Alzheimer's disease; ageing; human; memory; neuroscience; sleep; sleep slow waves; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology; General Medicine; General Neuroscience
Abstract :
[en] Sleep alteration is a hallmark of ageing and emerges as a risk factor for Alzheimer's disease (AD). While the fine-tuned coalescence of sleep microstructure elements may influence age-related cognitive trajectories, its association with AD processes is not fully established. Here, we investigated whether the coupling of spindles and slow waves (SW) is associated with early amyloid-β (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years in 100 healthy individuals in late-midlife (50-70 years; 68 women). We found that, in contrast to other sleep metrics, earlier occurrence of spindles on slow-depolarisation SW is associated with higher medial prefrontal cortex Aβ burden (p=0.014, r²β*=0.06) and is predictive of greater longitudinal memory decline in a large subsample (p=0.032, r²β*=0.07, N=66). These findings unravel early links between sleep, AD-related processes, and cognition and suggest that altered coupling of sleep microstructure elements, key to its mnesic function, contributes to poorer brain and cognitive trajectories in ageing.
Disciplines :
Neurology Neurosciences & behavior
Author, co-author :
Chylinski, Daphné ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Van Egroo, Maxime ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Narbutas, Justinas ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Muto, Vincenzo ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging
Bahri, Mohamed Ali ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Berthomier, Christian; Physip SA, Paris, France
SALMON, Eric ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Bastin, Christine ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Phillips, Christophe ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
Collette, Fabienne ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
MAQUET, Pierre ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie
Carrier, Julie ; Centre for Advanced Research in Sleep Medicine, Université de Montréal, Montreal, Canada
Lina, Jean-Marc; Centre for Advanced Research in Sleep Medicine, Université de Montréal, Montreal, Canada
Vandewalle, Gilles ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE] FWB - Fédération Wallonie-Bruxelles [BE] ERDF - European Regional Development Fund [BE] CIHR - Canadian Institutes of Health Research [CA] GE - General Electric [US-MA] [US-MA] Fondation Recherche Alzheimer [BE]
Carrier, J. et al. Sleep slow wave changes during the middle years of life. Eur. J. Neurosci. 33, 758–766 (2011).
Lim, A. S. P., Kowgier, M., Yu, L., Buchman, A. S. & Bennett, D. A. Sleep Fragmentation and the Risk of Incident Alzheimer’s Disease and Cognitive Decline in Older Persons. Sleep 36, 1027–1032 (2013).
Musiek, E. S. & Holtzman, D. M. Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science (80-.). 354, 1004–1008 (2016).
Baril, A. A. et al. Biomarkers of dementia in obstructive sleep apnea. Sleep Med. Rev. 42, 139– 148 (2018).
Elias, A. et al. Risk of Alzheimer’s disease in obstructive sleep apnea syndrome: Amyloid-β and tau imaging. J. Alzheimer’s Dis. 66, 733–741 (2018).
Mander, B. A. et al. β-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nat. Neurosci. 18, 1051–1057 (2015).
Ju, Y.-E. S. et al. Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain 140, 2104–2111 (2017).
Lucey, B. P. et al. Reduced non–rapid eye movement sleep is associated with tau pathology in early Alzheimer’s disease. Sci. Transl. Med. 11, eaau6550 (2019).
Kang, J.-E. et al. Amyloid-B Dynamics Are Regulated by Orexin and the Sleep-Wake Cycle. Science (80-.). 326, 1005–1007 (2009).
Ju, Y. E. S. et al. Sleep quality and preclinical Alzheimer disease. JAMA Neurol. 70, 587–593 (2013).
Sprecher, K. E. et al. Poor sleep is associated with CSF biomarkers of amyloid pathology in cognitively normal adults. Neurology 89, 445–453 (2017).
Lucey, B. P. et al. Effect of sleep on overnight cerebrospinal fluid amyloid β kinetics. Ann. Neurol. 83, 197–204 (2018).
Ooms, S. et al. Effect of 1 Night of Total Sleep Deprivation on Cerebrospinal Fluid β-Amyloid 42 in Healthy Middle-Aged Men: A Randomized Clinical Trial. JAMA Neurol. 71, 971–977 (2014).
Van Egroo, M. et al. Sleep–wake regulation and the hallmarks of the pathogenesis of Alzheimer’s disease. Sleep 42, (2019).
Ju, Y. E. S., Lucey, B. P. & Holtzman, D. M. Sleep and Alzheimer disease pathology-a bidirectional relationship. Nat. Rev. Neurol. 10, 115–119 (2014).
Wang, C. & Holtzman, D. M. Bidirectional relationship between sleep and Alzheimer’s disease: role of amyloid, tau, and other factors. Neuropsychopharmacology (2019). doi:10.1038/s41386-019-0478-5
Ulrich, D. Sleep Spindles as Facilitators of Memory Formation and Learning. Neural Plast. 2016, (2016).
Gais, S., Mölle, M., Helms, K. & Born, J. Learning-dependent increases in sleep spindle density. J. Neurosci. 22, 6830–6834 (2002).
Schmidt, C. et al. Encoding difficulty promotes postlearning changes in sleep spindle activity during napping. J. Neurosci. 26, 8976–8982 (2006).
Mednick, S. et al. The critical role of sleep spindles in hippocampal-dependent memory: a pharmacology study. J. Neurosci. 33, 4494–4504 (2013).
Miyamoto, D., Hirai, D. & Murayama, M. The roles of cortical slow waves in synaptic plasticity and memory consolidation. Front. Neural Circuits 11, 1–8 (2017).
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).
Winer, J. R. et al. Sleep as a potential biomarker of tau and β-amyloid burden in the human brain Abbreviated Title Authors Center for Human Sleep Science, Department of Psychology, University of California Berkeley, Department of Psychiatry and Human Behavior, Universit. (2019).
Winer, J. R. et al. Sleep Disturbance Forecasts β-Amyloid Accumulation across Subsequent Years. Curr. Biol. 30, 4291-4298.e3 (2020).
Achermann, P. & Borbély, A. A. Low-frequency (< 1 hz) oscillations in the human sleep electroencephalogram. Neuroscience 81, 213–222 (1997).
Lee, J., Kim, D. & Shin, H. S. Lack of delta waves and sleep disturbances during non-rapid eye movement sleep in mice lacking α1G-subunit of T-type calcium channels. Proc. Natl. Acad. Sci. U. S. A. 101, 18195–18199 (2004).
Hubbard, J. et al. Rapid fast-delta decay following prolonged wakefulness marks a phase of wake-inertia in NREM sleep. Nat. Commun. 11, 1–16 (2020).
Bouchard, M. et al. Sleeping at the switch. Elife 10, (2021).
Dang-Vu, T. T. et al. Cerebral correlates of delta waves during non-REM sleep revisited. Neuroimage 28, 14–21 (2005).
Dang-Vu, T. T. et al. Functional neuroimaging insights into the physiology of human sleep. Sleep 33, 1589–1603 (2010).
Saletin, J. M., van der Helm, E. & Walker, M. P. Structural brain correlates of human sleep oscillations. Neuroimage 83, 658–668 (2013).
Stark, S. M., Yassa, M. A., Lacy, J. W. & Stark, C. E. L. A task to assess behavioural pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment. Neuropsychologia 51, 2442–2449 (2013).
Marks, S. M., Lockhart, S. N., Baker, S. L. & Jagust, W. J. Tau and β-amyloid are associated with medial temporal lobe structure, function, and memory encoding in normal aging. J. Neurosci. 37, 3192–3201 (2017).
Chylinski, D. et al. Heterogeneity in the links between sleep arousals, amyloid-b, and cognition. JCI Insight 6, e152858 (2021).
Grothe, M. J. et al. In vivo staging of regional amyloid deposition. Neurology 89, 2031–2038 (2017).
Achermann, P., Dijk, D. J., Brunner, D. P. & Borbély, A. A. A model of human sleep homeostasis based on EEG slow-wave activity: Quantitative comparison of data and simulations. Brain Res. Bull. 31, 97–113 (1993).
Fernandez, L. M. J. & Lüthi, A. Sleep spindles: Mechanisms and functions. Physiol. Rev. 100, 805–868 (2020).
Steriade, M. The corticothalamic system in sleep. Front. Biosci. 8, d878–d899 (2003).
Adamantidis, A. R., Gutierrez Herrera, C. & Gent, T. C. Oscillating circuitries in the sleeping brain. Nat. Rev. Neurosci. 20, 746–762 (2019).
Ju, Y.-E. S., Lucey, B. P. & Holtzman, D. M. Sleep and Alzheimer disease pathology-a bidirectional relationship. Nat Rev Neurol 10, 115–9 (2015).
Xie, L. et al. Sleep drives metabolite clearance from the adult brain. Science (80-.). 342, 373– 377 (2013).
Van Egroo, M. et al. Preserved wake-dependent cortical excitability dynamics predict cognitive fitness beyond age-related brain alterations. Commun. Biol. 2, 1–10 (2019).
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).
Yamin, G. & Teplow, D. B. Pittsburgh Compound-B (PiB) binds amyloid β-protein protofibrils. J. Neurochem. 140, 210–215 (2017).
Rizzolo, L. et al. Relationship between brain AD biomarkers and episodic memory performance in healthy aging. Brain Cogn. 148, 105680 (2021).
Berthomier, C. et al. Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep 30, 1587–1595 (2007).
Peter-Derex, L. et al. Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders. J. Clin. Sleep Med. (2020). doi:https://doi.org/10.5664/jcsm.8864
Chylinski, D. et al. Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings. Clocks & Sleep 2, 258–272 (2020).
Wallant, D. C. t. et al. Automatic artifacts and arousals detection in whole-night sleep EEG recordings. J. Neurosci. Methods 258, 124–133 (2016).
Rosinvil, T. et al. Are age and sex effects on sleep slow waves only a matter of EEG amplitude? Sleep 1–33 (2020). doi:10.1093/sleep/zsaa186
Gaudreault, P. O. et al. The association between white matter and sleep spindles differs in young and older individuals. Sleep 41, 1–13 (2018).
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).
Martin, N. et al. Topography of age-related changes in sleep spindles. Neurobiol. Aging 34, 468–476 (2013).
Weiskopf, N. & Helms, G. Multi-parameter mapping of the human brain at 1mm resolution in less than 20 minutes. Proceeding Int. Soc. Magetic Reson. Med. 16, 2241 (2008).
Tabelow, K. et al. hMRI – A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194, 191–210 (2019).
Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851 (2005).
Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113 (2007).
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).
Klunk, W. E. et al. The Centiloid project: Standardizing quantitative amyloid plaque estimation by PET. Alzheimer’s Dement. 11, 1-15.e4 (2015).
Narbutas, J. et al. Associations Between Cognitive Complaints, Memory Performance, Mood, and Amyloid-β Accumulation in Healthy Amyloid Negative Late-Midlife Individuals. J. Alzheimers. Dis. 83, 127–141 (2021).
Stark, S. M., Stevenson, R., Wu, C., Rutledge, S. & Stark, C. E. L. Stability of Age-Related Deficits in the Mnemonic Similarity Task Across Task Variations. Behav. Neurosci. 129, 257– 268 (2015).
Jaeger, B. C., Edwards, L. J., Das, K. & Sen, P. K. An R2statistic for fixed effects in the generalized linear mixed model. J. Appl. Stat. 44, 1086–1105 (2017).
Kain, M. P., Bolker, B. M. & McCoy, M. W. A practical guide and power analysis for GLMMs: detecting among treatment variation in random effects. PeerJ 3, e1226 (2015).
Faul, F., Erdfelder, E., Buchner, A. & Lang, A. G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 41, 1149–1160 (2009).
