David Stevens1, Celeste W Y Leong2, Helena Cheung2, Joanne Arciuli3, Andrew Vakulin4, Jong-Won Kim5, Hannah D Openshaw6, Caroline D Rae7, Keith K H Wong8, Derk-Jan Dijk9, Josiah Wei Siong Leow6, Bandana Saini10, Ronald R Grunstein8, Angela L D'Rozario11. 1. CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, The University of Sydney, NSW, Australia; College of Nursing and Health Sciences, Flinders University, Bedford Park, SA, Australia; Adelaide Institute for Sleep Health: A Flinders Centre of Research Excellence, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia. 2. Faculty of Pharmacy, The University of Sydney, Sydney, Australia. 3. College of Nursing and Health Sciences, Flinders University, Bedford Park, SA, Australia. 4. CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, The University of Sydney, NSW, Australia; Adelaide Institute for Sleep Health: A Flinders Centre of Research Excellence, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia. 5. Department of Healthcare IT, Inje University, Inje-ro 197, Kimhae, Kyunsangnam-do, 50834, South Korea. 6. CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, The University of Sydney, NSW, Australia. 7. Neuroscience Research Australia (NeuRA), Sydney, Australia; School of Medical Sciences, The University of New South Wales, Sydney, Australia. 8. CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, The University of Sydney, NSW, Australia; Royal Prince Alfred Hospital, Sydney Health Partners, NSW, Australia; Sydney Medical School, The University of Sydney, NSW, Australia. 9. Surrey Sleep Research Centre, University of Surrey, Guildford, UK; UK Dementia Research Institute at the University of Surrey, UK. 10. CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, The University of Sydney, NSW, Australia; Faculty of Pharmacy, The University of Sydney, Sydney, Australia. 11. CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, The University of Sydney, NSW, Australia; The University of Sydney, School of Psychology, Brain and Mind Centre and Charles Perkins Centre, Australia. Electronic address: angela.drozario@sydney.edu.au.
Abstract
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.
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.