Improving the electrophysiological measurement of REM without atonia in the diagnosis of REM sleep behaviour disorder

Summary

• REM sleep behaviour disorder is frequently observed in the synucleinopathies (Parkinson’s disease, Lewy Body Dementia and Multiple System Atrophy).
• Recent evidence concludes that REM sleep behaviour disorder is a biomarker heralding the future development of an alpha synucleinopathy.
• Normally, muscles should demonstrate electrical silence (atonia) during REM sleep. The demonstration of REM without atonia is critical to the diagnosis of RBD represents the electrophysiological hallmark of RBD.
• Multiple physiological tools, including visual scoring systems and automated signal processing algorithms have been developed to improve the objectivity of the measurement of REM without atonia.
• The emergence of techniques to measure REM without atonia has raised several technical questions regarding the data collection and the method in which it is analysed. 

Introduction

REM sleep behaviour disorder (RBD) is frequently observed in the synucleinopathies (Parkinson’s disease, Lewy Body Dementia and Multiple System Atrophy). Within Parkinson’s disease cohorts RBD is  observed in more than half of all patients with PD¹ and has been linked with the akinetic rigid phenotype of PD², visual hallucinations³, selective cognitive deficits⁴ and dementia². As such accurate screening and diagnosis is essential for managing the comorbidity associated with RBD in PD⁵.  In addition, the emergence of RBD in later life can represent a pre-motor feature heralding the development of synucleinopathy and may thus have utility as a future biomarker⁶. Recent studies have suggested that almost all patients with idiopathic RBD will develop a neurodegenerative disorder, most probably an α synucleinopathy, if they live long enough^{7, 8}. The predominance of synucleinopathies was also reported by Boeve et al 2013 in a clinicopathological study of 172 cases of RBD⁹.

The diagnosis of RBD is based on patient history in conjunction with the demonstration REM without atonia noted in the surface EMG of the mentalis muscle during polysomnography¹⁰. Given the importance in accurately identifying RBD, multiple physiological tools have been developed to improve objective diagnosis. Specifically these tools have been focused on the measurement of REM sleep without atonia (RWA).  Normally, muscles should demonstrate electrical silence (atonia) during REM sleep, thus RWA represents the electrophysiological hallmark of RBD. In addition to visual scoring systems to quantify RWA, automated signal processing algorithms have been developed to improve the objectivity of this measurement^{11, 12}. This review aims to investigate the use of RWA derived from surface EMG collected during polysomnography to identify RBD in PD.

REM without atonia in RBD

The identification of RWA has become critical to the diagnosis of RBD. However, the precise chemical and neural mechanisms of RWA are yet to be determined. Evidence from animal models suggest a structure in the pons referred to as the subcoeruleus or lateral dorsal tegmentum is responsible for normal atonia expected in the REM phase of sleep¹³. REM atonia is proposed to be controlled through the ratio of cholinergic to aminergic tone, differentially activating kainite receptors in the midbrain¹⁴. Given that RBD can pre date the motor diagnosis of PD by up to 15 years¹⁵, the regions suggested in these animal models are in agreement with structures expected to be damaged through the deposition of alpha synuclein in the pre-clinical stages of the Braak pathological staging system¹⁶. However, attributing RWA to a structural deficit in the brainstem raises several questions pertaining to the varying clinical phenotypes of RBD. Principally RBD appears as paroxysmal nocturnal episodes with varying frequency and severity among patients¹⁷. There is some evidence that the night to night variability of RWA is relatively constant in idiopathic RBD¹⁸, however this is yet to be confirmed within PD cohorts. Furthermore, varying severities of RWA with concomitant RBD have been reported in the literature¹⁷. Studies proposing cut scores for the amount of RWA consistent with RBD suggest that despite reaching an electrophysiological diagnosis of RBD, the majority of the REM sleep remains normal (atonic)^{12, 19}. It may also be the case that sub-clinical RBD exists²⁰, in which RWA is present on polysomnography, however dream enactment behaviour is not sufficiently prominent as to impair the sleep quality of patients and their bed partners .

The heterogeneous phenotype of RBD suggests that RWA is likely to result from both structural lesions in the pons in addition to abnormalities of the cholinergic and aminergic chemicals that control REM atonia. Given these unanswered questions, it is imperative to improving the understanding of RBD that accurate objective techniques are developed to measure RWA as a continuous variable. In addition to gauging the effect of treatment, continuously variable RWA will allow accurate diagnosis of RBD necessary in the prediction of consequent neurodegenerative disorders.

Quantifying REM without atonia

The first method proposed to quantify RWA as a continuous variable was developed by Lapierre and Montplaisier in 1992 and was validated in a cohort of idiopathic RBD patients^{12, 21}. This method evaluated tonic or baseline RWA based on abnormally high EMG signal (defined as signal greater than 2 times the baseline or greater than an absolute voltage of 10 microvolts) being present for more than 50% of each epoch of REM sleep. The EMG tonic density was calculated as the percentage of epochs of REM demonstrating tonic RWA. If the tonic EMG density was greater than 30% this was deemed suggestive of a diagnosis of RBD and resulted in a sensitivity and specificity of 73.8% and 90.0% respectively when compared to the ICSD-2 diagnostic guidelines²². Furthermore, a second measurement of phasic EMG density was derived based on the percentage of 2 second mini epochs containing a phasic element of REM such as rapid eye movements that concomitantly reported abnormally high EMG activity (defined as greater than 4 times the baseline signal). Phasic EMG density greater than 15% was deemed suggestive of RBD and comparing this diagnostic tool with the current guideline reported a sensitivity and specificity of 88.9% and 82.5% respectively.

The method proposed by Lapierre and Montplaisier provides an accurate tool to measure RWA as a continuous variable and diagnose RBD. However, the visual scoring system is labour intensive and still has a subjective element conferring possible bias. To improve this, an automated computer based algorithm was developed by Ferri et al 2008¹¹. This algorithm generates a REM atonia index that grades RWA and has been validated in a mixed cohort of RBD¹¹ and was recently validated in PD²³. The REM atonia index averages the EMG signal in each 1 second epoch of REM sleep and grades the epoch as normal (figure 1 panel A)  or abnormal (figure 1 panel B)   based on a voltage threshold (< 1 µV = normal,  1-2 µV = indeterminate,  > 2 µV = abnormal). The REM atonia index is the ratio of normal to abnormal epochs of REM. This index was found to correlate closely with the visual scoring system developed by Montplaiser et al (2010). The REM atonia index has been further improved with a noise reduction algorithm²⁴ and represents an objective computational measurement of continuously variable RWA and  thus a purely objective diagnosis of RBD. As such the REM atonia index represents an instrument that could be applied to at risk populations such as idiopathic RBD or those with mild cognitive impairment, to determine who will transition to PD or another synucleinopathy. This method does not divide the EMG into tonic and phasic elements and given the high accuracy reported and the close correlation with the previously described visual scoring system, questions the need to make this division.

Both of the methods described so far rely on surface EMG data from the mentalis muscle collected during polysomnography. However, it is possible that by restricting the assessment of RWA to the mentalis muscle, some patients with RWA in other muscles, specifically in the upper and lower limbs may not be detected by this approach. To counter this problem Frauscher et al 2012 evaluated tonic and phasic RWA, using criteria very similar to those proposed by Montplaisier et al 2010, in multiple muscles in the upper and lower limbs and over the sternocleidomastoid muscle¹⁹. This study concludes that the optimal assessment of RWA should include the measurement of tonic or phasic activity within the mentalis muscle in addition to phasic activity within the left and right flexor digitorum brevis muscle¹⁹.

Technical difficulties in acquiring and measuring REM without atonia

Measurement techniques developed to quantify RWA have raised multiple technical questions regarding the optimal acquisition of surface EMG data, used to assess RWA. One problem raised by these techniques is the surface EMG signal is a low voltage signal susceptible to interference from snoring, breathing and other electrical noise. Furthermore, there is no agreement as to whether this signal should be assessed relative to the patient’s own baseline EMG signal or whether arbitrary voltage thresholds should be applied to all participants. Studies have also deemed a variety of voltage thresholds, below which the signal is consistent with atonia. The visual scoring method proposed by Montplaiser et al 2010 determined baseline surface EMG as between 3-7 microvolts, however the method used to derive this value was not described. Conversely the automated REM atonia index described by Ferri et al 2008 reported an average EMG signal during REM less than 1 microvolt to be the threshold of normal atonia. Similarly a variety of epoch lengths have been described to determine RWA ranging from 1 to 30 seconds and there is conflicting evidence as to whether to divide the surface EMG into tonic and phasic components or to assess the signal as one. Finally, there is a lack of agreement regarding which muscle to measure the signal. All of methods described rely on the accurate scoring of REM, which is difficult in patients within PD cohorts that experience frequent arousals with high rates of obstructive sleep apnea. In order to utilise the potential of RWA both in the diagnosis of RBD and in the prediction of neurodegenerative disease, studies are needed to answer these technical questions.

Conclusion

The accurate screening and diagnosis of RBD is essential for reducing comorbidity in PD. Furthermore, recent evidence concludes that REM sleep behaviour disorder is a biomarker heralding the development of an alpha synucleinopathy and provides up to 15 year window in which it might be possible to intervene and prevent or at least minimise the consequences of these syndromes. The emergence of techniques to measure RWA as a continuous variable have raised several technical questions regarding the data collection and the method in which it is analysed. Developing a unified approach to the objective quantification RWA, the electrophysiological hallmark of RBD, is critical to both the diagnosis of RBD as well as the future prediction of the neurodegenerative disorders preceded by RBD. It is hoped that the more accurate determination and quantification of RWA in RBD may improve the management of patients with PD in the future.

References

1. Gagnon JF, Bedard MA, Fantini ML, Petit D, Panisset M, Rompre S, et al. REM sleep behavior disorder and REM sleep without atonia in Parkinson’s disease. Neurology. 2002;59(4):585-9.
2. Postuma RB, Bertrand J-A, Montplaisir J, Desjardins C, Vendette M, Rios Romenets S, et al. Rapid eye movement sleep behavior disorder and risk of dementia in Parkinson’s disease: A prospective study. Movement Disorders. 2012;27(6):720-6.
3. Shine JM, Halliday GH, Carlos M, Naismith SL, Lewis SJG. Investigating visual misperceptions in Parkinson’s disease: a novel behavioral paradigm. Movement Disorders. 2012;27(4):500-5.
4. Naismith SL, Terpening Z, Shine JM, Lewis SJG. Neuropsychological functioning in Parkinson’s disease: Differential relationships with self-reported sleep-wake disturbances. Movement Disorders. 2011;26(8):1537-41.
5. Aarsland D, Larsen JP, Tandberg E, Laake K. Predictors of nursing home placement in Parkinson’s disease: a population-based, prospective study. Journal of the American Geriatrics Society. 2000;48(8):938-42.
6. Schenck CH, Bundlie SR, Mahowald MW. Delayed emergence of a parkinsonian disorder in 38% of 29 older men initially diagnosed with idiopathic rapid eye movement sleep behaviour disorder. Neurology. 1996;46(2):388-93.
7. Iranzo A, Tolosa E, Gelpi E, Molinuevo JL, Valldeoriola F, Serradell M, et al. Neurodegenerative disease status and post-mortem pathology in idiopathic rapid-eye-movement sleep behaviour disorder: an observational cohort study. Lancet Neurology. 2013;12(5):443-53.
8. Mahowald MW, Schenck CH. REM sleep behaviour disorder: a marker of synucleinopathy. Lancet Neurology. 2013;12(5):417-9.
9. Boeve BF, Silber MH, Ferman TJ, Lin SC, Benarroch EE, Schmeichel AM, et al. Clinicopathologic correlations in 172 cases of rapid eye movement sleep behavior disorder with or without a coexisting neurologic disorder. Sleep Medicine(0).
10. Schenck CH, Montplaisir JY, Frauscher B, Hogl B, Gagnon JF, Postuma R, et al. Rapid eye movement sleep behavior disorder: devising controlled active treatment studies for symptomatic and neuroprotective therapy-a consensus statement from the International Rapid Eye Movement Sleep Behavior Disorder Study Group. Sleep Medicine. 2013;14(8):795-806.
11. Ferri R, Manconi M, Plazzi G, Bruni O, Vandi S, Montagna P, et al. A quantitative statistical analysis of the submentalis muscle EMG amplitude during sleep in normal controls and patients with REM sleep behavior disorder. Journal of Sleep Research. 2008;17(1):89-100.
12. Montplaisir J, Gagnon J-F, Fantini ML, Postuma RB, Dauvilliers Y, Desautels A, et al. Polysomnographic diagnosis of idiopathic REM sleep behavior disorder. Movement Disorders. 2010;25(13):2044-51.
13. Boeve BF, Silber MH, Saper CB, Ferman TJ, Dickson DW, Parisi JE, et al. Pathophysiology of REM sleep behaviour disorder and relevance to neurodegenerative disease. Brain. 2007;130(Pt 11):2770-88.
14. Datta S. Cellular and chemical neuroscience of mammalian sleep. Sleep Medicine. 2010;11(5):431-40.
15. Postuma RB, Gagnon JF, Montplaisir J. Clinical prediction of Parkinson’s disease: planning for the age of neuroprotection. Journal of Neurology, Neurosurgery & Psychiatry. 2010;81(9):1008-13.
16. Boeve BF. Idiopathic REM sleep behaviour disorder in the development of Parkinson’s disease. Lancet Neurology. 2013;12(5):469-82.
17. Neikrug AB, Ancoli-Israel S. Diagnostic tools for REM sleep behavior disorder. Sleep Medicine Reviews. 2012;16(5):415-29.
18. Ferri R, Marelli S, Cosentino FI, Rundo F, Ferini-Strambi L, Zucconi M. Night-to-night variability of automatic quantitative parameters of the chin EMG amplitude (Atonia Index) in REM sleep behavior disorder. Journal of Clinical Sleep Medicine. 2013;9(3):253-8.
19. Frauscher B, Iranzo A, Hogl B, Casanova-Molla J, Salamero M, Gschliesser V, et al. Quantification of electromyographic activity during REM sleep in multiple muscles in REM sleep behavior disorder. Sleep. 2008;31(5):724-31.
20. Schenck CH, Mahowald MW. REM sleep behavior disorder: clinical, developmental, and neuroscience perspectives 16 years after its formal identification in SLEEP. Sleep. 2002;25(2):120-38.
21. Lapierre O, Montplaisir J. Polysomnographic features of REM sleep behavior disorder: development of a scoring method. Neurology. 1992;42(7):1371-4.
22. International classification of sleep disorders 2nd ed.: Diagnostic and coding manual. American Academy of Sleep Medicine. Westchester, IL: American Academy of Sleep Medicine; 2005.
23. Ferri R, Fulda S, Cosentino FII, Pizza F, Plazzi G. A preliminary quantitative analysis of REM sleep chin EMG in Parkinson’s disease with or without REM sleep behavior disorder. Sleep Medicine. 2012;13(6):707-13.
24. Ferri R, Rundo F, Manconi M, Plazzi G, Bruni O, Oldani A, et al. Improved computation of the atonia index in normal controls and patients with REM sleep behavior disorder. Sleep Medicine. 2010;11(9):947-9.