Polysomnographic predictors of sleep, motor and cognitive dysfunction progression in Parkinson's disease: a longitudinal study

Bugalho, Paulo; Ladeira, Filipa; Barbosa, Raquel; Marto, João Pedro; Borbinha, Cláudia; Conceição, Laurete da; Salavisa, Manuel; Saraiva, Marlene; Meira, Bruna; Fernandes, Marco

doi:10.1016/j.sleep.2020.06.020

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…abnormalities such as reduced spindles or SWS amount (17,38,39). Importantly, compared to SWS amount, SWA may be more sensitive to neurodegenerative cognitive impairment, as previously discussed (8).…”

Section: Discussionmentioning

confidence: 87%

Reduced Regional NREM Sleep Slow-Wave Activity Is Associated With Cognitive Impairment in Parkinson Disease

et al. 2021

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Growing evidence implicates a distinct role of disturbed slow-wave sleep in neurodegenerative diseases. Reduced non-rapid eye movement (NREM) sleep slow-wave activity (SWA), a marker of slow-wave sleep intensity, has been linked with age-related cognitive impairment and Alzheimer disease pathology. However, it remains debated if SWA is associated with cognition in Parkinson disease (PD). Here, we investigated the relationship of regional SWA with cognitive performance in PD. In the present study, 140 non-demented PD patients underwent polysomnography and were administered the Montréal Cognitive Assessment (MoCA) to screen for cognitive impairment. We performed spectral analysis of frontal, central, and occipital sleep electroencephalography (EEG) derivations to measure SWA, and spectral power in other frequency bands, which we compared to cognition using linear mixed models. We found that worse MoCA performance was associated with reduced 1–4 Hz SWA in a region-dependent manner (F2, 687 =11.67, p < 0.001). This effect was driven by reduced regional SWA in the lower delta frequencies, with a strong association of worse MoCA performance with reduced 1–2 Hz SWA (F2, 687 =18.0, p < 0.001). The association of MoCA with 1–2 Hz SWA (and 1–4 Hz SWA) followed an antero-posterior gradient, with strongest, weaker, and absent associations over frontal (rho = 0.33, p < 0.001), central (rho = 0.28, p < 0.001), and occipital derivations, respectively. Our study shows that cognitive impairment in PD is associated with reduced NREM sleep SWA, predominantly in lower delta frequencies (1–2 Hz) and over frontal regions. This finding suggests a potential role of reduced frontal slow-wave sleep intensity in cognitive impairment in PD.

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Section: Discussionmentioning

confidence: 87%

Reduced Regional NREM Sleep Slow-Wave Activity Is Associated With Cognitive Impairment in Parkinson Disease

et al. 2021

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“…Studies investigating the relationship between sleep disturbances and neuropsychological functions, conducted mainly in early to moderate PD patients, support the notion that some sleep alterations like RBD and EDS are connected to the development of cognitive dysfunctions and dementia in PD patients [5][6][7][8][9][10]12].…”

Section: Introductionmentioning

confidence: 91%

“…Sleep problems are known to contribute to neuropsychological deficits in otherwise healthy people [1]. The association between sleep problems and cognitive disorders has been explored in neurodegenerative disease, such as Alzheimer's disease (AD) [2][3][4] and PD [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Association between sleep disorders and cognitive dysfunctions in non-demented patients with advanced Parkinson’s disease

et al. 2021

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Background Parkinson’s disease (PD) is increasingly recognized as a multidimensional disorder, characterized by several non-motor symptoms, including disturbances of sleep and cognition. Current studies on the relationship between sleep problems and neuropsychological functions, mainly conducted in early to moderate PD patients, outline mixed results. In this study, we analysed the relationship between subjectively reported sleep alterations and cognitive functions in a large cohort of 181 advanced PD patients. Methods All consecutive, non-demented, advanced PD patients candidates for device-aided therapy completed two self-administered sleep questionnaires—the Parkinson’s Disease Sleep Scale (PDSS-2) and the Epworth Sleepiness Scale (ESS)—and underwent a comprehensive battery of neuropsychological tests encompassing five cognitive domains (reasoning, memory, attention, frontal executive functions, and language). Results Patients showed mild to moderate sleep problems (PDSS-2 score: 23.4 ± 1.2) and mild daytime sleepiness (ESS 8.6 ± 5.1). A significant correlation was found between PDSS-2 total score and non-verbal reasoning, as well as attentive skills, executive functions, and language abilities. No correlations were found between sleep measures and memory tests scores. Patients with clinically relevant sleep disturbances performed worse on attention, executive functions, and language. No significant correlations were found between daytime sleepiness and any neuropsychological test. Conclusions In advanced PD patients, sleep disturbances selectively correlate with specific neuropsychological functions and not with short-term memory and consolidation. Even if confirmations by means of longitudinal studies are needed, our observations suggest the importance of considering treatment of sleep disturbances to minimize their potential impact on cognition.

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“…Sleep quality in PD patients is assessed using several sleep questionnaires such as the Pittsburgh Sleep Quality Index (PSQI), Parkinson’s Disease Sleep Scale (PDSS), REM Behavior Disorder Sleep Questionnaire, and the Epworth Sleep Scale (ESS) [ 24 , 25 ]. However, polysomnography (PSG) is considered the gold standard for objective sleep assessment [ 26 , 27 ]. PSG can collect information using EEG, EOG, EMG, electrocardiography (ECG), and video.…”

Section: Introductionmentioning

confidence: 99%

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease

Kuo

Wang

et al. 2022

Biosensors

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Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson’s disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient’s sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole–Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

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Polysomnographic predictors of sleep, motor and cognitive dysfunction progression in Parkinson's disease: a longitudinal study

Cited by 21 publications

References 19 publications

Reduced Regional NREM Sleep Slow-Wave Activity Is Associated With Cognitive Impairment in Parkinson Disease

Reduced Regional NREM Sleep Slow-Wave Activity Is Associated With Cognitive Impairment in Parkinson Disease

Association between sleep disorders and cognitive dysfunctions in non-demented patients with advanced Parkinson’s disease

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease

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