REM sleep behavior disorder in a patient with pontine stroke.

Sleep Med

Department of Geriatric Neurology, Chinese PLA General Hospital, Fuxinglu 28, Beijing 100853, People's Republic of China.

Published: January 2009

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Objective/background: Comorbid insomnia with obstructive sleep apnea (COMISA) is associated with worse daytime function and more medical/psychiatric comorbidities vs either condition alone. COMISA may negatively impact sleep duration and reduce rapid eye movement (REM) sleep, thereby impairing cognition. These post-hoc analyses evaluated the effect of lemborexant (LEM), a dual-orexin-receptor antagonist approved for adults with insomnia, on sleep architecture in participants with COMISA.

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Background: Isolated rapid-eye movement (REM) sleep behavior disorder (iRBD) is characterized by abnormal behaviors in REM sleep and is considered as a prodromal symptom of alpha-synucleinopathies. Resting-state functional magnetic resonance imaging (rsfMRI) studies have unveiled altered functional connectivity (rsFC) in patients with iRBD. However, the associations between intra- and inter-network rsFC with clinical symptoms and neuropsychological functioning in iRBD remain unclear.

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Non-motor symptoms (NMS) in Parkinson's disease (PD) significantly impact quality of life, especially in later stages. REM sleep behavior disorder (RBD) affects approximately 42% of all PD patients and frequently precedes motor PD symptoms. RBD is linked to increased rates of depression and cognitive decline.

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Background:  Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is characterized by loss of the normal atonia of REM sleep accompanied by repetitive motor and behavior phenomena of dream content.

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"Multimodal Sleep Signal Tensor Decomposition and Hidden Markov Modeling for Temazepam-Induced Anomalies Across Age Groups".

J Neurosci Methods

January 2025

School of Electrical and Computer Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA.

Background: Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.

New Method: We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.

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