Sleep alterations are related to cognitive symptoms in Parkinson's disease: A 24-hour ambulatory polygraphic EEG study

Papp, Andrea; Horváth, András; Virág, Márta; Tóth, Zsuzsanna; Borbély, Csaba; Gombos, Ferenc; Szücs, Anna; Kamondi, Anita

doi:10.1016/j.ijpsycho.2022.01.010

Cited by 7 publications

(7 citation statements)

References 69 publications

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“…In line with previous reports based on small samples of patients with Parkinson's [9] or Alzheimer's [31] disease, we found that age-corrected sleep alterations in age-related disorders is similar to changes related to normal aging. Both disorders were characterized by an acceleration of EEG rhythms, with a specific increase in REM latency in Parkinson's disease.…”

Section: Discussionsupporting

confidence: 93%

“…Direct, disease-specific pathophysiological processes are known to affect sleep regulation in certain medical conditions. For example, Parkinson's disease, epilepsy and other neuropsychiatric disorders are tightly linked with fundamental sleep regulatory mechanisms and are known to associate with specific alterations of EEG-based, objective sleep measures, sometimes even predicting disease-specific behavioral correlates [7][8][9]. Furthermore, some disorders affecting organs other than the brain (for example, the liver, the kidney or the heart) are known to cause changes in objectively rated sleep through both direct (physiological, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Sleep alterations as a function of 88 health indicators

Ujma,

Bódizs

2023

Preprint

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Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between changes in sleep macrostructure, quantitative sleep EEG and health. We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N=3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors and the use of 48 medications. Of sleep macrostructure variables, increased REM latency and reduced REM duration was the most common finding across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG PSD alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases are associated with cardiovascular problems and associated medications, in line with immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Sleep alterations as a function of 88 health indicators

Ujma,

Bódizs

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In line with previous reports based on small samples of patients with Parkinson's [9] or Alzheimer's [35] disease, we found that age-corrected sleep alterations in agerelated disorders are similar to changes related to normal aging. Both disorders were characterized by an acceleration of EEG rhythms, with a specific increase in REM latency in Parkinson's disease.…”

Section: Discussionsupporting

confidence: 93%

“…Direct, disease-specific pathophysiological processes are known to affect sleep regulation in certain medical conditions. For example, Parkinson's disease, epilepsy and other neuropsychiatric disorders are tightly linked with fundamental sleep regulatory mechanisms and are known to associate with specific alterations of electroencephalographically (EEG)-based, objective sleep measures, sometimes even predicting disease-specific behavioral correlates [7][8][9]. Furthermore, some disorders affecting organs other than the brain (for example, the liver, the kidney, or the heart) are known to cause changes in objectively rated sleep through both direct (physiological, e.g., altered melatonin synthesis) and indirect (symptom-mediated, e.g., pain affecting sleep) routes [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Sleep alterations as a function of 88 health indicators

Ujma,

Bódizs

2024

BMC Med

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Background Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health. Methods We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications. Results Of sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85–95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects. Conclusions Sleep alterations are present in various health conditions.

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“…The example in Figure 3 of the probability output of the model shows that probability of RBD is high in sections extended periods of wake and REM sleep. PD has previously been shown to cause alterations in the ratio of all sleep stages compared to healthy controls [22], [23] -thus one may speculate as to whether the models are picking up on features related to PD from the RBD with PD group. However, given the analysis shown in Table III and the ratio of iRBD to RBD with PD, it is unlikely that including these patients would have such a major effect on the relevancy scores.…”

Section: B Physiological Interpretationmentioning

confidence: 99%

SViT: A Spectral Vision Transformer for the Detection of REM Sleep Behavior Disorder

Gunter

Brink-Kjær

Mignot

et al. 2023

IEEE J. Biomed. Health Inform.

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REM sleep behavior disorder (RBD) is a parasomnia with dream enactment and presence of REM sleep without atonia (RSWA). RBD diagnosed manually via polysomnography (PSG) scoring, which is time intensive. Isolated RBD (iRBD) is also associated with a high probability of conversion to Parkinson's disease. Diagnosis of iRBD is largely based on clinical evaluation and subjective PSG ratings of REM sleep without atonia. Here we show the first application of a novel spectral vision transformer (SViT) to PSG signals for detection of RBD and compare the results to the more conventional convolutional neural network architecture. The vision-based deep learning models were applied to scalograms (30 or 300 second windows) of the PSG data (EEG, EMG and EOG) and the predictions interpreted. A total of 153 RBD (96 iRBD and 57 RBD with PD) and 190 controls were included in the study and 5-fold bagged ensemble was used. Model outputs were analyzed per-patient (averaged), with regards to sleep stage, and the SViT was interpreted using integrated gradients. Models had a similar per-epoch test F1 score. However, the vision transformer had the best per-patient performance, with an F1 score 0.87. Training the SViT on channel subsets, it achieved an F1 score of 0.93 on a combination of EEG and EOG. EMG is thought to have the highest diagnostic yield, but interpretation of our model showed that high relevance was placed on EEG and EOG, indicating these channels could be included for diagnosing RBD.

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Sleep alterations are related to cognitive symptoms in Parkinson's disease: A 24-hour ambulatory polygraphic EEG study

Cited by 7 publications

References 69 publications

Sleep alterations as a function of 88 health indicators

Sleep alterations as a function of 88 health indicators

Sleep alterations as a function of 88 health indicators

SViT: A Spectral Vision Transformer for the Detection of REM Sleep Behavior Disorder

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