In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.

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...

Individuals with NAP, MDD and BD display differences in emotional stress reactivity. Type of mood disorder may exert a pathoplastic effect on emotional reactivity in individuals with MDD and BD. Individuals with NAP may be most vulnerable to the effects of daily life stress.

Major depressive disorder (MDD) is characterized by high negative affect (NA) and low positive affect (PA), but little is known about emotional reactivity in daily life. The authors used experience sampling methodology to investigate changes in NA and PA following minor daily events in MDD compared with healthy participants. Contrary to expectation, MDD participants did not report more frequent negative events, although they did report fewer positive events. Multilevel regression showed that both NA and PA responses to negative events were blunted in the MDD group, whereas responses to positive events were enhanced. NA responses to negative events persisted longer in MDD participants. Depressed participants with a positive family history or longer current episodes showed relatively greater NA responses to negative events.

In depression, the ability to experience daily life positive affect predicts recovery and reduces relapse rates. Interventions based on the experience sampling method (ESM-I) are ideally suited to provide insight in personal, contextualized patterns of positive affect. The aim of this study was to examine whether add-on ESM-derived feedback on personalized patterns of positive affect is feasible and useful to patients, and results in a reduction of depressive symptomatology. Depressed outpatients (n5102) receiving pharmacological treatment participated in a randomized controlled trial with three arms: an experimental group receiving add-on ESM-derived feedback, a pseudo-experimental group participating in ESM but receiving no feedback, and a control group. The experimental group participated in an ESM procedure (three days per week over a 6-week period) using a palmtop. This group received weekly standardized feedback on personalized patterns of positive affect. Hamilton Depression Rating Scale -17 (HDRS) and Inventory of Depressive Symptoms (IDS) scores were obtained before and after the intervention. During a 6-month follow-up period, five HDRS and IDS assessments were completed. Add-on ESM-derived feedback resulted in a significant and clinically relevant stronger decrease in HDRS score relative to the control group (p<0.01; 25.5 point reduction in HDRS at 6 months). Compared to the pseudo-experimental group, a clinically relevant decrease in HDRS score was apparent at 6 months (B523.6, p50.053). Self-reported depressive complaints (IDS) yielded the same pattern over time. The use of ESM-I was deemed acceptable and the provided feedback easy to understand. Patients attempted to apply suggestions from ESM-derived feedback to daily life. These data suggest that the efficacy of traditional passive pharmacological approach to treatment of major depression can be enhanced by using person-tailored daily life information regarding positive affect.