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Objective/background: The aim of this study was to examine the relationship between overnight consolidation of implicit statistical learning with spindle frequency EEG activity and slow frequency delta power during non-rapid eye movement (NREM) sleep in obstructive sleep apnea (OSA).
Patients/methods: Forty-seven OSA participants completed the experiment. Prior to sleep, participants performed a reaction time cover task containing hidden patterns of pictures, about which participants were not informed. After the familiarisation phase, participants underwent overnight polysomnography. 24 h after the familiarisation phase, participants performed a test phase to assess their learning of the hidden patterns, expressed as a percentage of the number of correctly identified patterns. Spindle frequency activity (SFA) and delta power (0.5-4.5 Hz), were quantified from NREM electroencephalography. Associations between statistical learning and sleep EEG, and OSA severity measures were examined.
Results: SFA in NREM sleep in frontal and central brain regions was positively correlated with statistical learning scores (r = 0.41 to 0.31, p = 0.006 to 0.044). In multiple regression, greater SFA and longer sleep onset latency were significant predictors of better statistical learning performance. Delta power and OSA severity were not significantly correlated with statistical learning.
Conclusions: These findings suggest spindle activity may serve as a marker of statistical learning capability in OSA. This work provides novel insight into how altered sleep physiology relates to consolidation of implicitly learnt information in patients with moderate to severe OSA.
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http://dx.doi.org/10.1016/j.sleep.2021.01.035 | DOI Listing |
Cereb Cortex
August 2025
Functional Imaging Laboratory (FIL), Department of Imaging Neuroscience, University College London, 12 Queen Square, London WC1N 3AR, United Kingdom.
This paper marks the 30th anniversary of the Statistical Parametric Mapping (SPM) software and the journal Cerebral Cortex: two modest milestones that mark the inception of cognitive neuroscience. We take this opportunity to reflect on SPM, a generation after its introduction. Each of the authors of this paper-who represent a small selection of the many contributors to SPM-were asked to consider lessons learned, what has gone well, and where there is room for improvement in future development.
View Article and Find Full Text PDFCereb Cortex
August 2025
Statistical Parametric Mapping is a widely used package of software for brain image analysis. It has also been the vehicle for sustained theoretical innovation and global impact in cognitive neuroscience. What can we learn from its success as it reaches middle age?
View Article and Find Full Text PDFDrugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFEur J Neurosci
September 2025
Institute of Public Health, Riga Stradiņš University, Riga, Latvia.
Evidence suggests that working memory (WM) capacity decreases with age, resulting in cognitive decline. Given the link between aging and reduced hippocampal volume, this study examined whether and how hippocampal volume is associated with WM. 46 participants aged 65-85 years (Mage = 71.
View Article and Find Full Text PDFJ Eval Clin Pract
September 2025
Pediatric Allergy and Immunology Department, Akdeniz University Hospital, Akdeniz University, Antalya, Türkiye.
Aims And Objectives: To evaluate the efficacy of YoungAsthma, a nurse-led, web-based mHealth intervention on asthma control and self-efficacy among adolescents with asthma utilizing decision tree analysis.
Background: Asthma is a prevalent chronic condition in pediatric populations, necessitating sustained management for optimal disease control.
Design: A randomized controlled clinical trial.