Critical slowing down as early warning for the onset and termination of depression

Abstract

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression. D epression is one of the main mental health hazards of our time. It can be viewed as a continuum with an absence of depressive symptoms at the low endpoint and severe and debilitating complaints at the high end (1). (Throughout this manuscript, the term "depression" refers to this continuum of depressive symptoms.) The diagnosis major depressive disorder (MDD) defines individuals at the high end of this continuum. Approximately 10-20% (2) of the general population will experience at least one episode of MDD during their lives, but even subclinical levels of depression may considerably reduce quality of life and work productivity (3). Depressive symptoms are therefore associated with substantial personal and societal costs (4,5). The onset of MDD in an individual can be quite abrupt, and similarly rapid shifts from depression into a remitted state, so-called sudden gains, are common (6). However, despite the high prevalence and associated societal costs of depression, we have little insight into how such critical transitions from health to depression (and vice versa) in individuals might be foreseen. Traditionally, the broad array of correlated symptoms found in depressed people (e.g., depressed mood, insomnia, fatigue, concentration problems, loss of interest, suicidal ideation, etc.) was thought to stem from some common cause, much as a lung tumor is the common cause of symptoms such as shortness of breath, chest pain, and coughing up blood. Recently, however, this common-cause view has been challenged (7-9). The alternative view is that the correlated symptoms should be regarded as the result of interactions of components of a complex dynamical system (7,(10)(11)(12). Consequently, new models of the etiology of depression involve a network of interactions between components, such as emotions, cognitions, and behaviors (8,9). This implies, for instance, that a person may become depressed through a causal chain of feelings and experiences, such as the following: stress → negative emotions → sleep problems → anhedonia (9, 13-15). However, the network view also implies that there can be positive feedback mechanisms between symptoms, such...

The relation between short-term emotion dynamics and psychological well-being: A meta-analysis.

“…Smaller short-term perturbations in emotions may accumulate to create adverse consequences for psychological health and risk for affective disorders (Wichers, 2014). In support of such claims, emotion dynamic patterns have been found to precede and prospectively predict changes in psychological well-being or psychopathology across longer periods of time van de Leemput et al, 2014).…”

“…Solid knowledge of the role of emotion dynamics in psychological well-being would be of great value for detection, diagnosis, prognosis, and treatment assessment purposes in this context (e.g., van de Leemput et al, 2014).…”

“Critical slowing down in depression” is a great idea that still needs empirical proof

“…With great interest we read the paper by van de Leemput et al (1). The authors conceptualize depression as a complex dynamic system with two distinct states (normal and depressed) and support this idea with dynamic system theory and model simulations.…”

Resilience, health perceptions, (QOL), stressors, and hospital admissions—Observations from the real world of clinical care of unstable health journeys in Monash Watch (MW), Victoria, Australia

“…A literature on systemic resilience is emerging 17, 18, 26. Greater frailty was associated with greater variation in self‐rated health, greater correlation between physical and mental health, and autocorrelation 17, 27.…”

“…Because stressors are may be unpredictable, continual monitoring may be required to identify triggers with dynamic indicators of resilience and self‐rating of health status. Dynamic indicators of resilience have been correlated with frailty measures17 and depression,18, 19 and approaches becoming implementable in clinical practice9, 20?…”