Purpose: To characterize precisely the sleep pattern in children with co-existence of TD + ADHD.
Methods: By means of polysomnography, sleep pattern was investigated in 19 children with TD + ADHD unmedicated before and during study and 19 healthy controls, matched for age, gender, and intelligence.
Results: Compared with healthy controls, children with TD + ADHD displayed shorter REM sleep latency and increased REM sleep duration. There was a negative correlational relationship between these REM-sleep alterations and they were determined by hyperactivity symptoms.
Conclusions: Sleep in children with coexistence of TD + ADHD may be characterized by an elevated REM sleep drive. Common mechanisms are suggested to underpin hypermotor symptoms and REM sleep regulation.
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http://dx.doi.org/10.1007/s00787-007-1006-4 | DOI Listing |
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
View Article and Find Full Text PDFRes Dev Disabil
January 2025
School of Psychological Science, Oregon State University, 2950 SW Jefferson Way, Corvallis, OR 97331, USA. Electronic address:
Introduction: Moebius syndrome is a rare congenital disorder with frequent anecdotal reports of sleep disturbances not sufficiently categorized by prior literature. The present mixed-methods, two-phase study aimed to characterize the sleep health and symptoms of a cohort of adults and children (via parent proxies) with Moebius syndrome.
Methods: In Phase 1, participants were 46 adults with Moebius Syndrome (M=33.
Parkinsonism Relat Disord
January 2025
Department of Nuclear Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea. Electronic address:
Introduction: In isolated REM sleep behavior disorder (iRBD), the evidence of cognitive impairment and co-existing amyloid pathology suggests that mild behavioral impairment (MBI) may be associated with disease progression. In this study, we investigated MBI and its association with cognitive function, brain amyloid load and glucose metabolism in iRBD patients to evaluate the utility of MBI as a predictive marker of disease progression.
Methods: Patients with iRBD underwent a neuropsychological evaluation, F-florbetaben (FBB) PET, and F-fluorodeoxyglucose (FDG) PET.
Nutr Res
December 2024
Faculty of Medicine Health and Life Science, Swansea University, Swansea, Wales, UK. Electronic address:
Limited research has examined the effect of meal composition on sleep. Based on previous research, we hypothesized that a low glycemic index (LGI) drink containing 50 g isomaltulose (Palatinose, GI = 32) would result in more N3 sleep, less rapid eye movement (REM) sleep, and better memory consolidation than a high glycemic index (HGI) drink containing 50 g glucose (GI = 100). Healthy males (n = 20) attended the laboratory on three occasions at least a week apart (one acclimatization night and two test nights).
View Article and Find Full Text PDFEntropy (Basel)
January 2025
Departamento de Ingeniería Eléctrica y Computadoras, Instituto de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, Argentina.
Studying sleep stages is crucial for understanding sleep architecture, which can help identify various health conditions, including insomnia, sleep apnea, and neurodegenerative diseases, allowing for better diagnosis and treatment interventions. In this paper, we explore the effectiveness of generalized weighted permutation entropy (GWPE) in distinguishing between different sleep stages from EEG signals. Using classification algorithms, we evaluate feature sets derived from both standard permutation entropy (PE) and GWPE to determine which set performs better in classifying sleep stages, demonstrating that GWPE significantly enhances sleep stage differentiation, particularly in identifying the transition between N1 and REM sleep.
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