York, 1972: 199228.
  • Parrino, L., Boselli, M., Spaggiari, M. C., Smerieri, A. and Terzano, M. G. Cyclic alternating pattern (CAP) in normal sleep: polysomnographic parameters in different age groups. Electroencephalogr. Clin. Neurophysiol., 1998, 107: 439450.
  • Parrino, L., Smerieri, A., Rossi, M. and Terzano, M. G. Relationship of slow and rapid EEG components of CAP to ASDA arousals in normal sleep. Sleep, 2001, 24: 881885.
  • Rechtschaffen, A. and Kales, A. A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects. Washington Public Health Service, US Government Printing Office, Washington, DC, 1968.
  • Schulte, F. J., Busse, C. and Eichhorn, W. Rapid eye movement sleep, motoneurone inhibition, and apneic spells in preterm infants. Pediatr. Res., 1977, 11: 709713.
  • Sheldon, S. H. and Jacobsen, J. REM-sleep motor disorder in children. J. Child Neurol., 1998, 13: 257260.
  • Stores, G. Rapid eye movement sleep behaviour disorder in children and adolescents. Dev. Med. Child Neurol., 2008, 50: 728732.
  • Vendrame, M., Havaligi, N., Matadeen-Ali, C., Adams, R. and Kothare, S. V. Narcolepsy in children: a single-center clinical experience. Pediatr. Neurol., 2008, 38: 314320.
  • Yamuy, J., Fung, S. J., Xi, M. and Chase, M. H. State-dependent control of lumbar motoneurons by the hypocretinergic system. Exp. Neurol., 2010, 221: 335345.