46 research outputs found
The Network Structure of Childhood Psychopathology in International Adoptees
International adoptees are at an increased risk of emotional and behavioral problems, especially those who are adopted at an older age. We took a new approach in our study of the network structure and predictability of emotional and behavioral problems in internationally adopted children in Finland. Our sample was from the on-going adoption study and comprised 778 internationally adopted children (387 boys and 391 girls, mean age 10.5 (SD 3.4) years). Networks were estimated using Gaussian graphical models and lasso regularization for all the children, and separately for those who were adopted at different ages. The results showed that anxiety/depressive symptoms, social problems, and aggressiveness were the most central symptom domains. Somatic symptoms were the least central and had the weakest effect on the other domains. Similarly, aggressiveness, social problems, and attention problems were high in terms of predictability (73-65%), whereas internalizing problems were relatively low (28-56%). There were clear but local age-group differences in network structure, symptom centrality, and predictability. According to our findings, network models provide important additional information about the centrality and predictability of specific symptom domains, and thus may facilitate targeted interventions among international adoptees.Peer reviewe
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Unravelling the complex nature of resilience factors and their changes between early and later adolescence
Abstract: Background: Childhood adversity (CA) is strongly associated with mental health problems. Resilience factors (RFs) reduce mental health problems following CA. Yet, knowledge on the nature of RFs is scarce. Therefore, we examined RF mean levels, RF interrelations, RF-distress pathways, and their changes between early (age 14) and later adolescence (age 17). Methods: We studied 10 empirically supported RFs in adolescents with (CA+; n = 631) and without CA (CA−; n = 499), using network psychometrics. Results: All inter-personal RFs (e.g. friendships) showed stable mean levels between age 14 and 17, and three of seven intra-personal RFs (e.g. distress tolerance) changed in a similar manner in the two groups. The CA+ group had lower RFs and higher distress at both ages. Thus, CA does not seem to inhibit RF changes, but to increase the risk of persistently lower RFs. At age 14, but not 17, the RF network of the CA+ group was less positively connected, suggesting that RFs are less likely to enhance each other than in the CA− group. Those findings underpin the notion that CA has a predominantly strong proximal effect. RF-distress pathways did not differ in strength between the CA+ and the CA− group, which suggests that RFs have a similarly protective strength in the two groups. Yet, as RFs are lower and distress is higher, RF-distress pathways may overall be less advantageous in the CA+ group. Most RF interrelations and RF-distress pathways were stable between age 14 and 17, which may help explain why exposure to CA is frequently found to have a lasting impact on mental health. Conclusions: Our findings not only shed light on the nature and changes of RFs between early and later adolescence, but also offer some accounts for why exposure to CA has stronger proximal effects and is often found to have a lasting impact on mental health
Unravelling the complex nature of resilience factors and their changes between early and later adolescence
Abstract: Background: Childhood adversity (CA) is strongly associated with mental health problems. Resilience factors (RFs) reduce mental health problems following CA. Yet, knowledge on the nature of RFs is scarce. Therefore, we examined RF mean levels, RF interrelations, RF-distress pathways, and their changes between early (age 14) and later adolescence (age 17). Methods: We studied 10 empirically supported RFs in adolescents with (CA+; n = 631) and without CA (CA−; n = 499), using network psychometrics. Results: All inter-personal RFs (e.g. friendships) showed stable mean levels between age 14 and 17, and three of seven intra-personal RFs (e.g. distress tolerance) changed in a similar manner in the two groups. The CA+ group had lower RFs and higher distress at both ages. Thus, CA does not seem to inhibit RF changes, but to increase the risk of persistently lower RFs. At age 14, but not 17, the RF network of the CA+ group was less positively connected, suggesting that RFs are less likely to enhance each other than in the CA− group. Those findings underpin the notion that CA has a predominantly strong proximal effect. RF-distress pathways did not differ in strength between the CA+ and the CA− group, which suggests that RFs have a similarly protective strength in the two groups. Yet, as RFs are lower and distress is higher, RF-distress pathways may overall be less advantageous in the CA+ group. Most RF interrelations and RF-distress pathways were stable between age 14 and 17, which may help explain why exposure to CA is frequently found to have a lasting impact on mental health. Conclusions: Our findings not only shed light on the nature and changes of RFs between early and later adolescence, but also offer some accounts for why exposure to CA has stronger proximal effects and is often found to have a lasting impact on mental health
Affect fluctuations examined with ecological momentary assessment in patients with current or remitted depression and anxiety disorders
BACKGROUND: There is increasing interest in day-to-day affect fluctuations of patients with depressive and anxiety disorders. Few studies have compared repeated assessments of positive affect (PA) and negative affect (NA) across diagnostic groups, and fluctuation patterns were not uniformly defined. The aim of this study is to compare affect fluctuations in patients with a current episode of depressive or anxiety disorder, in remitted patients and in controls, using affect instability as a core concept but also describing other measures of variability and adjusting for possible confounders. METHODS: Ecological momentary assessment (EMA) data were obtained from 365 participants of the Netherlands Study of Depression and Anxiety with current (n = 95), remitted (n = 178) or no (n = 92) DSM-IV defined depression/anxiety disorder. For 2 weeks, five times per day, participants filled-out items on PA and NA. Affect instability was calculated as the root mean square of successive differences (RMSSD). Tests on group differences in RMSSD, within-person variance, and autocorrelation were performed, controlling for mean affect levels. RESULTS: Current depression/anxiety patients had the highest affect instability in both PA and NA, followed by remitters and then controls. Instability differences between groups remained significant when controlling for mean affect levels, but differences between current and remitted were no longer significant. CONCLUSIONS: Patients with a current disorder have higher instability of NA and PA than remitted patients and controls. Especially with regard to NA, this could be interpreted as patients with a current disorder being more sensitive to internal and external stressors and having suboptimal affect regulation
Personality, Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health
Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast-evolving psychopathology network (e.g. a depressive episode) can influence the slow-evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed
Positive interventions in depression change the structure of well-being and psychological symptoms: A network analysis
The association between motives, perceived problems and current thoughts of self-harm following an episode of self-harm. A network analysis
A history of self-harm is a major risk factor for suicide. Some patients are more likely than others to repeat suicidal behaviour after an episode of self-harm. Insight in the relation between current thoughts of self-harm, motives for the self-harm episode and perceived problems may improve prevention strategies. Network analysis allows to investigate the co-occurence of these factors and their association with each other.
Methods
Ising model based networks are estimated on data collected between 2007–2015 within the Multicentre Study of Self-harm in Flanders. Patients were interviewed within 24 hours after hospitalization by a trained professional on their motives for the episode of self-harm and their perceived problems. Additionally, they were asked whether they had current thoughts of self-harm. Network analyses are used to determine which motives and problems are uniquely related to current thoughts of self-harm, and which are most central in the network.
Results
Data were used of 6068 patients (2279 males and 3789 females). Four internal motives (wish to die, lost control, escape from situation, situation was unbearable), one external motive (show somebody how hopeless I was) and four perceived problems (psychiatric, loneliness, trauma, rejection) are directly related to current thoughts of self-harm. Of all motives and problems, the motive a wish to die is most strongly related to current thoughts of self-harm. However, external motives are more central in the network when compared to internal motives and perceived problems.
Limitations
Data most probably refer to a selected group of self-harm patients as many individuals who self-harm do not come to the attention of hospital services. Patients might be reluctant to tell professionals they had current thoughts of self-harm.
Conclusions
Many internal motives and problems are directly related to current thoughts of self-harm, but external motives are more central in the network. The clinically most important motive (wish to die) does not play a central role in the network
Exploring the links between alexithymia, empathy and schizotypy in college students using network analysis
Intervening on psychopathology networks: Evaluating intervention targets through simulations
Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods