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Macro and micro sleep architecture and cognitive performance in older adults

Nature Human Behaviour volume 5, pages 123–145 (2021)Cite this article

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

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Abstract

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.

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Data availability

All PSG data are freely available via the National Sleep Research Resource (http://sleepdata.org). The MESA dataset is available at https://doi.org/10.25822/n7hq-c406. The MrOS dataset is at https://doi.org/10.25822/kc27-0425. 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. (http://zzz.bwh.harvard.edu/luna/). The C/C++ code is available at the following GitHub repository: http://github.com/remnrem/luna-base/. 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: https://doi.org/10.1038/s41562-020-01030-3.

References

  1. Foley, D. J. et al. Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep 18, 425–432 (1995).

    Article  CAS  PubMed  Google Scholar 

  2. Foley, D., Ancoli-Israel, S., Britz, P. & Walsh, J. Sleep disturbances and chronic disease in older adults: results of the 2003 National Sleep Foundation Sleep in America Survey. J. Psychosom. Res. 56, 497–502 (2004).

    Article  PubMed  Google Scholar 

  3. Petersen, R. C. et al. Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56, 303–308 (1999).

    Article  CAS  PubMed  Google Scholar 

  4. Li, S.-C. et al. Transformations in the couplings among intellectual abilities and constituent cognitive processes across the life span. Psychol. Sci. 15, 155–163 (2004).

    Article  PubMed  Google Scholar 

  5. Dijk, D. J., Duffy, J. F. & Czeisler, C. A. Contribution of circadian physiology and sleep homeostasis to age-related changes in human sleep. Chronobiol. Int. 17, 285–311 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Ohayon, M. M., Carskadon, M. A., Guilleminault, C. & Vitiello, M. V. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27, 1255–1273 (2004).

    Article  PubMed  Google Scholar 

  7. Purcell, S. M. et al. Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat. Commun. 8, 15930 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Redline, S. et al. The effects of age, sex, ethnicity, and sleep-disordered breathing on sleep architecture. Arch. Intern. Med. 164, 406–418 (2004).

    Article  PubMed  Google Scholar 

  9. Diering, G. H. et al. Homer1a drives homeostatic scaling-down of excitatory synapses during sleep. Science 355, 511–515 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wilson, M. A. & McNaughton, B. L. Reactivation of hippocampal ensemble memories during sleep. Science 265, 676–679 (1994).

    Article  CAS  PubMed  Google Scholar 

  11. Stickgold, R. & Walker, M. P. Sleep-dependent memory consolidation and reconsolidation. Sleep Med. 8, 331–343 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  12. De Vivo, L. et al. Ultrastructural evidence for synaptic scaling across the wake/sleep cycle. Science 355, 507–510 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Scullin, M. K. & Bliwise, D. L. Sleep, cognition, and normal aging: integrating a half century of multidisciplinary research. Perspect. Psychol. Sci. 10, 97–137 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ohayon, M. M. & Vecchierini, M.-F. Normative sleep data, cognitive function and daily living activities in older adults in the community. Sleep 28, 981–989 (2005).

    Article  PubMed  Google Scholar 

  15. Keage, H. A. D. et al. What sleep characteristics predict cognitive decline in the elderly? Sleep Med. 13, 886–892 (2012).

    Article  PubMed  Google Scholar 

  16. Song, Y. et al. Relationships between sleep stages and changes in cognitive function in older men: the MrOS Sleep Study. Sleep 38, 411–421 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cavuoto, M. G. et al. Objective but not subjective sleep predicts memory in community-dwelling older adults. J. Sleep Res. 25, 475–485 (2016).

    Article  PubMed  Google Scholar 

  18. Blackwell, T. et al. Associations between sleep architecture and sleep-disordered breathing and cognition in older community-dwelling men: the Osteoporotic Fractures in Men sleep study. J. Am. Geriatr. Soc. 59, 2217–2225 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Spira, A. P. et al. Actigraphic sleep duration and fragmentation in older women: associations with performance across cognitive domains. Sleep 40, zsx073 (2017).

    PubMed Central  Google Scholar 

  20. Devore, E. E. et al. Sleep duration in midlife and later life in relation to cognition. J. Am. Geriatr. Soc. 62, 1073–1081 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ramos, A. R. et al. Sleep duration and neurocognitive function in the Hispanic Community Health Study/Study of Latinos. Sleep 39, 1843–1851 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Blackwell, T. et al. Association of sleep characteristics and cognition in older community-dwelling men: the MrOS Sleep Study. Sleep 34, 1347–1356 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Potvin, O. et al. Sleep quality and 1-year incident cognitive impairment in community-dwelling older adults. Sleep 35, 491–499 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Jaussent, I. et al. Excessive sleepiness is predictive of cognitive decline in the elderly. Sleep 35, 1201–1207 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Foley, D. et al. Daytime sleepiness is associated with 3-year incident dementia and cognitive decline in older Japanese–American men. J. Am. Geriatr. Soc. 49, 1628–1632 (2001).

    Article  CAS  PubMed  Google Scholar 

  26. Carvalho, D. Z. et al. Association of excessive daytime sleepiness with longitudinal β-amyloid accumulation in elderly persons without dementia. JAMA Neurol. 75, 672–680 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Mander, B. A., Winer, J. R., Jagust, W. J. & Walker, M. P. Sleep: a novel mechanistic pathway, biomarker, and treatment target in the pathology of Alzheimer’s disease? Trends Neurosci. 39, 552–566 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lafortune, M. et al. Sleep spindles and rapid eye movement sleep as predictors of next morning cognitive performance in healthy middle-aged and older participants. J. Sleep Res. 23, 159–167 (2014).

    Article  PubMed  Google Scholar 

  29. Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T. & Walker, M. P. Old brains come uncoupled in sleep: slow wave–spindle synchrony, brain atrophy, and forgetting. Neuron 97, 221–230.e4 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Winer, J. R. et al. Sleep as a potential biomarker of tau and β-amyloid burden in the human brain. J. Neurosci. 39, 6315–6324 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Muehlroth, B. E. et al. Precise slow oscillation–spindle coupling promotes memory consolidation in younger and older adults. Sci. Rep. 9, 1940 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Prinz, P. N. et al. Sleep, EEG and mental function changes in senile dementia of the Alzheimer’s type. Neurobiol. Aging 3, 361–370 (1982).

    Article  CAS  PubMed  Google Scholar 

  33. Pase, M. P. et al. Sleep architecture and the risk of incident dementia in the community. Neurology 89, 1244–1250 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Varga, A. W. et al. Apnea-induced rapid eye movement sleep disruption impairs human spatial navigational memory. J. Neurosci. 34, 14571–14577 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bjorness, T. E., Riley, B. T., Tysor, M. K. & Poe, G. R. REM restriction persistently alters strategy used to solve a spatial task. Learn. Mem. 12, 352–359 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Smith, C. & Rose, G. M. Evidence for a paradoxical sleep window for place learning in the Morris water maze. Physiol. Behav. 59, 93–97 (1996).

    Article  CAS  PubMed  Google Scholar 

  37. Yaffe, K. et al. Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. J. Am. Med. Assoc. 306, 613–619 (2011).

    CAS  Google Scholar 

  38. Burke, G., Lima, J., Wong, N. D. & Narula, J. The multiethnic study of atherosclerosis. Glob. Heart 11, 267–268 (2016).

    Article  PubMed  Google Scholar 

  39. Bild, D. E. et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am. J. Epidemiol. 156, 871–881 (2002).

    Article  PubMed  Google Scholar 

  40. Chen, X. et al. Racial/ethnic differences in sleep disturbances: the Multi-Ethnic Study of Atherosclerosis (MESA). Sleep 38, 877–888 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. Wechsler, D. Adult Intelligence Scale-III (WAIS-III) (Psychological Corporation/Harcourt, 1996).

  42. Teng, E. L. et al. The Cognitive Abilities Screening Instrument (CASI): a practical test for cross-cultural epidemiological studies of dementia. Int. Psychogeriatr. 6, 45–58 (1994).

    Article  CAS  PubMed  Google Scholar 

  43. Fitzpatrick, A. L. et al. Sociodemographic correlates of cognition in the Multi-Ethnic Study of Atherosclerosis (MESA). Am. J. Geriatr. Psychiatry 23, 684–697 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Reitan, R. M. Validity of the trail making test as an indicator of organic brain damage. Percept. Mot. Skills 8, 271–276 (1958).

    Article  Google Scholar 

  45. Teng, E. L. & Chui, H. C. The Modified Mini-Mental State (3MS) examination. J. Clin. Psychiatry 48, 314–318 (1987).

    CAS  PubMed  Google Scholar 

  46. Kelland, D. Z. & Lewis, R. F. The Digit Vigilance Test: reliability, validity, and sensitivity to diazepam. Arch. Clin. Neuropsychol. 11, 339–344 (1996).

    Article  CAS  PubMed  Google Scholar 

  47. Cappuccio, F. P. & Miller, M. A. Sleep and cardio-metabolic disease. Curr. Cardiol. Rep. 19, 110 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Köhler, S. et al. Temporal evolution of cognitive changes in incident hypertension: prospective cohort study across the adult age span. Hypertension 63, 245–251 (2014).

    Article  PubMed  CAS  Google Scholar 

  49. Roberts, R. E. & Duong, H. T. The prospective association between sleep deprivation and depression among adolescents. Sleep 37, 239–244 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Byers, A. L. & Yaffe, K. Depression and risk of developing dementia. Nat. Rev. Neurol. 7, 323–331 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kluge, M., Schüssler, P. & Steiger, A. Duloxetine increases stage 3 sleep and suppresses rapid eye movement (REM) sleep in patients with major depression. Eur. Neuropsychopharmacol. 17, 527–531 (2007).

    Article  CAS  PubMed  Google Scholar 

  52. DeMartinis, N. A. & Winokur, A. Effects of psychiatric medications on sleep and sleep disorders. CNS Neurol. Disord. Drug Targets 6, 17–29 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Scheer, F. A. J. L. et al. Repeated melatonin supplementation improves sleep in hypertensive patients treated with beta-blockers: a randomized controlled trial. Sleep 35, 1395–1402 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Laventure, S. et al. Beyond spindles: interactions between sleep spindles and boundary frequencies during cued reactivation of motor memory representations. Sleep 41, zsy142 (2018).

    Article  PubMed Central  Google Scholar 

  55. Dubé, J. et al. Cortical thinning explains changes in sleep slow waves during adulthood. J. Neurosci. 35, 7795–7807 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Carrier, J. et al. Sleep slow wave changes during the middle years of life. Eur. J. Neurosci. 33, 758–766 (2011).

    Article  PubMed  Google Scholar 

  57. Martin, N. et al. Topography of age-related changes in sleep spindles. Neurobiol. Aging 34, 468–476 (2013).

    Article  PubMed  Google Scholar 

  58. Jackson, C. L., Patel, S. R., Jackson, W. B., Lutsey, P. L. & Redline, S.Agreement between self-reported and objectively measured sleep duration among white, Black, Hispanic, and Chinese adults in the United States: Multi-Ethnic Study of Atherosclerosis. Sleep 41, zsy057 (2018).

    Article  PubMed Central  Google Scholar 

  59. Bianchi, M. T., Thomas, R. J. & Westover, M. B. An open request to epidemiologists: please stop querying self-reported sleep duration. Sleep Med. 35, 92–93 (2017).

    Article  PubMed  Google Scholar 

  60. Johnson, D. A. et al. Greater cognitive deficits with sleep-disordered breathing among individuals with genetic susceptibility to Alzheimer disease. The Multi-Ethnic Study of Atherosclerosis. Ann. Am. Thorac. Soc. 14, 1697–1705 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Liu, Y. et al. Methylomics of gene expression in human monocytes. Hum. Mol. Genet. 22, 5065–5074 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sun, H. et al. Brain age from the electroencephalogram of sleep. Neurobiol. Aging 74, 112–120 (2019).

    Article  PubMed  Google Scholar 

  63. Jorm, A. F., Masaki, K. H., Petrovitch, H., Ross, G. W. & White, L. R. Cognitive deficits 3 to 6 years before dementia onset in a population sample: the Honolulu–Asia Aging Study. J. Am. Geriatr. Soc. 53, 452–455 (2005).

    Article  PubMed  Google Scholar 

  64. Brody, J. A. & Schneider, E. L. Diseases and disorders of aging: an hypothesis. J. Chronic Dis. 39, 871–876 (1986).

    Article  CAS  PubMed  Google Scholar 

  65. Toepper, M. Dissociating normal aging from Alzheimer’s disease: a view from cognitive neuroscience. J. Alzheimers Dis. 57, 331–352 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Wang, J. L. et al. Suprachiasmatic neuron numbers and rest–activity circadian rhythms in older humans. Ann. Neurol. 78, 317–322 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Swaab, D. F., Fliers, E. & Partiman, T. S. The suprachiasmatic nucleus of the human brain in relation to sex, age and senile dementia. Brain Res. 342, 37–44 (1985).

    Article  CAS  PubMed  Google Scholar 

  68. Blackwell, T. et al. Poor sleep is associated with impaired cognitive function in older women: the Study of Osteoporotic Fractures. J. Gerontol. A Biol. Sci. Med. Sci. 61, 405–410 (2006).

    Article  PubMed  Google Scholar 

  69. Lim, A. S. P. et al. Sleep is related to neuron numbers in the ventrolateral preoptic/intermediate nucleus in older adults with and without Alzheimer’s disease. Brain 137, 2847–2861 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Ward, A. M. et al. Daytime sleepiness is associated with decreased default mode network connectivity in both young and cognitively intact elderly subjects. Sleep 36, 1609–1615 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Carvalho, D. Z. et al. Excessive daytime sleepiness and fatigue may indicate accelerated brain aging in cognitively normal late middle-aged and older adults. Sleep Med. 32, 236–243 (2017).

    Article  PubMed  Google Scholar 

  72. Knutson, K. L. & Turek, F. W. The U-shaped association between sleep and health: the 2 peaks do not mean the same thing. Sleep 29, 878–879 (2006).

    Article  PubMed  Google Scholar 

  73. Patel, S. R., Malhotra, A., Gottlieb, D. J., White, D. P. & Hu, F. B. Correlates of long sleep duration. Sleep 29, 881–889 (2006).

    Article  PubMed  Google Scholar 

  74. Grandner, M. A. & Drummond, S. P. A. Who are the long sleepers? Towards an understanding of the mortality relationship. Sleep Med. Rev. 11, 341–360 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Rauchs, G. et al. Is there a link between sleep changes and memory in Alzheimer’s disease? NeuroReport 19, 1159–1162 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Mölle, M., Bergmann, T. O., Marshall, L. & Born, J. Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep 34, 1411–1421 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Feld, G. & Diekelmann, S.Sleep smart—optimizing sleep for declarative learning and memory. Front. Psychol. 6, 622 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Huber, R., Ghilardi, M. F., Massimini, M. & Tononi, G. Local sleep and learning. Nature 430, 78–81 (2004).

    Article  CAS  PubMed  Google Scholar 

  79. Ju, Y.-E. S. et al. Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain 140, 2104–2111 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Mander, B. A. et al. β-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nat. Neurosci. 18, 1051–1057 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Wang, C. & Holtzman, D. M. Bidirectional relationship between sleep and Alzheimer’s disease: role of amyloid, tau, and other factors. Neuropsychopharmacology 45, 104–120 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. Feinberg, I., Koresko, R. L. & Heller, N. EEG sleep patterns as a function of normal and pathological aging in man. J. Psychiatr. Res. 5, 107–144 (1967).

    Article  CAS  PubMed  Google Scholar 

  83. Spiegel, R., Herzog, A. & Köberle, S. Polygraphic sleep criteria as predictors of successful aging: an exploratory longitudinal study. Biol. Psychiatry 45, 435–442 (1999).

    Article  CAS  PubMed  Google Scholar 

  84. Kim, S. J., Lee, J. H., Lee, D. Y., Jhoo, J. H. & Woo, J. I. Neurocognitive dysfunction associated with sleep quality and sleep apnea in patients with mild cognitive impairment. Am. J. Geriatr. Psychiatry 19, 374–381 (2011).

    Article  PubMed  Google Scholar 

  85. Della Monica, C., Johnsen, S., Atzori, G., Groeger, J. A. & Dijk, D.-J. Rapid eye movement sleep, sleep continuity and slow wave sleep as predictors of cognition, mood, and subjective sleep quality in healthy men and women, aged 20–84 years. Front. Psychiatry 9, 255 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Markowska, A. L. et al. Individual differences in aging: behavioral and neurobiological correlates. Neurobiol. Aging 10, 31–43 (1989).

    Article  CAS  PubMed  Google Scholar 

  87. Stone, W. S., Altman, H. J., Berman, R. F., Caldwell, D. F. & Kilbey, M. M. Association of sleep parameters and memory in intact old rats and young rats with lesions in the nucleus basalis magnocellularis. Behav. Neurosci. 103, 755–764 (1989).

    Article  CAS  PubMed  Google Scholar 

  88. Liguori, C. et al. Orexinergic system dysregulation, sleep impairment, and cognitive decline in Alzheimer disease. JAMA Neurol. 71, 1498–1505 (2014).

    Article  PubMed  Google Scholar 

  89. Czeisler, C. A., Zimmerman, J. C., Ronda, J. M., Moore-Ede, M. C. & Weitzman, E. D. Timing of REM sleep is coupled to the circadian rhythm of body temperature in man. Sleep 2, 329–346 (1980).

    Article  CAS  PubMed  Google Scholar 

  90. Roh, J. H. et al. Potential role of orexin and sleep modulation in the pathogenesis of Alzheimer’s disease. J. Exp. Med. 211, 2487–2496 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Astori, S., Wimmer, R. D. & Lüthi, A. Manipulating sleep spindles—expanding views on sleep, memory, and disease. Trends Neurosci. 36, 738–748 (2013).

    Article  CAS  PubMed  Google Scholar 

  92. Marshall, L., Helgadóttir, H., Mölle, M. & Born, J. Boosting slow oscillations during sleep potentiates memory. Nature 444, 610–613 (2006).

    Article  CAS  PubMed  Google Scholar 

  93. Wilckens, K. A., Ferrarelli, F., Walker, M. P. & Buysse, D. J. Slow-wave activity enhancement to improve cognition. Trends Neurosci. 41, 470–482 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Bellesi, M., Riedner, B. A., Garcia-Molina, G. N., Cirelli, C. & Tononi, G. Enhancement of sleep slow waves: underlying mechanisms and practical consequences. Front. Syst. Neurosci. 8, 208 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Papalambros, N. A. et al. Acoustic enhancement of sleep slow oscillations and concomitant memory improvement in older adults. Front. Hum. Neurosci. 11, 109 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Johns, M. W. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep 14, 540–545 (1991).

    Article  CAS  PubMed  Google Scholar 

  97. Levine, D. W. et al. Validation of the Women’s Health Initiative Insomnia Rating Scale in a multicenter controlled clinical trial. Psychosom. Med. 67, 98–104 (2005).

    Article  PubMed  Google Scholar 

  98. Horne, J. A. & Ostberg, O. A self-assessment questionnaire to determine morningness–eveningness in human circadian rhythms. Int. J. Chronobiol. 4, 97–110 (1976).

    CAS  PubMed  Google Scholar 

  99. Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213 (1989).

    Article  CAS  PubMed  Google Scholar 

  100. Weaver, T. E. et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 20, 835–843 (1997).

    Article  CAS  PubMed  Google Scholar 

  101. Rechtschaffen, A. & Kales, A. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects (United States Government Printing Office, 1968).

  102. Feinberg, I. & Floyd, T. C. Systematic trends across the night in human sleep cycles. Psychophysiology 16, 283–291 (1979).

    Article  CAS  PubMed  Google Scholar 

  103. Hjorth, B. EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29, 306–310 (1970).

    Article  CAS  PubMed  Google Scholar 

  104. Tenke, C. E. & Kayser, J. Reference-free quantification of EEG spectra: combining current source density (CSD) and frequency principal components analysis (fPCA). Clin. Neurophysiol. 116, 2826–2846 (2005).

    Article  PubMed  Google Scholar 

  105. Miller, K. J. et al. Human motor cortical activity is selectively phase-entrained on underlying rhythms. PLoS Comput. Biol. 8, e1002655 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Duffy, F. H. & Als, H. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls—a large case control study. BMC Med. 10, 64 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979).

    Article  Google Scholar 

  108. Ayoub, A. et al. Differential effects on fast and slow spindle activity, and the sleep slow oscillation in humans with carbamazepine and flunarizine to antagonize voltage-dependent Na+ and Ca2+ channel activity. Sleep 36, 905–911 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Zeitlhofer, J. et al. Topographic distribution of sleep spindles in young healthy subjects. J. Sleep Res. 6, 149–155 (1997).

    Article  CAS  PubMed  Google Scholar 

  110. De Gennaro, L. & Ferrara, M. Sleep spindles: an overview. Sleep Med. Rev. 7, 423–440 (2003).

    Article  PubMed  Google Scholar 

  111. Massimini, M., Huber, R., Ferrarelli, F., Hill, S. & Tononi, G. The sleep slow oscillation as a traveling wave. J. Neurosci. 24, 6862–6870 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Dang-Vu, T. T. et al. Spontaneous neural activity during human slow wave sleep. Proc. Natl Acad. Sci. USA 105, 15160–15165 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Muehlroth, B. E. & Werkle-Bergner, M. Understanding the interplay of sleep and aging: methodological challenges. Psychophysiology 57, e13523 (2020).

    Article  PubMed  Google Scholar 

  114. Cohen, M. X. Analyzing Neural Time Series Data: Theory and Practice (The MIT Press, 2014).

  115. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  116. Campello, R. J. G. B., Moulavi, D. & Sander, J. in Advances in Knowledge Discovery and Data Mining Vol. 7819 (eds Pei, J. et al.) 160–172 (Springer, 2013).

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Acknowledgements

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 http://www.mesa-nhlbi.org. 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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Author notes

  1. Deceased: Ha T. Nguyen.

Authors and Affiliations

  1. Beth Israel Deaconess Medical Center, Boston, MA, USA

    Ina Djonlagic & Susan Redline

  2. Harvard Medical School, Boston, MA, USA

    Ina Djonlagic, Sara Mariani, Michael J. Prerau, Katherine E. Burdick, Susan Redline & Shaun M. Purcell

  3. Brigham and Women’s Hospital, Boston, MA, USA

    Sara Mariani, Veerle M. G. T. H. Van Der Klei, Michael J. Prerau, Katherine E. Burdick, Susan Redline & Shaun M. Purcell

  4. University of Washington, Seattle, WA, USA

    Annette L. Fitzpatrick

  5. Emory University, Atlanta, GA, USA

    Dayna A. Johnson

  6. USDA/ARS Children’s Nutrition Center, Baylor College of Medicine, Houston, TX, USA

    Alexis C. Wood

  7. University of California, Los Angeles, Los Angeles, CA, USA

    Teresa Seeman

  8. Wake Forest School of Medicine, Winston-Salem, NC, USA

    Ha T. Nguyen

  9. Columbia University Medical Center, New York, NY, USA

    José A. Luchsinger

  10. Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA

    Joseph M. Dzierzewski

  11. Wake Forest Baptist Medical Center, Winston-Salem, NC, USA

    Stephen R. Rapp

  12. California Pacific Medical Center Research Institute, San Francisco, CA, USA

    Gregory J. Tranah & Katie L. Stone

  13. Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    Kristine Yaffe

Authors
  1. Ina Djonlagic
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Contributions

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.

Corresponding author

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). https://doi.org/10.1038/s41562-020-00964-y

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