Sleep Monitoring in Neurodegenerative Diseases

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Giulia Masi	--	2129	2023-03-14 14:24:59	\|
2	format correct	Conner Chen	Meta information modification	2129	2023-03-16 04:35:30	\|

This entry is adapted from the peer-reviewed paper 10.3390/electronics12051098

Good sleep quality is of primary importance in ensuring people’s health and well-being. In fact, sleep disorders have well-known adverse effects on quality of life, as they influence attention, memory, mood, and various physiological regulatory body functions. Sleep alterations are often strictly related to age and comorbidities. For example, in neurodegenerative diseases, symptoms may be aggravated by alterations in sleep cycles or, vice versa, may be the cause of sleep disruption. Therefore, neurodegenerative diseases have also typical sleep sympthoms. In the clinical practice, different investigation about sleep aspects can be performed, producing a complex picture of different sleep monitoring approaches.

neurodegenerative diseases sleep monitoring sleep disorders

1. Introduction

Sleep plays a fundamental role in the lives of many animals, from some invertebrates to humans. It has both physiological and behavioral connotations and, although its functions and evolutionary significance are not yet fully known, its fundamental role in the maintenance of homeostasis and the adverse effects due to its sub-optimality are well-known in humans. Indeed, it influences attention, memory, mood, blood pressure, immune and inflammatory response, and stress response ^[1]^[2]^[3]. Under physiological conditions, a sleep phase and a wakefulness phase alternate in a regular manner, constituting the sleep–wake circadian rhythm. The sleep phase is a dynamic process aimed at obtaining the required neurophysiological states at certain times, according to circadian and homeostatic needs and despite external or internal interfering stimuli. Moreover, the so-called macrostructure of sleep, as recorded by electroencephalography (EEG) during polysomnography (PSG), is characterized by a chain of regular and predictable events (cyclic alternation of rapid eye movements (REM) and non-REM (NREM) sleep stages). The process shows an intrinsic variability and has to finely modulate itself in order to maintain the maximum adaptability while preserving sleep macrostructure. In this context, peculiar transient EEG patterns (sleep microstructure) are supposed to play the main role in the building up of EEG synchronization and in the flexible adaptation against perturbations. Alterations in sleep macro- or microstructure provoke sleep disruption, sleep instability and loss of sleep quantity and quality ^[4]^[5]. Sleep and wakefulness influence each other; therefore, sleep quality degradation, when persisting over time, may translate into severe and irreversible symptoms, taking the form of a pathological framework. Therefore, it is very important to create the best possible sleeping conditions and to intervene promptly when sleep disturbances occur, both in their diagnosis and eventual treatments. Even though sleep time and quality lessen with age, sleep disorders are related to comorbidities rather than age ^[6]. In particular, sleep disorders have a high incidence in neurodegenerative diseases (ND) and are known to influence well-being and quality of life ^[7]. Indeed, the symptoms of the NDs may be worsened by the sleep disorders, but, at the same time, the latter may be caused or augmented by the neurodegenerative disease, creating a more complex clinical picture. Optimized, sometimes individualized, treatments are being developed in clinical practice ^[8]. The relationship between sleep abnormalities/disorders and NDs is so close that sleep disorders can be used as criteria for the diagnosis of specific NDs ^[9]. As an example, stridor co-occurs with multiple system atrophy (MSA), while a REM-sleep behavior disorder may discriminate between Alzheimer’s disease (AD) and dementia with Lewy body (DLB). The most interesting discovery in the field is that, in some cases, especially in Parkinson’s disease (PD), the onset of sleep disturbance could reflect early alterations in the neural pathways involved, thus constituting a prodromal symptom ^[10]. This allows earlier intervention in treatment and follow-up; moreover, it will be crucial when neuroprotective drugs become available ^[11]. The assessment of sleep macro- and microstructure, movements, respiratory pattern or other neurophysiological changes that occur during sleep is essential to verify the quality of sleep and detect sleep disorders. For clinical purposes, PSG is the gold standard for the assessment of sleep disorders, and guidelines are available for recommended uses. In PSG, selected electrophysiological signals are recorded along with other biological signals of interest, such as airflow, oxygen saturation, chest movements or snoring. The type and number of signals that are recorded depends on the reported symptoms and the aim of the PSG. EEG, electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG) are required for sleep staging, whereas in the detection of sleep apnea, for instance, the primary focus is on oxygen saturation, airflow, and thorax and abdominal movements ^[12]. Complete polysomnographic examinations are very complex and invasive; they need cumbersome instrumentation, a proper location, night-time assistance by experienced personnel, time, money and they bring discomfort for the patient as well. The medical inspection of the signals (many hours of recording) needs to be performed by qualified experts and it is, however, subjected to inter- operator variability ^[13]^[14]. For these reasons, PSG can only be performed in proper settings and usually for in-patients, mainly when precise diagnosis is essential for targeting therapy. Therefore, many alternatives have been proposed in the research to cope with this limitation, in particular for screening or monitoring purposes. They exploit, in general, new technologies and automatic algorithms to reduce the invasiveness of the instrumentation required and the intervention of specialized personnel. This would allow a much more frequent, if not continuous, assessment of the patients’ condition with reduced cost and discomfort, providing the conditions for optimized diagnosis and treatments. Research in this area has several objectives:

To update and simplify the work of medical staff by automating or semi-automating certain procedures—such as sleep staging or sleep disorders diagnosis—through new instrumentation.
To verify medical treatment efficacy and, eventually, to optimize it, through sleep monitoring.
To ensure frequent or continuous follow-up by providing instrumentation and protocols to be used in non-hospital settings.

2. Background of Sleep Monitoring in Neurodegenerative Diseases

In NDs, the progressive loss of neurons in particular structures of the central nervous system (CNS) causes dysfunctions of neural pathways, leading to the symptoms typical of each disease. In some cases, treatments are available for symptoms relief, but the neurodegeneration process is unstoppable and irreversible. AD and PD are among the most common neurodegenerative disorders worldwide, with a high incidence in the elderly population ^[15]. In fact, aging is one of the main risk factors in developing NDs, even though their etiology can vary, and are not completely understood. Moreover, genes and environment are believed to be together responsible of these diseases’ onset. Other less common NDs are Huntington disease, DLB, amyotrophic lateral sclerosis (ALS), Friedreich ataxia, and MSA. A brief description of the principal symptoms and characteristics is provided in Table 1, with a focus on the diseases’ effects on sleep. In fact, these pathologies have a complex relationship with the sphere of sleep. Sleep disruption and disorders can be commonly found in patients with ND and may constitute an early biomarker. Iranzo in ^[11] highlights the frequent occurrence of the subsequent sleep disorders in ND:

Table 1. Neurodegenerative diseases (ND) and sleep-related symptoms, and sleep disorders incidence (sleep disorders incidence (SD)) ^[11].

ND	Symptoms	Sleep Symptoms	SD
Parkinson’s disease ^[16]^[17]	Motor: tremors, postural issues, bradykinesia, ON/OFF states, dystonia, rigidity, dyskinesias. Non-motor: orthostatic hypotension, depression, gastrointestinal symptoms, speech and writing change	RBD (also prodromal), sleep-disordered breathing, EDS, Insomnia, RLS, periodic leg movements in sleep (PLMS)	60–90%
Multiple system atrophy ^[18]	Parkinsonism, breathing problems	RBD, fragmented sleep, insomnia, stridor, EDS	80–100%
Dementia with Lewy body ^[19]	Dementia, parkinsonism, fluctuations, and visual hallucinations	Insomnia, circadian rhythm disorder, RBD ¹ (also prodromal), confusional awakenings, EDS	80%
Alzheimer’s Disease ^[20]	Cognitive impairment, dementia. Altered behavior, confusion, aggressiveness	Frequent daytime napping, difficulty in falling asleep and early wakeups, sleep fragmentation, reduced deep and REM sleep amounts, OSA, circadian rhythm alterations, slowdown of sleep EEG rhythms.	45%
Huntington Disease ^[21] (genetic)	Dementia, psychiatric disturbances	Sleep quality loss, insomnia, sleep fragmentation, EDS, circadian rhythm sleep disorders, reduced NREM and REM sleep.	87%
Amyotrophic lateral sclerosis ^[22]	Weakness, muscle atrophy, spasticity, respiratory dysfunction	Sleep-disordered breathing, nocturnal hypoventilation, nocturia, cramps, insomnia, EDS	17–76%
Friedreich ataxia ^[23] (genetic)	Impaired gait, balance, coordination, and speech	RBD, RLS, OSA	50%

¹ OSA: obstructive sleep apnea; EEG: electroencephalography; RBD: REM behavior disorder; EDS: excessive daytime sleepiness; RLS: restless leg syndrome. PLMS: periodic limb movements during sleep.

Insomnia.
Excessive daytime sleepiness (EDS).
Rapid eye movement (REM) sleep behavior disorder (RBD).
Periodic leg movements in sleep (PLMS).
Restless legs syndrome (RLS).
Central or obstructive sleep apnea (CSA, OSA).
Sleep disordered Breathing (SDS).
Nocturnal stridor.
Circadian rhythm disorders.

Further, sleep-quality impairment, sleep-time reduction, and presence of abnormal movements (both excessive and impaired) are other typical features. Sleep symptoms derive from multifactorial causes, including the deterioration of sleep–wake regulatory circuitries caused by the neurodegeneration itself and altered neural pathways, movement or respiratory symptoms specific to each pathology or several indirect mechanisms ^[24]. Sleep has, in turn, an influence on the neurodegeneration process, realizing a complex bi-directional relationship that could lead to new targeted interventions ^[25]. For instance, sub-optimal sleep—e.g., lack of sleep, disturbed sleep, sleep disorders—was found correlated to cognitive-impairment severity in AD patients and in the elderly, thus constituting a possible risk factor for the onset of cognitive impairment ^[26]^[27]. Lately, the discoveries regarding this relationship have been translated in the clinical practice, renovating disease diagnostic criteria and treatments ^[28]. However, sleep-related symptoms are still under-reported by patients and under-diagnosed by healthcare professionals. This is a flaw in optimized diagnosis and intervention, because of the reduced descriptive power of a complete clinical framework that considers these aspects. The result is a reduced quality of life for patients, sub-optimal treatments, and, sometimes, late diagnosis or misdiagnosis. In clinical practice, these sleep disruptions and disorders, including abnormal movements, are assessed through different tools, such as individual interviews (anamnesis), sleep diaries, sleep questionnaires, clinical scales, reduced or complete PSG, sleep diaries, and clinical scales; moreover, clinical protocols establish assessing procedures ^[29]^[30]. Typical sleep symptoms and main clinical assessing protocols are described in Table 2. PSG is the most complete clinical examination, able to evaluate every aspect of sleep and derive quantitative measures, constituting the gold standard in assessment and diagnosis of sleep-related problems. Sleep staging, REM sleep without atonia, apneas, oxygen saturation, sleep microstructure including the cyclic alternating pattern (CAP), and sleep parameters computation can be investigated by PSG. Some of the typical sleep parameters employed, besides sleep-stages descriptors, are total sleep time (TST), sleep latency, sleep efficiency, wake after sleep onset (WASO), and REM latency ^[31]. Standardized semiquantitative evaluation of symptom severity and quality-of-life reduction is provided by clinical rating scales, such as those shown in Table 2. The latter are employed for various sleep disturbances and disorders, including restless legs syndrome (RLS), insomnia, nocturia, breathing disorders, and daytime sleepiness ^[32]. It must be considered that each subject’s clinical history deeply influences the sleep evaluation tools; in fact, perception of symptoms is subjective and can be influenced by the clinical framework. As an example, in dementia, cognitive impairment can make it difficult to obtain a subject’s collaboration in clinical interviews and physical exams ^[33]. In synucleinopathies—such as PD, DBL, and MSA—RBD assessment is particularly relevant because its idiopathic occurrence is known to be a prodromal symptom that can anticipate any other symptom by decades ^[34]. In contrast, RBD developing after the onset of other symptoms may indicate a particular disease phenotype. For this reason, RBD screening and diagnosis have attracted much clinical attention in the last years.

Table 2. Clinical assessing methods in sleep investigation.

Sleep Investigation	Clinical Assessing Methods
Sleep quality ^[35]	Anamnesis, diaries such as Consensus Sleep Diary (CSD), clinical scales such as Pittsburgh Sleep Quality Index (PSQI) for sleep disturbances, sleep duration, sleep latency, sleep efficiency, use of sleep medication, daytime dysfunction, and sleep-quality subjective evaluation in the past months.
SD: restless leg syndrome (RLS) ^[36]	Anamnesis, PSG for detecting associated PLMS, International Restless Legs Scale (IRLS).
SD: REM behavior disorder (RBD) ^[37]	Anamnesis; PSG ¹ with sleep staging and REM sleep without atonia scorings; Video-PSG; screening questionnaires; rating scales: RBD Screening Questionnaire (RBDSQ), RBD Single-Question Screen (RBD1Q).
Sleep-related problems severity in PD	Rating scales: Parkinson’s disease sleep scale (PDSS), ESS, SCOPA-SLEEP; PSG.
Nocturnal movements in PD ^[32]	Anamnesis; PSG; Video-PSG, Actigraphy; rating scales: PDSS for leg or arm restlessness when resting, urgency to move when resting, getting out of bad for urination, nocturnal hypokinesia, painful posturing of the arms or legs, fidgeting. MDS-UPDRS for turning in bad, getting out of bad. NMSS for urgency to move when resting. NMSQ for getting out of bed for urination, acting out dreams, urgency to move when resting. PSQI, RLS and RBD scales.
Sleep disturbances in AD ^[33]	Anamnesis (manifestations of the sleep disorders can be atypical, cognitive impairment can make it difficult); RLS and breathing-disorders assessment; PSG; Actigraphy.
EDS ^[38]	Anamnesis, PSG, Multiple sleep latency test (MSLT), Maintenance of wakefulness test (MWT), Epworth sleepiness scale (ESS)

¹ PSG: Polysomnography; SD: Sleep Disorder; SCOPA-Sleep: Scales for Outcomes in Parkinson’s Disease-Sleep Disturbances; MDS-UPDRS: Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale; NMSQ: Non-Motor Symptoms Questionnaire; NMSS: Non-Motor Symptoms Scale; REM: Rapid Eye Movements; RBD: REM Behaviour Disorder; PLMS: periodic limb movements during sleep.

References

Killgore, W.D.S. Effects of sleep deprivation on cognition. Prog. Brain Res. 2010, 185, 105–129.
Spiegel, K. Effect of sleep deprivation on response to immunizaton. JAMA 2002, 288, 1471–1472.
Knutson, K.L.; Spiegel, K.; Penev, P.; Van Cauter, E. The metabolic consequences of sleep deprivation. Sleep Med. Rev. 2007, 11, 163–178.
Halász, P. Hierarchy of micro-arousals and the microstructure of sleep. Neurophysiol. Clin. Neurophysiol. 1998, 28, 461–475.
Parrino, L.; Ferrillo, F.; Smerieri, A.; Spaggiari, M.C.; Palomba, V.; Rossi, M.; Terzano, M.G. Is insomnia a neurophysiological disorder? The role of sleep EEG microstructure. Brain Res. Bull. 2004, 63, 377–383.
Foley, D.; Ancoli-Israel, S.; Britz, P.; Walsh, J. Sleep disturbances and chronic disease in older adults: Results of the 2003 National Sleep Foundation Sleep in America Survey. J. Psychosom. Res. 2004, 56, 497–502.
Iranzo, A.; Santamaria, J. Sleep in Neurodegenerative Diseases. In Sleep Medicine; Springer: New York, NY, USA, 2015; pp. 271–283.
Iranzo, A. Sleep in dementia and other neurodegenerative diseases. In Sleep Disorders in Neurology; John Wiley & Sons Ltd.: Chichester, UK, 2018; pp. 229–240.
Abbott, S.M.; Videnovic, A. Chronic sleep disturbance and neural injury: Links to neurodegenerative disease. Nat. Sci. Sleep 2016, 8, 55–61.
Priano, L.; Bigoni, M.; Albani, G.; Sellitti, L.; Giacomotti, E.; Picconi, R.; Cremascoli, R.; Zibetti, M.; Lopiano, L.; Mauro, A. Sleep microstructure in Parkinson’s disease: Cycling alternating pattern (CAP) as a sensitive marker of early NREM sleep instability. Sleep Med. 2019, 61, 57–62.
Iranzo, A. Sleep in neurodegenerative diseases. Sleep Med. Clin. 2016, 11, 1–18.
Mendonca, F.; Mostafa, S.S.; Ravelo-Garcia, A.G.; Morgado-Dias, F.; Penzel, T. A review of obstructive sleep apnea detection approaches. IEEE J. Biomed. Health Inform. 2019, 23, 825–837.
Younes, M.; Raneri, J.; Hanly, P. Staging sleep in polysomnograms: Analysis of inter-scorer variability. J. Clin. Sleep Med. 2016, 12, 885–894.
Miladinović, Đ.; Muheim, C.; Bauer, S.; Spinnler, A.; Noain, D.; Bandarabadi, M.; Gallusser, B.; Krummenacher, G.; Baumann, C.; Adamantidis, A.; et al. SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. PLoS Comput. Biol. 2019, 15, e1006968.
Dorsey, E.R.; Sherer, T.; Okun, M.S.; Bloem, B.R. The emerging evidence of the Parkinson pandemic. J. Parkinsons. Dis. 2018, 8, S3–S8.
Magrinelli, F.; Picelli, A.; Tocco, P.; Federico, A.; Roncari, L.; Smania, N.; Zanette, G.; Tamburin, S. Pathophysiology of motor dysfunction in Parkinson’s disease as the rationale for drug treatment and rehabilitation. Parkinsons Dis. 2016, 2016, 9832839.
Lajoie, A.C.; Lafontaine, A.L.; Kaminska, M. The spectrum of sleep disorders in Parkinson disease: A review. Chest 2021, 159, 818–827.
Lin, J.Y.; Zhang, L.Y.; Cao, B.; Wei, Q.Q.; Ou, R.W.; Hou, Y.B.; Liu, K.C.; Xu, X.R.; Jiang, Z.; Gu, X.J.; et al. Sleep-related symptoms in multiple system atrophy: Determinants and impact on disease severity. Chin. Med. J. 2020, 134, 690–698.
Ferman, T.J.; Smith, G.E.; Dickson, D.W.; Graff-Radford, N.R.; Lin, S.C.; Wszolek, Z.; Van Gerpen, J.A.; Uitti, R.; Knopman, D.S.; Petersen, R.C.; et al. Abnormal daytime sleepiness in dementia with Lewy bodies compared to Alzheimer’s disease using the Multiple Sleep Latency Test. Alzheimer Res. Ther. 2014, 6, 76.
Duncan, M.J.; Veasey, S.C.; Zee, P. Editorial: Roles of sleep disruption and circadian rhythm alterations on neurodegeneration and Alzheimer’s disease. Front. Neurosci. 2021, 15, 737895.
Herzog-Krzywoszanska, R.; Krzywoszanski, L. Sleep disorders in Huntington’s disease. Front. Psychiatry 2019, 10, 221.
Boentert, M. Sleep disturbances in patients with amyotrophic lateral sclerosis: Current perspectives. Nat. Sci. Sleep 2019, 11, 97–111.
Corben, L.A.; Ho, M.; Copland, J.; Tai, G.; Delatycki, M.B. Increased prevalence of sleep-disordered breathing in Friedreich ataxia. Neurology 2013, 81, 46–51.
Raggi, A.; Ferri, R. Sleep disorders in neurodegenerative diseases: Sleep in neurodegenerative diseases. Eur. J. Neurol. 2010, 17, 1326–1338.
Fifel, K.; Videnovic, A. Circadian and sleep dysfunctions in neurodegenerative disorders-an update. Front. Neurosci. 2020, 14, 627330.
Zhang, Y.; Ren, R.; Yang, L.; Zhang, H.; Shi, Y.; Okhravi, H.R.; Vitiello, M.V.; Sanford, L.D.; Tang, X. Sleep in Alzheimer’s disease: A systematic review and meta-analysis of polysomnographic findings. Transl. Psychiatry 2022, 12, 136.
Lyons, B.E.; Austin, D.; Seelye, A.; Petersen, J.; Yeargers, J.; Riley, T.; Sharma, N.; Mattek, N.; Dodge, H.; Wild, K.; et al. Corrigendum: Pervasive computing technologies to continuously assess Alzheimer’s disease progression and intervention efficacy. Front. Aging Neurosci. 2015, 7, 232.
Voysey, Z.J.; Barker, R.A.; Lazar, A.S. The treatment of sleep dysfunction in neurodegenerative disorders. Neurotherapeutics 2021, 18, 202–216.
American Academy of Sleep Medicine. International Classification of Sleep Disorders—Third Edition (ICSD-3); American Academy of Sleep Medicine (AASM): Darien, IL, USA, 2014.
Wood, E.A.; McCall, W.V. Assessment methodologies in sleep medicine clinical trials. Clin. Investig. 2013, 3, 791–800.
Shrivastava, D.; Jung, S.; Saadat, M.; Sirohi, R.; Crewson, K. How to interpret the results of a sleep study. J. Community Hosp. Intern. Med. Perspect. 2014, 4, 24983.
Zampogna, A.; Manoni, A.; Asci, F.; Liguori, C.; Irrera, F.; Suppa, A. Shedding light on nocturnal movements in Parkinson’s disease: Evidence from wearable technologies. Sensors 2020, 20, 5171.
Urrestarazu, E.; Iriarte, J. Clinical management of sleep disturbances in Alzheimer’s disease: Current and emerging strategies. Nat. Sci. Sleep 2016, 8, 21–33.
Claassen, D.; Josephs, K.; Ahlskog, J.; Silber, M.; Tippmann-Peikert, M.; Boeve, B. Rem sleep behavior disorder preceding other aspects of synucleinopathies by up to half a century. Neurology 2011, 77, 1155.
Mollayeva, T.; Thurairajah, P.; Burton, K.; Mollayeva, S.; Shapiro, C.M.; Colantonio, A. The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis. Sleep Med. Rev. 2016, 25, 52–73.
Kohnen, R.; Allen, R.P.; Benes, H.; Garcia-Borreguero, D.; Hening, W.A.; Stiasny-Kolster, K.; Zucconi, M. Assessment of restless legs syndrome—Methodological approaches for use in practice and clinical trials. Mov. Disord. 2007, 22, S485–S494.
Skorvanek, M.; Feketeova, E.; Kurtis, M.M.; Rusz, J.; Sonka, K. Accuracy of rating scales and clinical measures for screening of rapid eye movement sleep behavior disorder and for predicting conversion to Parkinson’s disease and other synucleinopathies. Front. Neurol. 2018, 9, 376.
McWhirter, D.; Bae, C.; Budur, K. The assessment, diagnosis, and treatment of excessive sleepiness: Practical considerations for the psychiatrist. Psychiatry 2007, 4, 26–35.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Engineering, Biomedical

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

View Times: 315

Update Date: 16 Mar 2023

Table of Contents

Video Upload Options

Confirm

1. Introduction

2. Background of Sleep Monitoring in Neurodegenerative Diseases

References