Subjective sleep more predictive of global cognitive function than objective sleep in older adults: A specification curve analysis.

OBJECTIVES: Sleep is associated with cognitive function in older adults. In the current study, we examined this relationship from subjective and objective perspectives, and determined the robustness and dimensional specificity of the associations using a comprehensive modelling approach. METHODS: Multiple dimensions of subjective (sleep quality and daytime sleepiness) and objective sleep (sleep stages, sleep parameters, sleep spindles, and slow oscillations), as well as subjectively reported and objectively measured cognitive function were collected from 55 older adults. Specification curve analysis was used to examine the robustness of correlations for the effects of sleep on cognitive function. RESULTS: Robust associations were found between sleep and objectively measured cognitive function, but not with subjective cognitive complaints. In addition, subjective sleep showed robust and consistent associations with global cognitive function, whereas objective sleep showed a more domain-specific association with episodic memory. Specifically, subjective sleep quality and daytime sleepiness correlated with global cognitive function, and objective sleep parameters correlated with episodic memory. CONCLUSIONS: Overall, associations between sleep and cognitive function in older adults depend on how they are measured and which specific dimensions of sleep and domains of cognitive function are considered. It highlights the importance of focusing on specific associations to ameliorate the detrimental effects of sleep disturbance on cognitive function in later life.

The influence of cognitive activity on subsequent daytime nap: A deep neural network model based on sleep spindles.

OBJECTIVES: Understanding the influence of cognitive activity on subsequent sleep has both theoretical and applied implications. This study aims to investigate the effect of pre-sleep cognitive activity, in the context of avoiding emotional interference, on macro-sleep and sleep spindles. METHODS: In a within-subjects design, participants' sleep electroencephalography was collected in both the with and without pre-sleep cognitive activity conditions. Subsequent macro-sleep (i.e., sleep stage distribution and sleep parameters) and spindle characteristics (i.e., density, amplitude, duration, and frequency) were analyzed. In addition, a novel machine learning framework (i.e., deep neural network, DNN) was used to discriminate between cognitive activity and control conditions. RESULTS: There were no significant differences in macro-sleep and sleep spindles between the cognitive activity and control conditions. Spindles-based DNN models achieved over 96% accuracy in differentiating between the two conditions, with fast spindles performing better than full-range and slow spindles. CONCLUSIONS: These results suggest a weak but positive effect of pre-sleep cognitive activity on subsequent sleep. It sheds light on a possible low-cost and easily accessible sleep intervention strategy for clinical and educational purposes.

Relationship between self-reported sleep and cognitive function: a specification curve analysis.

The relationship between self-reported sleep and cognitive function is complex; it is unclear whether self-reported sleep is a robust correlate of people's cognitive function. We address this gap by using a comprehensive large-scale dataset (N = 1054) coupled with a novel modeling approach, specification curve analysis (SCA), to test the association between self-reported sleep and cognitive function. The results of the SCA showed robust correlations between self-reported sleep and cognitive function, with poorer sleep associated with worse cognitive function. Furthermore, the correlations between sleep components and cognitive function were heterogeneous, with differences emerging across cognitive measures and domains. Specifically, daytime dysfunction was associated with the strongest effect on subjective cognitive function, whereas sleep duration and sleep efficiency had the strongest effect on objective cognitive function. Therefore, the relationship between self-reported sleep and cognition depends largely on what and how cognitive function is measured. Our findings guide measurement and domain selection for future research on the role of sleep in cognitive function.

Which models are appropriate for six subtropical forests: species-area and species-abundance models.

The species-area relationship is one of the most important topic in the study of species diversity, conservation biology and landscape ecology. The species-area relationship curves describe the increase of species number with increasing area, and have been modeled by various equations. In this paper, we used detailed data from six 1-ha subtropical forest communities to fit three species-area relationship models. The coefficient of determination and F ratio of ANOVA showed all the three models fitted well to the species-area relationship data in the subtropical communities, with the logarithm model performing better than the other two models. We also used the three species-abundance distributions, namely the lognormal, logcauchy and logseries model, to fit them to the species-abundance data of six communities. In this case, the logcauchy model had the better fit based on the coefficient of determination. Our research reveals that the rare species always exist in the six communities, corroborating the neutral theory of Hubbell. Furthermore, we explained why all species-abundance figures appeared to be left-side truncated. This was due to subtropical forests have high diversity, and their large species number includes many rare species.