Slow‐wave sleep and motor progression in Parkinson disease

Schreiner, Simon J.; Imbach, Lukas L.; Werth, Esther; Poryazova, Rositsa; Baumann‐Vogel, Heide; Valko, Philipp O.; Murer, Tobias; Noaín, Daniela; Baumann, Christian

doi:10.1002/ana.25459

Cited by 64 publications

(47 citation statements)

References 18 publications

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“…However, a bidirectional relationship between sleep disturbances and disease progression is also suggested by studies showing that the overall amount of SWS is inversely related to motor progression in PD (Fig. 1B) and that poor sleep quality can be predictive of more rapid gait deterioration 125,126 . Further work is needed to establish a causal relationship between sleep disturbances in PD and disease progression, but these early studies are notable.…”

Section: Impact Of Sleep Disturbances On Parkinson's Disease Pathophysiologymentioning

confidence: 91%

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The Neurophysiology of Sleep in Parkinson's Disease

et al. 2021

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Sleep disturbances are among the most common nonmotor complications of Parkinson's disease (PD), can present in prodromal stages, and progress with advancing disease. In addition to being a symptom of neurodegeneration, sleep disturbances may also contribute to disease progression. Currently, limited options exist to modulate sleep disturbances in PD. Studying the neurophysiological changes that affect sleep in PD at the cortical and subcortical level may yield new insights into mechanisms for reversal of sleep disruption. In this article, we review cortical and subcortical recording studies of sleep in PD with a particular focus on dissecting reported electrophysiological changes. These studies show that slow-wave sleep and rapid eye movement sleep are both notably disrupted in PD. We further explore the impact of these electrophysiological changes and discuss the potential for targeting sleep via stimulation therapy to modify PD-related motor and nonmotor symptoms.

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Section: Impact Of Sleep Disturbances On Parkinson's Disease Pathophysiologymentioning

confidence: 91%

“…1B) and that poor sleep quality can be predictive of more rapid gait deterioration. 125,126 Further work is needed to establish a causal relationship between sleep disturbances in PD and disease progression, but these early studies are notable.…”

Section: Impact Of Sleep Disturbances On Parkinson's Disease Pathophysiologymentioning

confidence: 99%

The Neurophysiology of Sleep in Parkinson's Disease

et al. 2021

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“…In PD, progressive deterioration of SWS has been described, with altered slow wave morphology at prodromal stages (12), reduced SWA in de novo patients (13), and reduced SWS amount in advanced stages (14). Moreover, impaired SWA has been linked with motor complications and progression (15,16).…”

Section: Introductionmentioning

confidence: 99%

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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“…By comparing the median of a single column of numeric values against a hypothetical median, this hypothesis test evaluates the statistical differences between two populations (Wilcoxon 1992). In this paper, six classes of problems have been solved using WSRT (Schreiner et al 2019;Chung et al 2017;Raghavendra et al 2018). To handle this multi-class problem, we utilized a oneagainst-one approach, which breaks × number of classes into binary classes containing a set of all possible pairs of × classes.…”

Section: Resultsmentioning

confidence: 99%

A novel enhanced decision tree model for detecting chronic kidney disease

Chaudhuri¹,

Sinha

Banerjee³

et al. 2021

Netw Model Anal Health Inform Bioinforma

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Prediction of diseases is sensitive as any error can result in the wrong person's treatment or not treating the right patient. Besides, some features distinguish a disease from curable to fatal or curable to chronic disease. Data mining techniques have been widely used in health-related research. The researchers, so far, could attain around 97 percent accuracy using several methods. Some researchers have demonstrated that the selection of correct features increases the prediction accuracy. This research work propose a method to distinguish between chronic and non-chronic kidney disease, identify its crucial features without reducing the accuracy of prediction, and a prediction algorithm to eliminate the possibility of under or overfitting. This study uses the recursive feature elimination (RFE) method that selects an optimal subset of features and an ensemble algorithm, the enhanced decision tree (EDT), to predict the disease. The results obtained in this paper show that the accuracy level of EDT is not changed with the removal of less significant features, thus enabling the decision-makers to concentrate on few features to reduce time and error of treatment. EDT establishes substantially high consistency in predicting, with or without feature selection, the disease.

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Slow‐wave sleep and motor progression in Parkinson disease

Cited by 64 publications

References 18 publications

The Neurophysiology of Sleep in Parkinson's Disease

The Neurophysiology of Sleep in Parkinson's Disease

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

A novel enhanced decision tree model for detecting chronic kidney disease

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