Personality, Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health

Exploring Gender Differences in the Relationship Between Thyroid Function and Aggressive and Impulsive Behaviors in Patients with Major Depressive Disorder

Purpose

Major depressive disorder (MDD) is a widespread mental health condition with significant global impact. Exploring the gender differences in the interplay between thyroid function, aggression, and impulsivity offers valuable insights into its multifactorial nature and management.

Patients and Methods

A cross-sectional study was conducted at Anhui Mental Health Center and included 113 MDD patients (56 males, 57 females) and 102 healthy controls (45 males and 57 females). Thyroid function was assessed through serum thyroid hormone levels, and impulsivity and aggression were measured using the Buss-Perry Aggression Questionnaire (BPAQ) and Barratt's Impulsiveness Scale version 11 (BIS). Potential confounding factors such as age, education, and Hamilton Depression Rating Scale (HAMD) scores were adjusted for.

Results

Both male and female MDD patients showed significant changes in serum thyrotropin levels (F(1,213)=10.996, p=0.001), impulsivity (F(1,213)=151.521, p<0.05), and aggression (F(1,213)=44.411, p<0.05) compared to healthy controls. MANCOVA revealed significant differences in attentional impulsivity, motor impulsivity, physical aggression, anger, hostility, and self-directed aggression (all p<0.05). Moreover, significant differences between genders were observed in these areas (all p<0.05). In males, TSH levels were inversely related to several behavioral dimensions (all p<0.05), while no such correlation was found in females.

Conclusion

This study highlights the role of thyroid function, especially TSH levels, in influencing impulsivity and aggression in male MDD patients, suggesting a gender-specific physiological-behavioral relationship. The findings contribute to the development of gender-specific treatment strategies. In the future, longitudinal studies with larger sample sizes should be conducted to explore molecular mechanisms for more personalized treatments.

A cross-sectional network analysis of successful aging in a resilience-based framework

Aging inevitably gives rise to many challenges and transitions that can greatly impact our (mental) well-being and quality of life if these are not controlled adequately. Hence, the key to successful aging may not be the absence of these stressors, but the ability to demonstrate resilience against them. The current study set out to explore how resilience and successful aging may intersect by investigating how various resilience capacity-promoting (protective) and resilience capacity-reducing (risk) factors relate to mental well-being and quality of life. Through a large-scale (N = 2000, age 55+, 30 factors) network analysis, we established the interplay between risk/protective factors from various domains, including demographics, (mental) health, (environmental) stress, lifestyle, coping/personality, and ageism. We revealed some unique pathways through which each of these factors contribute to individuals' mental well-being and/or quality of life, and interpreted these findings in terms of a resilience-based framework of successful aging. Our findings emphasize the complexity of factors that can impact quality of life and mental well-being in later life and can steer researchers and practitioners in devising efficacious, multi-pronged interventions that target risk and protective factors simultaneously, thereby maximizing their potential in boosting beneficial outcomes among older individuals.

Multisystemic supports and adolescent resilience to depression over time: A South African mixed methods study

In sub-Saharan countries, like South Africa, there is scant understanding of adolescent resilience to depression over time; the multisystemic resource combinations that support such resilience; and whether more diverse resource combinations yield better mental health dividends. In response, we conducted a longitudinal concurrent nested mixed methods study with 223 South African adolescents (mean age: 17.16 years, SD = 1.73; 64.60% girls; 81.60% Black). Using longitudinal mixture modeling, the quantitative study identified trajectories of depression and associations between trajectory membership and resource diversity. Using a draw-and-write methodology and reflexive thematic analyses, the qualitative study explored the resource diversity associated with each trajectory. Taken together, these studies identified four depression trajectories (Stable Low; Declining; Worsening; Chronic High) with varying resource diversity at baseline and over time. Resource diversity was inclusive of personal, relational, contextual, and culturally valued resources in both the Stable Low and Declining trajectories, with emphasis on relational supports. Personal resources were emphasized in the Worsening and Chronic High trajectories, and culturally valued and contextual resources de-emphasized. In summary, resource constellations characterized by within and across system diversity and cultural responsiveness are more protective and will be key to advancing sub-Saharan adolescent mental health.

Resilience Phenotypes and Psychological Functioning among Individuals with Opioid Use Disorder

BACKGROUND

Opioid use disorder (OUD) is a heterogeneous disorder. However, there is a lack of deep phenotyping investigations focusing on important psychological constructs such as resilience that may impact OUD. The present study aimed to investigate the relationship between trait resilience and the five-factor model of personality (FFM) among individuals with opioid use disorder (OUD). We also explored whether the FFM and trait resilience form specific phenotypes associated with psychological functioning.

METHODS

This secondary analysis of an epigenetic study included participants of African ancestry (n = 72), an understudied population, who met DSM-5 criteria for OUD. Participants completed measures to assess personality traits, trait resilience, current and previous drug use, and psychological functioning (depression, anxiety, and stress).

RESULTS

Linear regression revealed a significant relationship between resilience (CD-RISC-25 score) and the FFM, R2 = 0.56, F(5,62) = 15.7, p<.001. Further, a two-cluster classification emerged as the optimal solution from the cluster analysis. Cluster 1 (n = 33, 45.8% of the sample) showed lower resilience (CD-RISC-25 score: M = 58.6, SD = 11.2) compared to Cluster 2 (n = 35, 48.6%; CD-RISC-25 score: M = 76.1, SD = 11.9). The "High-Resilience Cluster" (Cluster 2) was characterized by higher FFM traits of: Extraversion, Openness, Agreeableness, and Conscientiousness, and lower Neuroticism versus Cluster 1. Multivariate analysis of variance revealed statistically significant differences between the two resilience clusters concerning other psychological symptoms, Λ = 0.732, F(4, 50) = 7.05, p < 0.003.

CONCLUSIONS

These findings suggest associations between the FFM and trait resilience among individuals with OUD. Two distinct "resilience phenotypes" emerged, with high-resilience individuals displaying less stress, anxiety, and depressive symptoms. Results highlight the clinical importance of resilience as a potential target for intervention in people with OUD.

Embedding Research on Emotion Duration in a Network Model

Contrary to early theorizing, emotions often last for longer periods of time. Variability in people's emotion duration contributes to psychopathologies. Therefore, emotion theories need to account for this variability. So far, reviews only list predictors of emotion duration without integrating them in a theoretical framework. Mechanisms explaining why these predictors relate to emotion duration remain unknown. I propose to embed research on emotion duration in a network model of emotions and illustrate the central ideas with simulations using a formal network model. In the network model, the components of an emotion have direct causal effects on each other. According to the model, emotions last longer (a) when the components are more strongly connected or (b) when the components have higher thresholds (i.e., they are more easily activated). High connectivity prolongs emotions because components are constantly reactivated. Higher thresholds prolong emotions because components are more easily reactivated even when connectivity is lower. Indirect evidence from research on emotion coherence and research on the relationship of predictors of emotion duration with components outside of emotional episodes supports the usefulness of the network model. I further argue and show in simulations that a common cause model, in which a latent emotion causes changes in emotion components, cannot account for research on emotion duration. Finally, I describe future directions for research on emotion duration and emotion dynamics from a network perspective.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00203-3.

Do the Temperamental Characteristics of Both Mother and Child Influence the Well-Being of Adopted and Non-Adopted Children?

(1) Background: For decades, the temperaments of infants and small children have been a focus of studies in human development and been seen as a potential contributor to children’s developmental patterns. However, less is known about the interplay between the temperamental characteristics of mothers and their children in the context of explaining variations in developmental outcomes. The aim of our study was to explore the associations—with or without genetic links—of the temperaments and psychological distress of mothers and the temperaments of children with behavioral problems in a group of internationally adopted children and their adoptive mothers and in a group of non-adopted children and their mothers. (2) Methods: Data (n = 170) were derived from the ongoing Finnish Adoption (FinAdo) follow-up study. The children included were under the age of 7 years; 74 were adopted internationally through legal agencies between October 2010 and December 2016, and the remaining 96 were non-adopted children living with their birth parents (biological group) recruited from day-care centers. We used Mary Rothbart’s temperament questionnaires to assess temperament, the Child Behavior Checklist (CBCL) to obtain data on the children’s behavioral/emotional problems and competencies, and the General Health Questionnaire (GHQ) to assess parental psychological distress. The study was approved by the Ethics Committee of the Hospital District of Southwest Finland, and written informed consent was obtained from the parents and the children themselves. (3) Results: The negative affectivities of both mothers and children were associated with the total CBCL and with both internalizing and externalizing problem behaviors after adjusting for age, gender, and adoption status. Both relationships remained significant when tested simultaneously, suggesting additive effects. Maternal negative affect was associated with problem behavior irrespective of child extraversion/surgency. Child extraversion/surgency was associated with lower levels of all internalizing behavioral problems when adjusted for maternal sociability. Child negative affect was associated with all behavioral problem measures irrespective of maternal sociability or maternal psychological distress. Maternal distress was associated with child problem behaviors only in children with low extraversion/surgency. (4) Limitations: The sample size was relatively small, and the information was gathered solely with questionnaires. (5) Conclusions: The results of the study may be clinically significant. Child negative affect, maternal negative affect, and maternal experienced distress, combined with low child extraversion/surgency, may increase the risk of child problem behaviors in both adoptees and non-adoptees.

Assessment and Discussion of Correlation Among Psychological Symptoms, Occupational Strain, and Neurotic Personality for Metro Drive

Metro driver is the prime person who is responsible for metro operation safety. The mental health of a metro driver is very important for the operation of the subway and requires the driver to keep high mental alertness to monitor the surrounding environment and also handle emergencies under uncertain or dangerous conditions. After a long-term occupational strain, a metro driver is likely to suffer from some mental health problems, such as anxiety and depression, that ultimately threaten the lives of passengers. Therefore, in this study, we focus on the psychological symptoms of metro drivers from the angle of occupational strain and neuroticism. A total of 396 metro drivers from Kunming Rail Transit Operation Co., Ltd. in China were investigated through a questionnaire survey. Symptom Checklist-90 (SCL-90), Personal Strain Questionnaire (PSQ), and NEO-Five-Factor Inventory-Neuroticism Subscale (NEO-FFI-N) were applied to evaluate the psychological symptoms, occupational strain, and neuroticism in metro drivers, respectively. The surveyed data were analyzed by SPSS software. Based on the data, a path structural equation model was established to explore the correlation among occupational strain, psychological symptoms, and neuroticism. The results show that the scores for psychological symptoms and occupational strain are higher than the Chinese adult norm among metro drivers. The occupational strain, neuroticism, and psychological symptoms are all positively correlated in the metro drivers. Occupational stress has a direct influence on the psychological symptom, while neuroticism plays a partial mediation role between occupational strain and psychological symptoms. The results of this study can be applied to optimize the employee selection system and training system for metro operation companies.

Accuracy of a one-dimensional reduction of dynamical systems on networks

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)0028-083610.1038/nature16948] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

A multisystemic perspective on the temporal interplay between adolescent depression and resilience-supporting individual and social resources

BACKGROUND

Adolescent depression can severely interfere with age-appropriate and lifetime development and functionality. Physical functioning in daily life (as a means of individual support) as well as social support from family and friends have been shown to enhance adolescent resilience against depression. By applying a multisystemic model of resilience, this study investigates how these resources reciprocally influence each other and how they interact with depression over time.

METHODS

Longitudinal data (three assessments over three years) from the Resilient Youth in Stressed Environments project was analyzed using panel network analysis to investigate short- and long-term uni- and bidirectional effects. The sample consisted of N = 500 adolescents from Canada (mean age = 18.49, SD = 3.01, 56.40% young women) at the first assessment.

RESULTS

Depression seemed to fluctuate, while the resources showed significant stability over the course of the study. Perceived family and friend support were not significantly influenced by depression while they had significantly negative temporal effects on depression. Only physical functioning showed a negative feedback loop, notably with somatic symptoms of depression. Family and friend support shared a reinforcing feedback loop, while physical functioning was not related to either type of support.

LIMITATIONS

Future studies should address the low average of depressive symptomatology and subjective, global measures of social support.

CONCLUSIONS

The resources show potential time-dependent effects: symptom-specific resources need to be applied in the short-term, while a pool of multisystemic resilience resources seems necessary over the long-term to increase the resilience to depression among adolescents.

Psychopathological networks: Theory, methods and practice

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Developing our understanding of nutrition in depression

Research to date has convincingly demonstrated that nutrition impacts depression. Population-based studies have shown that diet, food types, dietary supplements, gut bacteria, endocrine systems and obesity all play a role in depression. While nutrition could provide an important therapeutic opportunity in depression, clinical trials have not shown clinically meaningful results, and it appears unlikely that nutrition is a central determinant of depression. Conversely, however, prior research is inconclusive to inferring that nutrition does not have a clinically significant effect. This would require elucidating precisely when nutrition affects depression which necessitates an alternative, more granular, model for the nutrition–depression interaction. The network theory of mental disorders, which studies how mental disorders arise from a causally related network of symptoms and external factors, is proposed as an alternative model for understanding the complexity of the nutrition–depression link. This approach would uncover which relationships, between aspects of nutrition and depression symptoms, warrant further study at a population and laboratory level. Furthermore, from within nutrition science, is a movement dubbed ‘New Nutrition Science’ (NNS) that aims to integrate biological, social and environmental determinants of nutrition. NNS is important to nutrition–depression research which has yet to reveal how social factors impact the nutrition–depression interaction. Network theory methodology is fully compatible with the network modelling already used in NNS. Embracing both network theory and NNS in future research will develop a full and complex understanding of nutrition in depression.

[Student's mental health: better measurement and considereration of the challenges]

Mental health represents a high cost for society, estimated at 109 billion euros per year in France, 80 % of which are indirect costs. Mental diseases start before the age of 24 for 75 % of patients. Students, whose age is predominantly between 18 and 25, are a particularly high-risk population and for whom mental illness can have very serious consequences. The Observatoire de la vie Etudiante surveys from 2016 found a 12 month prevalence of depression of 15 % with suicidal thoughts in 8 % of the students higher than what is observed in the French general population, respectively 10 % and 5 %. The confinement itself and its consequences both economic (unemployment, difficulty for young people to find a job…) and social (isolation) could have a very bad effect on their mental health. The survey made by a governmental organization (Santé Publique France) revealed a significant increase in the prevalence of anxiety disorders at the start of confinement in the general population. This prevalence decreased during confinement but remained significantly higher than in 2017. Economic simulated data indicate that prevention in mental health could not only be effective but also profitable. In France, reimbursement by national health insurance of 12 sessions of psychologists for young people (between 11 to 21) is being tested in order to further and widely implement psychological prevention strategies rather than relying on already widely reimbursed pharmacological treatments. There are, however, several issues to discuss. First of all, is the need to define what psychopathology is considered to be. Then, it should be understood how measures of these concepts are created, how tools are constructed and how they operate in their environment. For depression, many different scales exist and even if the most used one are taken into account, they have very little content or symptoms in common. In addition, for the same scale, many different cut-offs exist to define whether a case should be considered as pathological or not, and so it is with the period of time studied being considered that may vary, both leading to consequences such as the differences observed in prevalence. Other biases should also be considered such as the age of the participants, the gender, the size of the sample, the response rate, the method of assessment and recruitment. Finally, it is necessary to question the assumptions and models used concerning the causes of psychic pathologies. Biological hypotheses on the origin of depression involve genetics and inflammation, but sociological and psychological factors must also be considered as well as the underlying complexity both in their nature and interactions at different structural levels of space and time. We should avoid drifting towards a biological or a sociological reductionism and move forward through complex systems approaches and models. With regard to student mental health in France, unfortunately, quality data are still lacking, and existing studies are difficult to compare as some may also have methodological issues. This article leads to the conclusion that there is a need for policies to assess student mental health at both local and national levels with a reflexional thinking on the tools and scales to use as measurements of these phenomena. This approach does not require being too assertive, but should have full transparency on the way the measures were designed and obtained. Measures are as much needed as are the needs for coordinated prevention and care in mental health.

The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors

Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.