Anna E Mullins1,2,3,4, Jong W Kim5,6,7, Keith K H Wong5,8,9, Delwyn J Bartlett5,8, Andrew Vakulin5,10, Derk-Jan Dijk11, Nathaniel S Marshall5,12, Ronald R Grunstein5,6,8,9, Angela L D'Rozario5,13. 1. CIRUS Centre for Sleep and Chronobiology - NHMRC Centre of Research Excellence, Woolcock Institute of Medical Research, University of Sydney, PO Box M77, Missenden Road, Sydney, NSW, 2050, Australia. anna.mullins@sydney.edu.au. 2. Sydney Nursing School, University of Sydney, Sydney, NSW, Australia. anna.mullins@sydney.edu.au. 3. CRC for Alertness, Safety and Productivity, Melbourne, Australia. anna.mullins@sydney.edu.au. 4. The Varga Laboratory, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY, 10029, USA. anna.mullins@sydney.edu.au. 5. CIRUS Centre for Sleep and Chronobiology - NHMRC Centre of Research Excellence, Woolcock Institute of Medical Research, University of Sydney, PO Box M77, Missenden Road, Sydney, NSW, 2050, Australia. 6. CRC for Alertness, Safety and Productivity, Melbourne, Australia. 7. Department of Healthcare IT, Inje University, Inje-ro 197, Kimhae, Kyunsangnam-do, 50834, South Korea. 8. Sydney Medical School, University of Sydney, Sydney, NSW, Australia. 9. Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Sydney Local Health District, Camperdown, Sydney, NSW, Australia. 10. Adelaide Institute for Sleep Health: A Flinders Centre of Research Excellence, College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia. 11. Surrey Sleep Research Centre, UK Dementia Research Institute at the University of Surrey, Guildford, UK. 12. Sydney Nursing School, University of Sydney, Sydney, NSW, Australia. 13. School of Psychology, Faculty of Science, Brain and Mind Centre and Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
Abstract
PURPOSE: Using quantitative EEG (qEEG) analysis, we investigated sleep EEG microstructure as correlates of neurobehavioural performance after 24 h of extended wakefulness in untreated OSA. METHODS: Eight male OSA patients underwent overnight polysomnography (PSG) at baseline followed by 40 h awake with repeated performance testing (psychomotor vigilance task [PVT] and AusEd driving simulator). EEG slowing during REM and spindle density during NREM sleep were calculated using power spectral analysis and a spindle detection algorithm at frontal and central electrode sites. Correlations between sleep EEG microstructure measures and performance after 24-h awake were assessed. RESULTS: Greater EEG slowing during REM sleep was associated with slower PVT reaction times (rho = - 0.79, p = 0.02), more PVT lapses (rho = 0.87, p = 0.005) and more AusEd crashes (rho = 0.73, p = 0.04). Decreased spindle density in NREM sleep was also associated with slower PVT reaction times (rho = 0.89, p = 0.007). Traditional PSG measures of disease severity were not consistent correlates of neurobehavioural performance in OSA. CONCLUSIONS: Sleep EEG microstructure measures recorded during routine PSG are associated with impaired vigilance in OSA patients after sleep deprivation. SIGNIFICANCE: Quantitative brain oscillatory (or EEG)-based measures of sleep may better reflect the deleterious effects of untreated OSA than traditional PSG metrics in at-risk individuals. Trial Registration ACTRN12606000066583.
PURPOSE: Using quantitative EEG (qEEG) analysis, we investigated sleep EEG microstructure as correlates of neurobehavioural performance after 24 h of extended wakefulness in untreated OSA. METHODS: Eight male OSA patients underwent overnight polysomnography (PSG) at baseline followed by 40 h awake with repeated performance testing (psychomotor vigilance task [PVT] and AusEd driving simulator). EEG slowing during REM and spindle density during NREM sleep were calculated using power spectral analysis and a spindle detection algorithm at frontal and central electrode sites. Correlations between sleep EEG microstructure measures and performance after 24-h awake were assessed. RESULTS: Greater EEG slowing during REM sleep was associated with slower PVT reaction times (rho = - 0.79, p = 0.02), more PVT lapses (rho = 0.87, p = 0.005) and more AusEd crashes (rho = 0.73, p = 0.04). Decreased spindle density in NREM sleep was also associated with slower PVT reaction times (rho = 0.89, p = 0.007). Traditional PSG measures of disease severity were not consistent correlates of neurobehavioural performance in OSA. CONCLUSIONS: Sleep EEG microstructure measures recorded during routine PSG are associated with impaired vigilance in OSA patients after sleep deprivation. SIGNIFICANCE: Quantitative brain oscillatory (or EEG)-based measures of sleep may better reflect the deleterious effects of untreated OSA than traditional PSG metrics in at-risk individuals. Trial Registration ACTRN12606000066583.
Authors: Jesse L Parker; Sarah L Appleton; Yohannes Adama Melaku; Angela L D'Rozario; Gary A Wittert; Sean A Martin; Barbara Toson; Peter G Catcheside; Bastien Lechat; Alison J Teare; Robert J Adams; Andrew Vakulin Journal: J Clin Sleep Med Date: 2022-06-01 Impact factor: 4.324
Authors: Anna E Mullins; Korey Kam; Ankit Parekh; Omonigho M Bubu; Ricardo S Osorio; Andrew W Varga Journal: Neurobiol Dis Date: 2020-08-27 Impact factor: 5.996