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Macro and micro sleep architecture and cognitive performance in older adults

A Publisher Correction to this article was published on 16 December 2020

This article has been updated


We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.

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Fig. 1: Demographic profiles of primary sleep and cognitive measures.
Fig. 2: Spindles, SOs and their coupling.
Fig. 3: PSC analysis.
Fig. 4: Primary sleep–cognition association results for omnibus tests and the 23 selected objective metrics, in MESA and MrOS.
Fig. 5: Empirical clustering of sleep metrics.
Fig. 6: Correlations between cognition-associated metrics and other putative confounding and mediating variables.
Fig. 7: Patterns of associations between sleep metrics and age, cognition, cardiometabolic disease and sex.

Data availability

All PSG data are freely available via the National Sleep Research Resource ( The MESA dataset is available at The MrOS dataset is at Full PSG and clinical/covariate data are available for all interested parties pending completion of a Data Access and Use Agreement and Institutional Review Board approval, as outlined on the National Sleep Research Resource website.

Code availability

Sleep EEG data were processed using the Luna package developed by S.M.P. ( The C/C++ code is available at the following GitHub repository: Specifically, the analysis presented in this manuscript used Luna to derive measures of sleep architecture (HYPNO command) and to perform epoch-level artefact detection (SIGSTATS), signal filtering (FILTER), spectral estimation (PSD) and spindle–SO detection (SPINDLES).

Change history

  • 16 December 2020

    A Correction to this paper has been published:


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MESA and the MESA SNP Health Association Resource project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the NHLBI, and by grants UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881, DK063491, K24 AG045334, P30AG059303 and R01AG058969. Funding support for the sleep PSG dataset was provided by grant HL56984. Funding for SNP Health Association Resource genotyping was provided by NHLBI contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, California, United States) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, United States) using the Affymetrix Genome-Wide Human SNP Array 6.0. The authors thank the other investigators, the staff and the participants of the MESA study for valuable contributions. A full list of participating MESA investigators and institutions can be found at The MrOS is supported by National Institutes of Health (NIH) funding. The National Institute on Aging (NIA), National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Center for Advancing Translational Sciences (NCATS) and NIH Roadmap for Medical Research provide support under the following grant numbers: U01 AG027810; U01 AG042124; U01 AG042139; U01 AG042140; U01 AG042143; U01 AG042145; U01 AG042168; U01 AR066160; and UL1 TR000128. The NHLBI provides funding for the MrOS Sleep ancillary study ‘Outcomes of Sleep Disorders in Older Men’ under the following grant numbers: R01 HL071194; R01 HL070848; R01 HL070847; R01 HL070842; R01 HL070841; R01 HL070837; R01 HL070838; and R01 HL070839. In addition, this work was also supported by NIH/National Institute of Mental Health grant R03 MH108908 (to S.M.P.), NIH/NHLBI grant R01 HL146339 (to S.M.P.), NIH/NHLBI grant R21 HL145492 (to S.M.P.), NIH/National Institute on Minority Health and Health Disparities grant R21 MD012738 (to S.M.P.), NIH grant K23AG049955 (to J.M.D.), NIH/NHLBI grant K01HL138211 (to D.J.), NIH/NHLBI grant R35 HL135818 (to S.R.), NIH/National Institute of Neurological Disorders and Stroke grant R01 NS096177 (to M.J.P.), NIH/National Institute on Aging grant R01 AG054081 (to M.J.P.), K24 AG045334 (to J.A.L.), a Beth Israel Deaconess Medical Center Neurology Department Grant (to I.D.) and NIH/NHLBI grant R24 HL114473 (to S.R., S.M. and S.M.P.). This work is, in part, also a publication of the US Department of Agriculture/Agricultural Research Service (USDA/ADS) Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine (Houston, Texas), funded in part by the USDA/ADS (cooperative agreement 58-3092-5-001). The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products or organizations imply endorsement from the US Government. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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I.D., S.R. and S.M.P. conceived of and planned the study. S.R.R., A.L.F., A.C.W., T.S., H.T.N., J.A.L. and S.R. collected the primary sleep and cognitive data in MESA. K.L.S., G.J.T., K.Y. and S.R. collected the primary sleep and cognitive data in MrOS. S.M.P. developed the analytical software and approach. I.D., S.M., M.J.P., V.M.G.T.H.V.D.K., D.J., J.M.D. and K.E.B. discussed the analytical approach/results. S.M.P., I.D. and S.R. drafted the manuscript. All authors reviewed and commented on the final manuscript.

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Correspondence to Shaun M. Purcell.

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Peer review information Primary Handling Editor: Marike Schiffer.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–18, Supplementary Tables 1–7, Supplementary Methods and Supplementary References.

Reporting Summary

Supplementary Data 1–4

Supplementary Data 1. All baseline model results for MESA. Supplementary Data 2. All baseline model results for MrOS. Supplementary Data 3. SO, spindle/SO coupling and spindle/SWA coupling results under alternate SO definitions in MESA. Supplementary Data 4. SO, spindle/SO coupling and spindle/SWA coupling results under alternate SO definitions in MrOS.

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Djonlagic, I., Mariani, S., Fitzpatrick, A.L. et al. Macro and micro sleep architecture and cognitive performance in older adults. Nat Hum Behav 5, 123–145 (2021).

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