Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing

Cabeza, Roberto; Albert, Marilyn; Belleville, Sylvie; Craik, Fergus I. M.; Duarte, Audrey; Grady, Cheryl L.; Lindenberger, Ulman; Nyberg, Lars; Park, Denise C.; Reuter‐Lorenz, Patricia A.; Rugg, Michael D.; Steffener, Jason; Rajah, M. Natasha

doi:10.1038/s41583-018-0068-2

Cited by 830 publications

(927 citation statements)

References 116 publications

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“…This is particularly relevant to the research in neurocognitive aging, and may reduce selection bias, for example by permitting the inclusion of individuals with a wider range of hypertension, cardiovascular conditions or comorbidity. The use of RSFA as a mechanism to adjust for confounds in BOLD-fMRI, or as a predictor, will allow the development of better models of ageing and age-related disorders (Cabeza et al, 2018;Tsvetanov et al, 2018). Figure 1.…”

Section: Resultsmentioning

confidence: 99%

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Tsvetanov

Henson

Jones

et al. 2019

Preprint

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Accurate identification of brain function is necessary to understand neurocognitive ageing, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is therefore essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health and neurovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and neurovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and neurovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and neurovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age-differences in RSFA, after controlling for cardiovascular health. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and neurovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterise neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

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Section: Resultsmentioning

confidence: 99%

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Tsvetanov

Henson

Jones

et al. 2019

Preprint

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“…Behavioral variability is widespread among species, including humans 1 , and has been attributed to gene-environment interactions and developmental stochasticity [2][3][4] . In humans, there is high variability in cognitive aging trajectories 5 , which differ between individuals in terms of rate and severity of the age-related cognitive decline. To understand sources of individualized aging, the concepts of "brain reserve" and "brain maintenance" have been introduced 5,6 .…”

Section: Introductionmentioning

confidence: 99%

“…In humans, there is high variability in cognitive aging trajectories 5 , which differ between individuals in terms of rate and severity of the age-related cognitive decline. To understand sources of individualized aging, the concepts of "brain reserve" and "brain maintenance" have been introduced 5,6 . While "brain reserve" refers to the accumulation of neural resources during early life that attenuate later functional decline during aging, "brain maintenance" describes the preservation of brain integrity over time.…”

Section: Introductionmentioning

confidence: 99%

Early-life environmental enrichment generates persistent individualized behavior in mice

Zocher

Schilling²,

Grzyb³

et al. 2019

Preprint

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Individuals differ in their response to environmental stimuli, but the stability of individualized behaviors and their associated changes in brain plasticity are poorly understood. We developed a novel model of enriched environment to longitudinally monitor 40 inbred mice exploring 35 connected cages over periods of three to six months. We show that behavioral individuality that emerged during the first three months of environmental enrichment persisted when mice were withdrawn from the enriched environment for three additional months. Behavioral trajectories were associated with stable inter-individual differences in adult hippocampal neurogenesis and persistent epigenetic effects on neuronal plasticity genes in the hippocampus. Using genome-wide DNA methylation sequencing, we show that one third of the DNA methylation changes were maintained after withdrawal from the enriched environment. Our results suggest that, even under the most constraint conditions controlling genes and environment, early-life experiences result in lasting individualized changes in behavior and brain plasticity.

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“…We suggest that the basis for research on age-dependent differences in a given (neural) process is rooted in thorough theoretical considerations that incorporate the complex and dynamic nature of aging (e.g., Cabeza et al, 2018Cabeza et al, , 2005Lindenberger et al, 2006) and the individual as the primary unit of analysis (Fisher et al, 2018;Grandy et al, 2017;Molenaar and Campbell, 2009;Nesselroade et al, 2007;Nesselroade and Molenaar, 2016;Rose et al, 2013). Ideally, such a research process starts out with an exact definition of a neural or physiological phenomenon that we intend to investigate.…”

Section: Resultsmentioning

confidence: 99%

Understanding the interplay of sleep and aging: Methodological challenges

Muehlroth

Werkle‐Bergner

2019

Preprint

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In quest of new avenues to explain, predict, and treat pathophysiological conditions during aging, research on sleep and aging has flourished. Despite the great scientific potential to pinpoint mechanistic pathways between sleep, aging, and pathology, only little attention has been paid to the suitability of analytic procedures applied to study these interrelations. On the basis of electrophysiological sleep and structural brain data of healthy younger and older adults, we identify, illustrate, and resolve methodological core challenges in the study of sleep and aging. We demonstrate potential biases in common analytic approaches when applied to older populations. We argue that uncovering age‐dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age‐independent interindividual differences. Age‐adapted methodological approaches are thus required to foster the development of valid and reliable biomarkers of age‐associated cognitive pathologies.

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Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing

Cited by 830 publications

References 116 publications

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Early-life environmental enrichment generates persistent individualized behavior in mice

Understanding the interplay of sleep and aging: Methodological challenges

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