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Abstract Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8–23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

Parental mental health is associated with children’s emotion regulation (ER) and risk for psychopathology. The relationship between parental psychopathology and children’s functional ER networks and whether connectivity patterns mediate the relationship between parent and youth psychopathology remains unexplored. Using resting-state functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study (N = 4202, mean age = 10.0) and a multilevel approach, we analyzed the relationship between self-reported parental psychopathology and their offsprings’ connectivity of four ER networks, as well as associations with self-reported youth psychopathology at a 3-year follow-up. Parental internalizing and total problems were associated with 1) higher connectivity between a subcortical-cortical integrative and ventrolateral prefrontal cortical (PFC) network, 2) lower connectivity between dorsolateral and ventrolateral PFC networks involved in cognitive aspects of ER, and 3) lower connectivity within a subcortical ER network (β = −0.05–0.04). Parental externalizing and total problems were associated with lower connectivity within the integrative network (βext = −0.05; βtot = −0.04). Mediation analyses yielded direct effects of parental to youth psychopathology, but no mediation effect of ER network connectivity. Overall, our results show that ER network connectivity in youth is related to parental psychopathology, yet do not explain intergenerational transmission of psychopathology.

Positive Appraisal Style Theory of Resilience posits that a person's general style of evaluating stressors plays a central role in mental health and resilience. Specifically, a tendency to appraise stressors positively (positive appraisal style; PAS) is theorized to be protective of mental health and thus a key resilience factor. To this date no measures of PAS exist. Here, we present two scales that measure perceived positive appraisal style, one focusing on cognitive processes that lead to positive appraisals in stressful situations (PASS-process), and the other focusing on the appraisal contents (PASS-content). For PASS-process, the items of the existing questionnaires Brief COPE and CERQ-short were analyzed in exploratory and confirmatory factor analyses (EFA, CFA) in independent samples (N = 1157 and N = 1704). The resulting 10-item questionnaire was internally consistent (α = .78, 95% CI [.86, .87]) and showed good convergent and discriminant validity in comparisons with self-report measures of trait optimism, neuroticism, urgency, and spontaneity. For PASS-content, a newly generated item pool of 29 items across stressor appraisal content dimensions (probability, magnitude, and coping potential) were subjected to EFA and CFA in two independent samples (N = 1174 and N = 1611). The resulting 14-item scale showed good internal consistency (α = .87, 95% CI [.86, .87]), as well as good convergent and discriminant validity within the nomological network. The two scales are a new and reliable way to assess self-perceived positive appraisal style in large-scale studies, which could offer key insights into mechanisms of resilience.

Abstract Deep learning approaches can uncover complex patterns in data. In particular, variational autoencoders achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project, which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, and a differing number of observations per individual. This technique allows us to subsequently investigate individual reactions to stimuli, such as the mental health impact of stressors. A potentially large number of baseline characteristics can then be linked to this individual response by regularized regression, e.g., for identifying resilience factors. Thus, our new method provides a way of connecting different kinds of complex longitudinal and baseline measures via individualized, dynamic models. The promising results obtained in the exemplary resilience application indicate that our proposal for dynamic deep learning might also be more generally useful for other application domains.

Abstract The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p

Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. How to operationalize resilience and to determine the factors and processes that lead to good long-term mental health outcomes in stressor-exposed individuals is a matter of ongoing debate and of critical importance for the advancement of the field. One of the biggest challenges for implementing an outcome-based definition of resilience in longitudinal observational study designs lies in the fact that real-life adversity is usually unpredictable and that its substantial qualitative as well as temporal variability between subjects often precludes defining circumscribed time windows of inter-individually comparable stressor exposure relative to which the maintenance or recovery of mental health can be determined. To address this pertinent issue, we propose to frequently and regularly monitor stressor exposure (E) and mental health problems (P) throughout a study's observation period [Frequent Stressor and Mental Health Monitoring (FRESHMO)-paradigm]. On this basis, a subject's deviation at any single monitoring time point from the study sample's normative E–P relationship (the regression residual) can be used to calculate that subject's current mental health reactivity to stressor exposure (“stressor reactivity,” SR). The SR score takes into account the individual extent of experienced adversity and is comparable between and within subjects. Individual SR time courses across monitoring time points reflect intra-individual temporal variability in SR, where periods of under-reactivity (negative SR score) are associated with accumulation of fewer mental health problems than is normal for the sample. If FRESHMO is accompanied by regular measurement of potential resilience factors, temporal changes in resilience factors can be used to predict SR time courses. An increase in a resilience factor measurement explaining a lagged decrease in SR can then be considered to index a process of adaptation to stressor exposure that promotes a resilient outcome (an allostatic resilience process). This design principle allows resilience research to move beyond merely determining baseline predictors of resilience outcomes, which cannot inform about how individuals successfully adjust and adapt when confronted with adversity. Hence, FRESHMO plus regular resilience factor monitoring incorporates a dynamic-systems perspective into resilience research.

Meditation-based mental training interventions show physical and mental health benefits. However, it remains unclear how different types of mental practice affect emotion processing at both the neuronal and the behavioural level. In the context of the ReSource project, 332 participants underwent an fMRI scan while performing an emotion anticipation task before and after three 3-month training modules cultivating 1) attention and interoceptive awareness (Presence); 2) socio-affective skills, such as compassion (Affect); 3) socio-cognitive skills, such as theory of mind (Perspective). Only the Affect module led to a significant reduction of experienced negative affect when processing images depicting human suffering. In addition, after the Affect module, participants showed significant increased activation in the right supramarginal gyrus when confronted with negative stimuli. We conclude that socio-affective, but not attention- or meta-cognitive based mental training is specifically effective to improve emotion regulation capabilities when facing adversity.

Although many different accounts of the functions of the default mode network (DMN) have been proposed, few can adequately account for the spectrum of different cognitive functions that utilize this network. The current study used functional magnetic resonance imaging (fMRI) to explore the hypothesis that the role of the DMN in higher order cognition is to allow cognition to be shaped by information from stored representations rather than information in the immediate environment. Using a novel task paradigm, we observed increased BOLD activity in regions of the medial prefrontal cortex and posterior cingulate cortex when individuals made decisions on the location of shapes from the prior trial and decreased BOLD activity when individuals made decisions on the location of shapes on the current trial. These data are inconsistent with views of the DMN as a task-negative system or one that is sensitive only to stimuli with strong personal or emotional ties. Instead the involvement of the DMN when people make decisions about where a shape was, rather than where it is now, supports the hypothesis that the core hubs of the DMN allow cognition to be guided by information other than the immediate perceptual input. We propose that a variety of different forms of higher order thought (such as imagining the future or considering the perspective of another person) engage the DMN because these more complex introspective forms of higher order thought all depend on the capacity for cognition to be shaped by representations that are not present in the external environment.

Recent work has highlighted that the generation of thoughts unrelated to the current environment may be both a cause and a consequence of unhappiness. The current study used lag analysis to examine whether the relationship between self-generated thought and negative affect depends on the content of the thoughts themselves. We found that the emotional content could strongly predict subsequent mood (e.g. negative thoughts were associated with subsequent negative mood). However, this direct relationship was modulated by the socio-temporal content of the thoughts: thoughts that were past- and other-related were associated with subsequent negative mood, even if current thought content was positive. By contrast, future- and self-related thoughts preceded improvements of mood, even when current thought content was negative. These results highlight the important link between self-generated thought and mood and suggest that the socio-temporal content plays an important role in determining whether an individual's future affective state will be happy or sad.