Typical human sleep throughout the night consists of alternating periods of rapid eye movement (REM) sleep and non-REM (NREM) sleep. This ultradian rhythm of NREM/REM cycling is thought to be produced by the state-dependent activity of "REM-on" and "REM-off" brainstem and hypothalamic neuronal populations that, respectively, promote or suppress REM sleep. Synaptic interactions among these populations define REM sleep regulatory networks; however, the identity of the key neuronal populations in these networks and the dynamics of interactions among them are disputed and cannot be addressed comprehensively with current experimental techniques. The purpose of this study is to use physiologically based mathematical models to explore the dynamic implications associated with competing hypotheses for network-based REM sleep regulation. Generally, putative REM sleep regulatory networks fall into two categories: a reciprocal interaction network consisting of an excitatory REM-on population interacting with an inhibitory REM-off population, and a mutual inhibition network where both REM-on and REM-off populations are inhibitory. We focus on the generation of regular periodic cycling solutions, which would generate the NREM/REM ultradian rhythm, in these networks. By applying our understanding of network mechanisms, we develop efficient numerical criteria for the existence of stable cycling solutions in each network structure. To investigate the robustness of cycling, we systematically analyze the response of model dynamics to manipulation of the components governing network interactions. By establishing the implications of network structure for the mechanisms and dynamics of NREM/REM state transitions, this comparative analysis identifies key targets for future experimental work to distinguish the structure of the proposed physiological REM sleep regulatory network. Introduction.Physiologically based mathematical modeling of sleep-wake behavior offers a novel technique for probing the putative neural mechanisms generating transitions between wake and sleep states. Human sleep is composed of both rapid eye movement (REM) sleep and non-REM (NREM) sleep, and the transitions among states of wake, NREM sleep, and REM sleep have distinct, stereotyped properties. The structure of a typical consolidated nighttime sleep period begins with NREM sleep and reveals a robust cyclic alternation between NREM and REM sleep over the course of the night. Known as the ultradian rhythm, these

As a major life transition characterized by changes in social, behavioral, and psychological domains, retirement is associated with numerous risk factors that can contribute to the development of depression in later life. Understanding how these risk factors intersect with overall health and functioning can inform opportunities for mental health promotion during this transition. The objective of this review is to summarize the literature on risk and protective factors for depression during retirement transitions, discuss challenges related to appropriate management of depression in later life, and describe opportunities for prevention and intervention for depression relating to retirement transitions, both within and beyond the health care system. Key implications from this review are that 1) the relationship between depression and retirement is multifaceted; 2) while depression is a common health condition among older adults, this syndrome should not be considered a normative part of aging or of retirement specifically; 3) the existing mental health specialty workforce is insufficient to meet the depression management needs of the aging population, and 4) therefore, there is a need for interprofessional and multidisciplinary intervention efforts for preventing and managing depression among older adults. In sum, both healthcare providers, public health practitioners, and community organizations have meaningful opportunities for promoting the mental health of older adults during such major life transitions.

One million older Americans retire annually. While these transitions are not generally associated with poor mental health, the broader macro-economic context in which retirement transitions take place may shape how they relate to mental health. The objective of this study was to use state-of-the-art natural language processing (NLP) to develop a model to identify retirement transitions from textual data in the National Violent Death Reporting System (NVDRS), and to use that model to examine how the number of suicides related to retirement transitions changed during the recovery from the Great Recession. Data come from the NVDRS (2003 - 2018, n=62,165), a state-based registry of suicide deaths. We used RoBERTa to train a NLP model to identify retirement transitions (e.g., recent retirement, anticipated retirement, unable to retire despite wanting to) from 1,291 annotated sentences from NVDRS text narratives of suicide decedents aged ≥55 (model performance: F1=0.92). Applying this model, 19.35 of every 1,000 suicides among decedents aged ≥55 years mentioned a retirement transition. Decedent characteristics associated with retirement transitions were younger age (< 75 years), having a college education and experiencing financial problems. The probability that a narrative referenced a retirement transition increased 1.495-fold during the Great Recession (2007 - 2009) and declined during recovery (2009-2016) before beginning to increase again. Findings illustrate the utility of NLP methods to identify workforce transitions from NVDRS narratives, and further understanding the impact of macro contextual events like the Great Recession on population mental health.