Nightmares in the elderly: Associations with self-reported executive functions.

STUDY OBJECTIVES: To describe nightmare phenomenology among community dwelling elderly and to test the hypothesis that reduction in cognitive control is associated with nightmare-related phenomenology including nightmare frequency and severity, greater emotional reactivity, imagery immersion, and dream enactment behaviors (DEBs). METHODS: Study 1: Survey with multiple regression and ANOVAs on N = 56 people with frequent nightmares plus N = 62 age- and gender-matched controls to quantify the strength of the association between cognitive control variables and nightmare phenomenology. Study 2: Computational simulation of nightmare phenomenology in relation to cognitive control to simulate the empirical findings and to assess the underlying causal theory through computationally supported causal inference. RESULTS: Study 1: Regressions demonstrated a strong association between reduction in cognitive control and more extreme nightmare phenomenology, including severity, frequency, daytime effects, and DEBs. Study 2: The computational simulation of nightmare phenomenology in relation to cognitive control is validated relative to regressions from study 1 and offers computational support for the causal theory explaining the associations in study 1. CONCLUSIONS: In aging people, decline in executive cognitive functions, cognitive control, and inhibitory processes reduce cognitive control over emotions, thus contributing to unusual nightmare activity, including more extreme nightmare phenomenology such as more severe nightmares, greater emotional reactivity, deeper imagery immersion, and DEBs.

Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic.

INTRODUCTION: COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses. METHODS: An agent-based model of COVID-19 was developed with individual agents able to move between 3 states (susceptible, infectious, or recovered), with each agent placed in 1 of 7 social network types and assigned a propensity to comply with NPIs (quarantine, contact tracing, and physical distancing). A series of policy questions were tested to illustrate the impact of social networks and NPI compliance on viral spread among (1) populations, (2) specific at-risk subgroups, and (3) individual trajectories. RESULTS: Simulation outcomes showed large impacts of physical distancing policies on number of infections, with substantial modification by type of social network and level of compliance. In addition, outcomes on metrics that sought to maximize those never infected (or recovered) and minimize infections and deaths showed significantly different epidemic trajectories by social network type and among higher or lower at-risk age cohorts. CONCLUSIONS: Although dynamic simulation models have important limitations, which are discussed, these decision support tools should be a key resource for navigating the ongoing impacts of the COVID-19 pandemic and can help local and national decision makers determine where, when, and how to invest resources.

A neurocomputational theory of nightmares: the role of formal properties of nightmare images.

Study Objectives: To test and extend Levin & Nielsen's (2007) Affective Network Dysfunction (AND) model with nightmare disorder (ND) image characteristics, and then to implement the extension as a computational simulation, the Disturbed Dreaming Model (DDM). Methods: We used AnyLogic V7.2 to computationally implement an extended AND model incorporating quantitative effects of image characteristics including valence, dominance, and arousal. We explored the DDM parameter space by varying parameters, running approximately one million runs, each for one month of model time, varying pathway bifurcation thresholds, image characteristics, and individual-difference variables to quantitively evaluate their combinatory effects on nightmare symptomology. Results: The DDM shows that the AND model extended with pathway bifurcations and image properties is computationally coherent. Varying levels of image properties, we found that when nightmare images exhibit lower dominance and arousal levels, the ND agent will choose to sleep but then has a traumatic nightmare, whereas, when images exhibit greater than average dominance and arousal levels, the nightmares trigger sleep-avoidant behavior, but lower overall nightmare distress at the price of exacerbating nightmare effects during waking hours. Conclusions: Computational simulation of nightmare symptomology within the AND framework suggests that nightmare image properties significantly influence nightmare symptomology. Computational models for sleep and dream studies are powerful tools for testing quantitative effects of variables affecting nightmare symptomology. The DDM confirms the value of extending the Levin & Nielsen AND model of disturbed dreaming/ND.