The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research.

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline's F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).

Assessing Emotion Polyregulation in Daily Life: Who Uses It, When Is It Used, and How Effective Is It?

Most research on emotion regulation has focused on understanding individual emotion regulation strategies. Preliminary research, however, suggests that people often use several strategies to regulate their emotions in a given emotional scenario (polyregulation). The present research examined who uses polyregulation, when polyregulation is used, and how effective polyregulation is when it is used. College students (N = 128; 65.6% female; 54.7% White) completed an in-person lab visit followed by a 2-week ecological momentary assessment protocol with six randomly timed survey prompts per day for up 2 weeks. At baseline, participants completed measures assessing past-week depression symptoms, social anxiety-related traits, and trait emotion dysregulation. During each randomly timed prompt, participants reported up to eight strategies used to change their thoughts or feelings, negative and positive affect, motivation to change emotions, their social context, and how well they felt they were managing their emotions. In pre-registered analyses examining the 1,423 survey responses collected, polyregulation was more likely when participants were feeling more intensely negative and when their motivation to change their emotions was stronger. Neither sex, psychopathology-related symptoms and traits, social context, nor subjective effectiveness was associated with polyregulation, and state affect did not moderate these associations. This study helps address a key gap in the literature by assessing emotion polyregulation in daily life. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00166-x.

The causal systems approach to prolonged grief: Recent developments and future directions.

The network theory of prolonged grief posits that causal interactions among symptoms of prolonged grief play a significant role in their coherence and persistence as a syndrome. Drawing on recent developments in the broader network approach to psychopathology, we argue that advancing our understanding of the causal system that gives rise to prolonged grief will require that we (a) strengthen our assessment of each component of the grief syndrome, (b) investigate intra-individual relationships among grief components as they evolve over time within individuals, (c) incorporate biological and social components into network studies of grief, and (d) generate formal theories that posit precisely how these biological, psychological, and social components interact with one another to give rise to prolonged grief disorder.

Shame, guilt, and pride after loss: Exploring the relationship between moral emotions and psychopathology in bereaved adults.

BACKGROUND: Self-blame following bereavement has been implicated in the development of post-loss psychopathology. However, prior studies have not distinguished between the emotions of shame versus guilt. This study examined the cross-sectional associations among bereavement-related shame, bereavement-related guilt, and two mental disorders that commonly arise after bereavement: complicated grief and depression. In addition, exploratory analyses examined the associations between bereavement-related pride and post-loss psychopathology. METHODS: Participants included 92 bereaved adults who experienced the death of a family member at least one year prior to the study. Participants completed self-report measures of complicated grief symptoms, depression symptoms, shame, guilt, and pride. RESULTS: Shame and guilt were positively correlated with complicated grief and depression symptoms. When controlling for their shared variance, only shame remained a significant predictor of post-loss psychopathology. Follow-up analyses indicated that the effect of guilt on psychopathology depended on the level of shame, and vice versa. At low shame, guilt predicted psychopathology; however guilt did not predict psychopathology at moderate to high shame. At low to moderate guilt, shame predicted psychopathology; however shame did not predict psychopathology at high guilt. Pride negatively predicted depression symptoms, but not complicated grief symptoms, when we controlled for shame and guilt. LIMITATIONS: Limitations include the cross-sectional design and modest sample size. CONCLUSIONS: Our analyses identify shame as the more pathogenic moral emotion for bereaved adults. However, whereas guilt in the absence of shame is often considered adaptive, we found that guilt predicted greater psychological distress at low levels of shame in this sample.

Assessing vulnerability to panic: a systematic review of psychological and physiological responses to biological challenges as prospective predictors of panic attacks and panic disorder.

BACKGROUND: Cognitive-behavioural theories of panic disorder posit that panic attacks arise from a positive feedback loop between arousal-related bodily sensations and perceived threat. In a recently developed computational model formalising these theories of panic attacks, it was observed that the response to a simulated perturbation to arousal provided a strong indicator of vulnerability to panic attacks and panic disorder. In this review, we evaluate whether this observation is borne out in the empirical literature that has examined responses to biological challenge (eg, CO2 inhalation) and their relation to subsequent panic attacks and panic disorder. METHOD: We searched PubMed, Web of Science and PsycINFO using keywords denoting provocation agents (eg, sodium lactate) and procedures (eg, infusion) combined with keywords relevant to panic disorder (eg, panic). Articles were eligible if they used response to a biological challenge paradigm to prospectively predict panic attacks or panic disorder. RESULTS: We identified four eligible studies. Pooled effect sizes suggest that there is biological challenge response has a moderate prospective association with subsequent panic attacks, but no prospective relationship with panic disorder. CONCLUSIONS: These findings provide support for the prediction derived from cognitive-behavioural theories and some preliminary evidence that response to a biological challenge may have clinical utility as a marker of vulnerability to panic attacks pending further research and development. TRIAL REGISTRATION NUMBER: 135908.