Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep

Cognitive and Physiological Measures in Well-Being Science: Limitations and Lessons.

Social and personality psychology have been criticized for overreliance on potentially biased self-report variables. In well-being science, researchers have called for more "objective" physiological and cognitive measures to evaluate the efficacy of well-being-increasing interventions. This may now be possible with the recent rise of cost-effective, commercially available wireless physiological recording devices and smartphone-based cognitive testing. We sought to determine whether cognitive and physiological measures, coupled with machine learning methods, could quantify the effects of positive interventions. The current 2-part study used a college sample (N = 245) to contrast the cognitive (memory, attention, construal) and physiological (autonomic, electroencephalogram) effects of engaging in one of two randomly assigned writing activities (i.e., prosocial or "antisocial"). In the prosocial condition, participants described an interaction when they acted in a kind way, then described an interaction when they received kindness. In the "antisocial" condition, participants wrote instead about an interaction when they acted in an unkind way and received unkindness, respectively. Our study replicated previous research on the beneficial effects of recalling prosocial experiences as assessed by self-report. However, we did not detect an effect of the positive or negative activity intervention on either cognitive or physiological measures. More research is needed to investigate under what conditions cognitive and physiological measures may be applicable, but our findings lead us to conclude that they should not be unilaterally favored over the traditional self-report approach

Cognitive and Physiological Measures in Well-Being Science: Limitations and Lessons.

Social and personality psychology have been criticized for overreliance on potentially biased self-report variables. In well-being science, researchers have called for more "objective" physiological and cognitive measures to evaluate the efficacy of well-being-increasing interventions. This may now be possible with the recent rise of cost-effective, commercially available wireless physiological recording devices and smartphone-based cognitive testing. We sought to determine whether cognitive and physiological measures, coupled with machine learning methods, could quantify the effects of positive interventions. The current 2-part study used a college sample (N = 245) to contrast the cognitive (memory, attention, construal) and physiological (autonomic, electroencephalogram) effects of engaging in one of two randomly assigned writing activities (i.e., prosocial or "antisocial"). In the prosocial condition, participants described an interaction when they acted in a kind way, then described an interaction when they received kindness. In the "antisocial" condition, participants wrote instead about an interaction when they acted in an unkind way and received unkindness, respectively. Our study replicated previous research on the beneficial effects of recalling prosocial experiences as assessed by self-report. However, we did not detect an effect of the positive or negative activity intervention on either cognitive or physiological measures. More research is needed to investigate under what conditions cognitive and physiological measures may be applicable, but our findings lead us to conclude that they should not be unilaterally favored over the traditional self-report approach

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks.

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep

Human Memories Can Be Linked by Temporal Proximity.

Real-world memories involve the integration of multiple events across time, yet the mechanisms underlying this integration is unknown. Recent rodent studies show that distinct memories encoded within a few hours, but not several days, share a common neural ensemble, and a common fate whereby later fear conditioning can transfer from one memory to the other. Here, we tested if distinct memories could be linked by temporal proximity in humans. 74 young adults encoded two memories (A and B) close (3-h) or far apart (7-day) in time. One day after encoding the second memory (B), Memory A was updated by pairing it with electric shock (i.e., fear conditioning). We tested whether the memory and fear associated with Memory B would be stronger in the 3-h, compared with the 7-day condition. Results were generally consistent with rodent studies, where we found heightened Memory B fear expression when the two memories were encoded close, but not far apart, in time. Furthermore, there was less forgetting of Memory B in the 3-h compared to 7-day condition. Our results suggest that temporally proximal memories may be linked, such that updating one experience updates the other