[en] Age-related cognitive decline arises from alterations in brain structure as well as in sleep-wake regulation. Here, we investigated whether preserved wake-dependent regulation of cortical function could represent a positive factor for cognitive fitness in aging. We quantified cortical excitability dynamics during prolonged wakefulness as a sensitive marker of age-related alteration in sleep-wake regulation in 60 healthy older individuals (50-69 y; 42 women). Brain structural integrity was assessed with amyloid-beta- and tau-PET, and with MRI. Participants' cognition was investigated using an extensive neuropsychological task battery. We show that individuals with preserved wake-dependent cortical excitability dynamics exhibit better cognitive performance, particularly in the executive domain which is essential to successful cognitive aging. Critically, this association remained significant after accounting for brain structural integrity measures. Preserved dynamics of basic brain function during wakefulness could therefore be essential to cognitive fitness in aging, independently from age-related brain structural modifications that can ultimately lead to dementia.
Disciplines :
Neurosciences & behavior
Author, co-author :
Van Egroo, Maxime ; Université de Liège - ULiège > CRC In vivo Imaging-Sleep and chronobiology
Narbutas, Justinas ; Université de Liège - ULiège > CRC In vivo Imaging-Aging & Memory
Chylinski, Daphné ; Université de Liège - ULiège > CRC In vivo Imaging-Sleep and chronobiology
Giorgio, A. et al. Age-related changes in grey and white matter structure throughout adulthood. Neuroimage 51, 943–951 (2010).
Jack, C. R. et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207–216 (2013).
Reuter-Lorenz, P. A. & Park, D. C. How does it STAC up? revisiting the scaffolding theory of aging and cognition. Neuropsychol. Rev. 24, 355–370 (2014).
Jack, C. R. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement. 14, 535–562 (2018).
Braak, H., Thal, D. R., Ghebremedhin, E. & Tredici, K. Del Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J. Neuropathol. Exp. Neurol. 70, 960–969 (2011).
Van Egroo, M. et al. Sleep–wake regulation and the hallmarks of the pathogenesis of Alzheimer’s disease. Sleep zsz017, 42, (2019).
Van Someren, E. J. W. et al. Medial temporal lobe atrophy relates more strongly to sleep-wake rhythm fragmentation than to age or any other known risk. Neurobiol. Learn. Mem. 160, 132–138 (2019).
Dubé, J. et al. Cortical thinning explains changes in sleep slow waves during adulthood. J. Neurosci. 35, 7795–7807 (2015).
Sexton, C. E., Storsve, A. B., Walhovd, K. B., Johansen-Berg, H. & Fjell, A. M. Poor sleep quality is associated with increased cortical atrophy in community-dwelling adults. Neurology 83, 967–973 (2014).
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).
Mander, B. A. et al. β-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nat. Neurosci. 18, 1051–1057 (2015).
Musiek, E. S. et al. Circadian rest-activity pattern changes in aging and preclinical Alzheimer disease. JAMA Neurol. 75, 582–590 (2018).
Schmidt, C., Peigneux, P. & Cajochen, C. Age-related changes in sleep and circadian rhythms: Impact on cognitive performance and underlying neuroanatomical networks. Front. Neurol. 3, 118 (2012).
Duffy, J. F. & Dijk, D. J. Getting through to circadian oscillators: why use constant routines? J. Biol. Rhythms 17, 4–13 (2002).
Carrier, J. et al. Sleep slow wave changes during the middle years of life. Eur. J. Neurosci. 33, 758–766 (2011).
Gaggioni, G. et al. Age-related decrease in cortical excitability circadian variations during sleep loss and its links with cognition. Neurobiol. Aging 78, 52–63 (2019).
Rizzo, V., Richman, J. & Puthanveettil, S. V. Dissecting mechanisms of brain aging by studying the intrinsic excitability of neurons. Front. Aging Neurosci. 7, 1–9 (2015).
Oh, M. M., Oliveira, F. A. & Disterhoft, J. F. Learning and aging related changes in intrinsic neuronal excitability. Front. Aging Neurosci. 2, 1–10 (2010).
Yaffe, K., Falvey, C. M. & Hoang, T. Connections between sleep and cognition in older adults. Lancet Neurol. 13, 1017–1028 (2014).
Oosterman, J. M., Van Someren, E. J. W., Vogels, R. L. C., Van Harten, B. & Scherder, E. J. A. Fragmentation of the rest-activity rhythm correlates with age-related cognitive deficits. J. Sleep. Res. 18, 129–135 (2009).
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).
Bubu, O. M. et al. Sleep, cognitive impairment and Alzheimer’s disease: a systematic review and meta-analysis. Sleep 40, 1–18 (2016).
Shi, L. et al. Sleep disturbances increase the risk of dementia: a systematic review and meta-analysis. Sleep. Med. Rev. 40, 4–16 (2018).
Huber, R. et al. Human cortical excitability increases with time awake. Cereb. Cortex 23, 332–338 (2013).
Ly, J. Q. M. et al. Circadian regulation of human cortical excitability. Nat. Commun. 7, 11828 (2016).
Skorucak, J., Arbon, E. L., Dijk, D.-J. & Achermann, P. Response to chronic sleep restriction, extension, and subsequent total sleep deprivation in humans: adaptation or preserved sleep homeostasis? Sleep 41, 1–17 (2018).
Saletin, J. M., van der Helm, E. & Walker, M. P. Structural brain correlates of human sleep oscillations. Neuroimage 83, 658–668 (2013).
Jurado, M. B. & Rosselli, M. The elusive nature of executive functions: a review of our current understanding. Neuropsychol. Rev. 17, 213–233 (2007).
Niendam, T. A. et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn. Affect. Behav. Neurosci. 12, 241–268 (2012).
Podell, J. E. et al. Neurophysiological correlates of age-related changes in working memory updating. Neuroimage 62, 2151–2160 (2012).
Gorbach, T. et al. Longitudinal association between hippocampus atrophy and episodic-memory decline. Neurobiol. Aging 51, 167–176 (2016).
Stern, Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 11, 1006–1012 (2012).
Masters, C. L. & Selkoe, D. J. Biochemistry of amyloid ß-Protein and amyloid deposits in Alzheimer disease. Cold Spring Harb. Perspect. Med. 2, a006262 (2012).
Kopeikina, K., Hyman, B. & Spires-Jones, T. Soluble forms of tau are toxic in Alzheimer’s disease. Transl. Neurosci. 3, 223–233 (2012).
Meyer, P. T. et al. Effect of aging on cerebral A1adenosine receptors: A [18F]CPFPX PET study in humans. Neurobiol. Aging 28, 1914–1924 (2007).
Scammell, T. E., Arrigoni, E. & Lipton, J. O. Neural circuitry of wakefulness and sleep. Neuron 93, 747–765 (2017).
Chellappa, S. L. et al. Circadian dynamics in measures of cortical excitation and inhibition balance. Sci. Rep. 6, 1–13 (2016).
Wang, J. L. et al. Suprachiasmatic neuron numbers and rest-activity circadian rhythms in older humans. Ann. Neurol. 78, 317–322 (2015).
Farajnia, S., Deboer, T., Rohling, J. H. T., Meijer, J. H. & Michel, S. Aging of the suprachiasmatic clock. Neuroscientist 20, 44–55 (2014).
Norton, S., Matthews, F. E., Barnes, D. E., Yaffe, K. & Brayne, C. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol. 13, 788–794 (2014).
Villemagne, V. L., Doré, V., Burnham, S. C., Masters, C. L. & Rowe, C. C. Imaging tau and amyloid-β proteinopathies in Alzheimer disease and other conditions. Nat. Rev. Neurol. 14, 225–236 (2018).
Guerreiro, R. et al. Genome-wide analysis of genetic correlation in dementia with Lewy bodies. Parkinson’s Alzheimer’s Dis. 38, 10–13 (2016).
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).
Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113 (2007).
Thomas, B. A. et al. PETPVC: A toolbox for performing partial volume correction techniques in positron emission tomography. Phys. Med. Biol. 61, 7975–7993 (2016).
Tamaki, M., Bang, J. W., Watanabe, T. & Sasaki, Y. Night watch in one brain hemisphere during sleep associated with the first-night effect in humans. Curr. Biol. 26, 1190–1194 (2016).
Berthomier, C. et al. Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep 30, 1587–1595 (2007).
Coppieters’t Wallant, D. et al. Automatic artifacts and arousals detection in whole-night sleep EEG recordings. J. Neurosci. Methods 258, 124–133 (2016).
Hull, J. T., Wright, K. P. & Czeisler, C. A. The influence of subjective alertness and motivation on human performance independent of circadian and homeostatic regulation. J. Biol. Rhythms 18, 329–338 (2003).
Danilenko, K. V., Verevkin, E. G., Antyufeev, V. S., Wirz-Justice, A. & Cajochen, C. The hockey-stick method to estimate evening dim light melatonin onset (DLMO) in humans. Chronobiol. Int. 31, 349–355 (2014).
Jaeger, B. C., Edwards, L. J., Das, K. & Sen, P. K. An R 2 statistic for fixed effects in the generalized linear mixed model. J. Appl. Stat. 44, 1086–1105 (2017).
Beck, A. T., Epstein, N., Brown, G. & Steer, R. A. An inventory for measuring clinical anxiety: Psychometric properties. J. Consult. Clin. Psychol. 56, 893–897 (1988).
Beck, A. T., Steer, R. A. & Garbin, G. M. Psychometric properties of the Beck depression inventory: twenty-five years of evaluation. Clin. Psychol. Rev. 8, 77–100 (1988).
Buysse, D. J. et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213 (1989).
Johns, M. W. Daytime sleepiness, snoring, and obstructive sleep apnea: The Epworth Sleepiness Scale. Chest 103, 30–36 (1993).
Horne, J. A. & Ostberg, O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int. J. Chronobiol. 4, 97–110 (1976).