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

A Network Analysis of the Fear Avoidance Model of Genital Pain

Using a novel data-driven network approach, this study aimed to examine the interconnection between the key elements of the Fear-Avoidance Model of female genital pain - sexual arousal, fear-avoidant cognitions, and motivational coping - and its associated factors to predict the intensity and frequency of genital pain across women over time. Network modeling allowed for a comprehensive evaluation of the Fear-Avoidance model while capturing the dynamic features of genital pain. We estimated a cross-sectional and a temporal, contemporaneous, and between-persons network model on convenience-based data of 543 female students (mean age = 23.7 years, SD = 3.6) collected at three time points. Results showed that lubrication, pain catastrophizing, pain avoidance, fear-avoidance beliefs, sexual satisfaction, anxiety, and frequency of coital and non-coital sex predicted pain, with lubrication being the most consistent predictor across estimations. The network of women with recurrent genital pain showed a similar pattern as the network of the total sample, except that pain avoidance and fear-avoidance beliefs rather than pain catastrophizing predicted pain directly, and frequency of coital and non-coital sexual activities played a more prominent role. These results suggest that the main problem of genital pain centers around women not being sufficiently aroused during intercourse and inadequate ways of pain coping, which are critical targets of cognitive-behavioral therapy treatment and should be developed further.

The role of environmental sensitivity in the mental health of Syrian refugee children: a multi-level analysis

Individuals with high environmental sensitivity have nervous systems that are disproportionately receptive to both the protective and imperilling aspects of the environment, suggesting their mental health is strongly context-dependent. However, there have been few consolidated attempts to examine putative markers of sensitivity, across different levels of analysis, within a single cohort of individuals with high-priority mental health needs. Here, we examine psychological (self-report), physiological (hair hormones) and genetic (polygenic scores) markers of sensitivity in a large cohort of 1591 Syrian refugee children across two waves of data. Child-caregiver dyads were recruited from informal tented settlements in Lebanon, and completed a battery of psychological instruments at baseline and follow-up (12 months apart). Univariate and multivariate Bayesian linear mixed models were used to examine a) the interrelationships between markers of sensitivity and b) the ability of sensitivity markers to predict anxiety, depression, post-traumatic stress disorder, and externalising behaviour. Self-reported sensitivity (using the Highly Sensitive Child Scale) significantly predicted a higher burden of all forms of mental illness across both waves, however, there were no significant cross-lagged pathways. Physiological and genetic markers were not stably predictive of self-reported sensitivity, and failed to similarly predict mental health outcomes. The measurement of environmental sensitivity may have significant implications for identifying and treating mental illness, especially amongst vulnerable populations, but clinical utility is currently limited to self-report assessment.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

Unique Challenges in Biomarkers for Psychotic Disorders

Biomarkers are observations that provide information about the risk of certain conditions (predictive) or their underlying mechanisms (explanatory) [...].

Elevated emotion network connectivity is associated with fluctuations in depression

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

Posttraumatic stress symptoms in healthcare workers during the COVID-19 pandemic: A four-wave longitudinal study

BACKGROUND

Posttraumatic stress symptoms of healthcare workers have become a significant public concern in the healthcare system that have long COVID-19. It is less known how the pandemic impacts the HCWs' PTSS longitudinally and long-term risk factors for it.

METHODS

Four consecutive surveys were conducted among healthcare workers in China from 2019 to 2023 COVID-19 outbreaks. Multilevel mixed-effect models were used to examine longitudinal changes and risk factors. Network analysis was utilized to explore network centrality changes in PTSS symptoms.

RESULTS

HCWs' PTSS symptoms were increased over time during the COVID-19 pandemic. Being female, being nurse, working in the emergency department, working longer hours, less frequently going back home and having COVID-19 infection are risk factors of PTSS for HCWs; unmarried is the protective factor. Significant interaction between symptom changes and profession exists. PTSS networks showed that Avoidance of thoughts, Emotional-cue activity, Exaggerated startle response and Hypervigilance were the central symptoms during four waves. The global strength of the PTSS network grows over time, and nodal strength of Avoidance of thoughts, Loss of interest and Negative beliefs increased by COVID-19.

CONCLUSION

The pandemic's impacts on healthcare workers vary by professions. PTSS symptoms exacerbate, reinforce each other, and persists with recurring waves.

Gender network analysis of the Eating Disorder Examination-Questionnaire (EDE-Q7) in Peruvian adults

Introduction

Background: network assessment of eating disorder (ED)-related symptomatology from a gender perspective is an important topic of study; however, there is limited research in the Latin American context. Objective: this study aimed to explore the patterns of association of the components of the Eating Disorder Examination-Questionnaire (EDE-Q7) according to gender, using two simultaneous network models in 890 Peruvian adults (63.51 % were women; mean age: 26.40). Methods: two graphs considering the gender factor were made using the R package qgrap and the merged LASSO graph. Results: higher network centrality measures were obtained for items related to body image dissatisfaction and overvaluation in women; while in the men's network, the items of food restriction and overestimation of weight were the most central symptoms. Conclusion: both network models were invariant and showed no significant differences in both structure and connections.

Modeling vulnerability and intervention targets in the Borderline Personality Disorder system: A network analysis of in silico and in vivo interventions

Modeling psychopathology as a complex dynamic system represents Borderline Personality Disorder (BPD) as a constellation of symptoms (e.g., nodes) that feedback and self-sustain each other shaping a network structure. Through in silico interventions, we simulated the evolution of the BPD system by manipulating: 1) the connectivity strength between nodes (i.e., vulnerability), 2) the external disturbances (i.e., stress) and 3) the predisposition of symptoms to manifest. Similarly, using network analysis we evaluated the effect of an in vivo group psychotherapy to detect the symptoms modified by the intervention. We found that a network with greater connectivity strength between nodes (more vulnerable) showed a higher number of activated symptoms than networks with less strength connectivity. We also found that increases in stress affected more vulnerable networks compared to less vulnerable ones, while decreases in stress revealed a hysteresis effect in the most strongly connected networks. The in silico intervention to symptom alleviation revealed the relevance of nodes related to difficulty in anger regulation, nodes which were also detected as impacted by the in vivo intervention. The complex systems methodology is an alternative to the common cause model with which research has approached the BPD phenomenon.

A network approach to the relationship between career adaptability and starting salary among graduates

Career adaptability is a psychological resource for individuals to deal with career events, and it is a system of human-environment interaction. The components of the career adaptability concept are not independent of each other but rather an interactive network. The present study aims to shed light on the nomological network of career adaptability and the starting salary by investigating their indicators using network analysis to reveal their structural networks and interrelationships. In addition, we compared the similarities and differences between the networks of different gender groups. Results indicate that career adaptability directly connects to starting salary for graduates, and some indicators are the core factors that influence starting salary. Besides, the global structure of gender-specific networks is very similar. However, some differences have been detected, such as becoming curious about new opportunities is the male network's core, while the core of the female network is doing the right thing.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-023-04655-5.

A Cross-Country Network Analysis of Resilience Systems in Young Adults

Multisystemic resilience has been conceptualised as involving a constellation of protective factors which operate at different levels to promote adaptation and thriving despite experiences of adversity. We used network modelling to discover how protective factors at two different systemic levels (intrapersonal strengths and social-ecological resources) interrelate, drawing on survey data from 5283 emerging adults (M = 24.53 years; 52% female) in Brazil, China, Indonesia, Russia, Thailand, the US and Vietnam. Results indicated that the level of connectivity within and between protective factor levels was similar between the countries, but that there was substantial variation in the specific interrelations among protective factors (both within and between levels), including the presence of some country-specific negative interrelations between protective factors at different levels. The findings support the importance of cultural context in studies of resilience, with implications for the development of appropriate resilience-building interventions for this age group.

Restoring the missing person to personalized medicine and precision psychiatry

Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological "omics" data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.

Plasticity in mental health: A network theory

Plasticity is the ability to modify brain and behavior, ultimately promoting an amplification of the impact of the context on the individual's mental health. Thus, plasticity is not beneficial per se but its value depends on contextual factors, such as the quality of the living environment. High plasticity is beneficial in a favorable environment, but can be detrimental in adverse conditions, while the opposite applies to low plasticity. Resilience and vulnerability are not univocally associated to high or low plasticity. Consequently, individuals should undergo different preventive and therapeutic strategies according to their plasticity levels and living conditions. Here, an operationalization of plasticity relying on network theory is proposed: the strength of the connection among the network elements defining the individual, such as its symptoms, is a measure of plasticity. This theoretical framework represents a promising tool to investigate research questions related to changes in neural structure and activity and in behavior, and to improve therapeutic strategies for psychiatric disorders, such as major depression.

Network Analysis of Depressive Symptomatology in Underweight and Obese Adults

BACKGROUND

Depressive symptoms can affect people's quality of life and social environment. In addition, in severe situations, they can lead to suicidal behaviors.

OBJECTIVE

This study aimed to analyze the differences in depressive symptoms in underweight and obese Peruvian adults.

METHODS

A cross-sectional study was carried out based on secondary data obtained from the Instituto Nacional de Estadística e Informática (INEI), Lima, Peru. A sample of 10 053 participants was considered, of which 55.96% were women. Two Gaussian plot models were estimated and the levels of depressive symptomatology were compared between the 2 groups (adults with underweight and obese).

RESULTS

A total of 1510 (15.02%) were underweight adults and 8543 (84.98%) were obese adults. There were differences in the reporting of depressive symptoms in the underweight group; the most central items were "Depressed mood" (PH2), "Tiredness/low energy" (PH4), and "Psychomotor difficulties" (PH8).

CONCLUSION

This study provides new evidence on the dynamic relationship between depressive symptoms according to the body mass index categories (underweight and obese) assessed.

Cognitive Variables in Social Anxiety Disorder in Children and Adolescents: A Network Analysis

Clark and Wells' prominent model of social anxiety disorder (SAD) assumes that cognitive variables such as negative expectations or dysfunctional cognitions play a central role in the symptomatology of SAD. In contrast to adults, it is less clear how well the cognitive model can be applied to children and adolescents. A network analysis with seven nodes was conducted to explore the importance of cognitive variables and their interaction with symptoms of SAD based on N = 205 children and adolescents (8-18 years, M = 11.54 years). Cognitive variables had a high but differential impact within the positively connected network of SAD. Dysfunctional cognitions were most strongly connected within the network. Dysfunctional cognitions, as predicted by Clark and Wells' model, seem to act as a hub affecting several symptoms. The association between negative expectations and avoidance indicates that negative expectations may particularly contribute to the maintenance of SAD.

El análisis de redes en la investigación clínica

En el número 1 del volumen 69 de la presente revista se publicó un estudio que evaluó los niveles del burnout en residentes de medicina en Colombia y que evidenció el impacto que puede causar este síndrome en quienes lo padecen.1 El burnout es una respuesta a los problemas emocionales e interpersonales que se presentan en el trabajo y en la cual intervienen sentimientos de agotamiento, actitud indiferente, percepción de incompetencia ante la falta de recursos para afrontar las responsabilidades, insatisfacción y baja autoestima.