The regulation of the timing of sleep is thought to be linked to the temporal dynamics of slow-wave activity [SWA, electroencephalogram (EEG) spectral power in the approximately 0.75-4.5 Hz range] in the cortical non-rapid eye movement (NREM) sleep EEG. In the two-process model of sleep regulation, SWA was used as a direct indication of sleep debt, or Process S. Originally, estimation of the latter was performed in a gross way, by measuring average SWA across NREM-REM sleep cycles, fitting an exponential curve to the values thus obtained and estimating its time constant. In later studies, SWA was assumed to be proportional to the instantaneous decay rate of Process S, rather than taken as a direct reflection of S. Following up on this, we extended the existing model of SWA dynamics in which the effects of intrusions of REM sleep and wakefulness were incorporated. For each subject, a 'gain constant' can be estimated that quantifies the efficiency of SWA in dissipating S. As the course of SWA is variable across cortical locations, local differences are likely to exist in the rate of discharge of S, eventually leading to different levels of S in different cortical regions. In this study, we estimate the extent of local differences of SWA regulation on the basis of the extended model of SWA dynamics, for 26 locations on the scalp. We observed higher efficiency of SWA in dissipation of S in frontal EEG derivations, suggesting that SWA regulation has a clear local aspect. This result further suggests that the process involved in (local) SWA regulation cannot be identical to the Process S involved (with Process C) in effectual determination of sleep timing - a single behaviour that cannot vary between locations on the scalp. We therefore propose to distinguish these two representations and characterize the former, purely SWA-related, as 'Process Z', which then is different for different locations on the scalp. To demonstrate those differences, we compare the gain constants derived for the medial EEG derivations (Fz, Cz, Pz, Oz) with each other and with the decay rate derived from SWA values per NREM-REM sleep cycle.
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Clin Neurophysiol
January 2025
Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA.
Objectives: (1) Gain insight into the mechanisms of postoperative delirium (POD). (2) Determine mechanistic overlap with post-ictal delirium (PID). Epilepsy patients undergoing intracranial electrophysiological monitoring can experience both POD and PID, and thus are suitable subjects for these investigations.
View Article and Find Full Text PDFNeurol Educ
December 2024
From the Departments of Neurology and Neurosurgery (C.S.W.A., E.C.L.), Emory University School of Medicine, Atlanta, GA; Division of Biostatistics (T.M.), Rollins School of Public Health, Emory University, Atlanta, GA; Department of Neurology (G.F.P.), University of Pittsburgh, PA; Department of Neurology (A.S.Z.), Weill Cornell Medical College, New York, NY; Emory University School of Medicine (N.D.), Atlanta, GA; Consulting Web Developer (S.M.), Scotland; Department of Neurology (A.S.), Wake Forest University, Winston-Salem, NC; Departments of Neurology and Neurosurgery (N.S.D), Icahn School of Medicine at Mount Sinai, New York, NY; Department of Neurology (A.L.B.), University of California, San Francisco; Department of Neurology (N.A.M.), University of Maryland School of Medicine, Baltimore, MD; and Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN.
Background And Objectives: Social media platforms such as X (formerly Twitter) are increasingly used in medical education. Characteristics of tweetorials (threaded teaching posts) associated with higher degrees of engagement are unknown. We sought to understand features of neurology-themed tweetorials associated with high sharing and engagement.
View Article and Find Full Text PDFJ Biomed Inform
January 2025
Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China. Electronic address:
Objective: The application of artificial intelligence (AI) in health care has led to a surge of interest in surgical process modeling (SPM). The objective of this study is to investigate the role of deep learning in recognizing surgical workflows and extracting reliable patterns from datasets used in minimally invasive surgery, thereby advancing the development of context-aware intelligent systems in endoscopic surgeries.
Methods: We conducted a comprehensive search of articles related to SPM from 2018 to April 2024 in the PubMed, Web of Science, Google Scholar, and IEEE Xplore databases.
Front Aging Neurosci
January 2025
Department of Neurology, University Hospital of Zurich, Zurich, Switzerland.
Introduction: Improving sleep in murine Alzheimer's disease (AD) is associated with reduced brain amyloidosis. However, the window of opportunity for successful sleep-targeted interventions, regarding the reduction in pathological hallmarks and related cognitive performance, remains poorly characterized.
Methods: Here, we enhanced slow-wave activity (SWA) during sleep via sodium oxybate (SO) oral administration for 2 weeks at early (6 months old) or moderately late (11 months old) disease stages in Tg2576 mice and evaluated resulting neuropathology and behavioral performance.
Sci Rep
January 2025
Department of Information Technology Management, Faculty of Management Technology and Information System, Port Said University, Port Said, 42526, Egypt.
The Internet of Things (IoTs) has revolutionized cities, enabling them to become smarter. IoTs play an important role in monitoring the traffic cameras, roads, smart farming, connected vehicles, air quality, water level, humidity, and carbon dioxide pollution levels in city buildings. One of the major challenges of smart cities is the cyber threat to sensitive data.
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