Individual Differences in Habitual Sleep Quality and Episodic Memory Network Activity Across the Adult Lifespan

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One of the most common and distressing cognitive declines in normal aging is in episodic memory. In everyday life this might manifest as forgetting a person's name but not the person. Substantial inter-individual variability exists, with some older individuals showing episodic memory performance and neural recruitment equivalent to some young. Existing theories suggest that malleable lifestyle factors, like sleep, contribute to this variability but are not yet well-informed by such evidence. Established age-related declines in habitual sleep quality, the relationship between sleep and neural functioning in memory-supporting brain regions, and the role of sleep in episodic memory consolidation make sleep an essential lifestyle factor to examine. The primary goal of the proposed research is to elucidate the extent to which individual differences in sleep quality contribute to those in neural activity that, in turn, contribute to episodic memory success across the normal adult lifespan. Students from high school through graduate level receive hands on training in all aspects of conducting cognitive neuroscience research pertinent to human memory functioning and the health and lifestyle factors that influence it in diverse, adult lifespan samples. The potential impact of this research for understanding how malleable, individual difference factors affect cognitive decline in aging is high. These results from a large, racially diverse sample will inform existing theories of cognitive changes across the lifespan and future interventions by providing knowledge of the approximate age, specific brain systems, and aspects of memory for which sleep quality is most important and for which sleep interventions may be most effective in the general population.Difficulty utilizing cognitive control and the patterns of prefrontal cortical (PFC) activity underlying it is a major contributor to episodic memory decline in normal aging. Sleep quality is related to PFC integrity and its associated network connectivity. Normal aging has been shown to negatively affect this activity, sleep quality, and episodic memory performance but the relationship between these variables is unclear. The overarching goal of this project is to determine the extent to which age-related sleep disruptions contribute to those in PFC network activity during learning that, in turn, affect episodic memory performance. A mediation model is proposed in which poor sleep quality partially mediates the negative impact of healthy aging on episodic memory via reduced resolution of interference during learning, a major age-related cognitive control deficit. A diverse sample of 90 young, middle-aged, and older adult participants without sleep disorders, perform multiple episodic memory tasks that tax different interference resolution functions is measured to assess the generalizability of the negative impact of poor sleep. Episodic memory is assessed behaviorally and supporting brain activity measured with fMRI. In order to evaluate the specificity of the model to episodic memory, both episodic and item memory are tested. Memory is tested immediately following learning and after a 24-hour delay in order to separately assess the impact of sleep on new learning from consolidation. The influence of multiple confounds, including medication, naps, depression, and exercise is controlled through screening and statistical analyses. Objective measures of sleep are obtained using actigraphy, the best method for assessing habitual sleep patterns over multiple days for a large number of individuals naturally, in their homes, with minimal intrusion. Mediation results from average sleep estimates, the night immediately prior to memory testing, and night-to-night variability are compared in order to separate the impact of habitually poor sleep from variable or a poor night's sleep. Univariate analyses are used to identify PFC regions that support resolution of interference defined by activity that is correlated on a trial-by-trial basis with multivariate pattern analysis (MVPA)-derived interference signals. Functional connectivity analyses are additionally applied to identify the networks affected by sleep quality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.