Reflections on psychological resilience:a comparison of three conceptually different operationalizations in predicting mental health

BACKGROUND: Psychological resilience refers to the ability to maintain mental health or recover quickly after stress. Despite the popularity of resilience research, there is no consensus understanding or operationalization of resilience. OBJECTIVE: We plan to compare three indicators of resilience that each involve a different operationalization of the construct: a) General resilience or one’s self-reported general ability to overcome adversities; b) Daily resilience as momentarily experienced ability to overcome adversities; and c) Recovery speed evident in the pattern of negative affect recovery after small adversities in daily life. These three indicators are constructed per person to investigate their cross-sectional associations, stability over time, and predictive validity regarding mental health. METHODS: Data will be derived from the prospective MIRORR study that comprises 96 individuals at different levels of psychosis risk and contains both single-time assessed questionnaires and 90-days intensive longitudinal data collection at baseline (T0) and three yearly follow-up waves (T1–T3). General resilience is assessed using the Brief Resilience Scale (BRS) at baseline. Daily resilience is measured by averaging daily resilience scores across 90 days. For recovery speed, vector-autoregressive models with consecutive impulse response simulations will be applied to diary data on negative affect and daily stressors to calculate pattern of affect recovery. These indicators will be correlated concurrently (at T0) to assess their overlap and prospectively (between T0 and T1) to estimate their stability. Their predictive potential will be assessed by regression analysis with mental health (SCL-90) as an outcome, resilience indicators as predictors, and stressful life events as a moderator. CONCLUSION: The comparison of different conceptualizations of psychological resilience can increase our understanding of its multifaceted nature and, in future, help improve diagnostic, prevention and intervention strategies aimed at increasing psychological resilience

Cortisol dynamics in depression:Application of a continuous-time process model

Background: The temporal dynamics of cortisol may be altered in depression. Optimally studying these dynamics in daily life requires specific analytical methods. We used a continuous-time multilevel process model to study set point (rhythm-corrected mean), variability around this set point, and regulation strength (speed with which cortisol levels regulate back to the set point after any perturbation). We examined the generalizability of the parameters across two data sets with different sampling and assay methods, and the hypothesis that regulation strength, but not set point or variability thereof, would be altered in depressed, compared to non-depressed individuals. Methods: The first data set is a general population sample of female twins (n = 523), of which 21 were depressed, with saliva samples collected 10 times a day for 5 days. The second data set consists of pair-matched clinically depressed and non-depressed individuals (n = 30), who collected saliva samples 3 times a day for 30 days. Set point, regulation strength and variability were examined using a Bayesian multilevel Ornstein-Uhlenbeck (OU) process model. They were first compared between samples, and thereafter assessed within samples in relation to depression. Results: Set point and variability of salivary cortisol were twice as high in the female twin sample, compared to the pair-matched sample. The ratio between set point and variability, as well as regulation strength, which are relative measures and therefore less affected by the specific assay method, were similar across samples. The average regulation strength was high; after an increase in cortisol, cortisol levels would decrease by 63 % after 10 min, and by 95 % after 30 min, but depressed individuals of the pair-matched sample displayed an even faster regulation strength. Conclusions: The relative parameters of the two data sets. The results suggest that regulation strength is increased in depressed individuals. We recommend the presented methodology for future studies and call for replications with more diverse depressed populations

Group, subgroup, and person specific symptom associations in individuals at different levels of risk for psychosis:A combination of theory-based and data-driven approaches

Introduction Dynamics between symptoms may reveal insights into mechanisms underlying the development of psychosis. We combined a top-down (theory-based) and bottom-up (data-driven) approach to examine which symptom dynamics arise on group-level, on subgroup levels, and on individual levels in early clinical stages. We compared data-driven subgroups to theory-based subgroups, and explored how the data-driven subgroups differed from each other. Methods Data came from N = 96 individuals at risk for psychosis divided over four subgroups (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Each subsequent subgroup represented a higher risk for psychosis (clinical stages 0-1b). All individuals completed 90 days of daily diaries, totaling 8640 observations. Confirmatory Subgrouping Group Iterative Multiple Model Estimation (CS-GIMME) and subgrouping (S-)-GIMME were used to examine group-level associations, respectively, theory-based and data-driven subgroups associations, and individual-specific associations between daily reports of depression, anxiety, stress, irritation, psychosis, and confidence. Results One contemporaneous group path between depression and confidence was identified. CS-GIMME identified several subgroup-specific paths and some paths that overlapped with other subgroups. S-GIMME identified two data-driven subgroups, with one subgroup reporting more psychopathology and lower social functioning. This subgroup contained most individuals from the higher stages and those with more severe psychopathology from the lower stages, and shared more connections between symptoms. Discussion Although subgroup-specific paths were recovered, no clear ordering of symptom patterns was found between different early clinical stages. Theory-based subgrouping distinguished individuals based on psychotic severity, whereas data-driven subgrouping distinguished individuals based on overall psychopathological severity. Future work should compare the predictive value of both methods

Dynamic symptom networks across different at-risk stages for psychosis:An individual and transdiagnostic perspective

The clinical staging model distinguishes different stages of mental illness. Early stages, are suggested to be more mild, diffuse and volatile in terms of expression of psychopathology than later stages. This study aimed to compare individual transdiagnostic symptom networks based on intensive longitudinal data between individuals in different early clinical stages for psychosis. It was hypothesized that with increasing clinical stage (i) density of symptom networks would increase and (ii) psychotic experiences would be more central in the symptom networks. Data came from a 90-day diary study, resulting in 8640 observations within N = 96 individuals, divided over four subgroups representing different early clinical stages (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Sparse Time Series Chain Graphical Models were used to create individual contemporaneous and temporal symptom networks based on 10 items concerning symptoms of depression, anxiety, psychosis, non-specific and vulnerability domains. Network density and symptom centrality (strength) were calculated individually and compared between and within the four subgroups. Level of psychopathology increased with clinical stage. The symptom networks showed large between-individual variation, but neither network density not psychotic symptom strength differed between the subgroups in the contemporaneous (pdensity = 0.59, pstrength > 0.51) and temporal (pdensity = 0.75, pstrength > 0.35) networks. No support was found for our hypothesis that higher clinical stage comes with higher symptom network density or a more central role for psychotic symptoms. Based on the high inter-individual variability, our results highlight the importance of individualized assessment of symptom networks

The added value of daily diary data in 1- and 3-year prediction of psychopathology and psychotic experiences in individuals at risk for psychosis

This study aimed to assess whether adding information on psychological experiences derived from a daily diary to baseline cross-sectional data could improve short- (1-year) and long-term (3-years) prediction of psychopathology and positive psychotic experiences (PEs). We used 90-day daily diary data from 96 individuals in early subclinical risk stages for psychosis. Stepwise linear regression models were built for psychopathology and PEs at 1- and 3-years follow-up, adding: (1) baseline questionnaires, (2) the mean and variance of daily psychological experiences, and (3) individual symptom network density. We assessed whether similar results could be achieved with a subset of the data (7-14- and 30-days). The mean and variance of the diary improved model prediction of short- and long-term psychopathology and PEs, compared to prediction based on baseline questionnaires solely. Similar results were achieved with 7-14- and 30-day subsets. Symptom network density did not improve model prediction except for short-term prediction of PEs. Simple metrics, i.e., the mean and variance from 7 to 14 days of daily psychological experiences assessments, can improve short- and long-term prediction of both psychopathology and PEs in individuals in early subclinical stages for psychosis. Diary data could be a valuable addition to clinical risk prediction models for psychopathology development.</p

The dynamic relationship between sleep and psychotic experiences across the early stages of the psychosis continuum

BACKGROUND: Psychotic disorders develop gradually along a continuum of severity. Understanding factors associated with psychosis development, such as sleep, could aid in identification of individuals at elevated risk. This study aimed to assess (1) the dynamic relationship between psychotic experiences (PEs) and sleep quality and quantity, and (2) whether this relationship differed between different clinical stages along the psychosis continuum.METHODS: We used daily diary data (90 days) of individuals ( N = 96) at early stages (i.e. before a first diagnosis of psychosis) along the psychosis continuum. Multilevel models were constructed with sleep quality and sleep quantity as predictors of PEs and vice versa. Post-hoc, we constructed a multilevel model with both sleep quality and quantity as predictors of PEs. In addition, we tested whether associations differed between clinical stages. RESULTS: Within persons, poorer sleep predicted next day PEs ( B = -0.02, p = 0.01), but not vice versa. Between persons, shorter sleep over the 90-day period predicted more PEs ( B = -0.04, p = 0.002). Experiencing more PEs over 90-days predicted poorer ( B = -0.02, p = 0.02) and shorter ( B = -1.06, p = 0.008) sleep. We did not find any significant moderation effects for clinical stage. CONCLUSIONS: We found a bidirectional relationship between sleep and PEs with daily fluctuations in sleep predicting next day PEs and general patterns of more PEs predicting poorer and shorter sleep. Our results highlight the importance of assessing sleep as a risk marker in the early clinical stages for psychosis.</p

Pathways between childhood victimization and psychosis-like symptoms in the ALSPAC Birth Cohort

Background: Several large population-based studies have demonstrated associations between adverse childhood experiences and later development of psychotic symptoms. However, little attention has been paid to the mechanisms involved in this pathway and the few existing studies have relied on cross-sectional assessments. Methods: Prospective data on 6692 children from the UK Avon Longitudinal Study of Parents and Children (ALSPAC) were used to address this issue. Mothers reported on children’s exposure to harsh parenting and domestic violence in early childhood, and children self-reported on bullying victimization prior to 8.5 years. Presence of children’s anxiety at 10 years and their depressive symptoms at 9 and 11 years were ascertained from mothers, and children completed assessments of self-esteem and locus of control at 8.5 years. Children were interviewed regarding psychotic symptoms at a mean age of 12.9 years. Multiple mediation analysis was performed to examine direct and indirect effects of each childhood adversity on psychotic symptoms. Results: The association between harsh parenting and psychotic symptoms was fully mediated by anxiety, depressive symptoms, external locus of control, and low self-esteem. Bullying victimization and exposure to domestic violence had their associations with psychotic symptoms partially mediated by anxiety, depression, locus of control, and self-esteem. Similar results were obtained following adjustment for a range of confounders and when analyses were conducted for boys and girls separately. Conclusions: These findings tentatively suggest that specific cognitive and affective difficulties in childhood could be targeted to minimize the likelihood of adolescents exposed to early trauma from developing psychotic symptoms

A Network of Psychopathological, Cognitive, and Motor Symptoms in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSDs) are complex syndromes involving psychopathological, cognitive, and also motor symptoms as core features. A better understanding of how these symptoms mutually impact each other could translate into diagnostic, prognostic, and, eventually, treatment advancements. The present study aimed to: (1) estimate a network model of psychopathological, cognitive, and motor symptoms in SSD; (2) detect communities and explore the connectivity and relative importance of variables within the network; and (3) explore differences in subsample networks according to remission status. A sample of 1007 patients from a multisite cohort study was included in the analysis. We estimated a network of 43 nodes, including all the items from the Positive and Negative Syndrome Scale, a cognitive assessment battery and clinical ratings of extrapyramidal symptoms. Methodologies specific to network analysis were employed to address the study’s aims. The estimated network for the total sample was densely interconnected and organized into 7 communities. Nodes related to insight, abstraction capacity, attention, and suspiciousness were the main bridges between network communities. The estimated network for the subgroup of patients in remission showed a sparser density and a different structure compared to the network of nonremitted patients. In conclusion, the present study conveys a detailed characterization of the interrelations between a set of core clinical elements of SSD. These results provide potential novel clues for clinical assessment and intervention

Affective reactivity to daily life stress:Relationship to positive psychotic and depressive symptoms in a general population sample

Introduction: Increased affective reactivity to daily life stress has been found in individuals with psychosis and depression, and in those at risk for these conditions. Because depressive and psychotic symptoms often co-occur, increased affective reactivity in these disorders may be explained by the presence of depressive symptoms, psychotic symptoms, or both. Therefore, we examined whether affective reactivity to daily stress is related to positive psychotic symptoms, independently of depressive symptoms, and vice versa. Methods: We used data from an intensive sampling study in the general population (n = 411), with three measurements a day (t = 90). The following subjective stressors were assessed: appraisal of activities, appraisal of social interactions, and experienced physical discomfort. Affective reactivity was conceptualized as both the positive affect (PA) and negative affect (NA) response to these stressors. By means of mixed model analyses, it was examined whether affective reactivity was independently related to depressive and/or positive psychotic symptoms. Results: The PA response to activities and NA response to social interactions were negatively and positively related to depressive symptoms, respectively, independent of psychotic symptoms. In contrast, no (in) dependent association was found between positive psychotic symptoms and affective reactivity to any of the daily life stressors. These findings were confirmed in a subsample with increased symptoms.  Limitations: The prevalence of positive psychotic symptoms was relatively low in this general population sample. Conclusions: Increased affect reactivity predicts depressive symptoms, but not positive psychotic symptoms. Affective reactivity may still facilitate the development of psychotic symptomatology via its impact on depressive symptoms

The dynamics of social activation and suspiciousness in individuals at ultra-high risk for psychosis

INTRODUCTION: Social functioning is often impaired during the ultra-high risk (UHR) phase for psychosis, but group-level studies regarding the role of social functioning in transition to psychosis are inconsistent. Exploring the inter-individual differences which underlie the association between social functioning and psychotic symptoms in this phase could yield new insights.OBJECTIVE: To examine the idiographic and dynamic association between social activation and suspiciousness in individuals at UHR for psychosis using time-series analysis.METHODS: Twenty individuals at UHR for psychosis completed a diary application every evening for 90 days. Two items on social activation (quantity: 'time spent alone' and quality: 'feeling supported') and two items on suspiciousness ('feeling suspicious' and 'feeling disliked') were used. Time series (T = 90) of each individual were analyzed using vector auto regression analysis (VAR), to estimate the lagged (over 1 day) effect of social activation on suspiciousness, and vice versa, as well as their contemporaneous associations.RESULTS: Heterogeneous person-specific associations between social activation and suspiciousness were found in terms of strength, direction and temporal aspects.CONCLUSIONS: The association between social activation and suspiciousness differs amongst individuals who are at UHR for psychosis. These findings underline the importance of tailoring psychosocial interventions to the individual. Future studies may examine whether using results of single-subject studies in clinical practice to personalize treatment goals leads to better treatment outcomes.</p