Modeling Psychological Attributes in Psychology - An Epistemological Discussion: Network Analysis vs. Latent Variables

Most influential symptoms in the early detection of clinical high risk for psychosis in Indonesia: A basic symptom network

Basic symptoms (BS) are subtle, self-experienced disturbances in mental processes. Cognitive, visual and acoustic BS were included in clinical-high risk criteria for psychosis (CHR-P). So far, their interplay has been studied only in Western samples and always in concert with other symptoms, such as (attenuated) psychotic symptoms. Thus, we studies the interrelation of the 56 BS assessed with the 'Schizophrenia Proneness Instrument, Adult version', in an Indonesian sample of CHR-P patients (N = 108) 16-30 years-of-age, 18.95 % male) according to BS criteria using network analysis. The used estimator partially correlates with the Gaussian graphical model (GGM) and graph module. Model selection uses regularization techniques by setting λ = 0.05. The most central and traversed nodes in the robust, stable network were visual disturbances-'near- and tele-vision', 'disturbances of the perception of straight lines/contours' and 'micropsia, macropsia'. Visual disturbances were also predominately involved in the 16 strongest edges, thereby linking strongly with alienation experiences. Contrary to networks in Western samples in that cognitive BS played a central and important role, subjective visual disturbances seem to play an important role in the possible emergence and maintenance of BS, in particular of alienation experiences, in Indonesian patients with CHR-P. Future studies should therefore study this potential role longitudinally along with their neurobio/cognitive underpinnings. Furthermore, it should be studies, if cognitive-behavioral therapy as suggested for alienation experiences may prevent progression and reduce symptom load.

Cross-cultural insights into internet addiction and mental health: a network analysis from China and Malawi

BACKGROUND

Internet addiction has emerged as a significant mental health issue among university students. The study aimed to compare the network structures of Internet addiction and mental health symptoms among university students in China and Malawi, which provide insights into culturally sensitive prevention and intervention strategies.

METHODS

Network analysis was used on two datasets: Malawi (n = 688) and China (n = 975) using the Internet Addiction Test and the Self-Reporting Questionnaire. Network estimation, centrality, bridge estimation and network comparison, were conducted using R software.

RESULTS

The prevalence of internet addiction and mental health was significantly higher in China than Malawi. In Malawi, the strongest edges were "school work" - "job performance" and "worthless" - "unable to play a useful part". The central nodes were "daily work suffering", "fantasize" and "lost interest". In China, the strongest edges were "neglect household" - "neglect partner" and "difficult to enjoy" - "daily work suffering". The central nodes were "trouble thinking", "unhappiness", and "unable to play a useful part". Bridge symptoms varied between the countries, with functional impairments (school work, daily work suffering and difficult to make decision) being more prominent in Malawi and emotional disturbances (daily work suffering, act annoyed and difficult to enjoy) in China.

CONCLUSIONS

The study highlights significant differences in the network structures of internet addiction and mental health between China and Malawi, which underscore the importance of culturally sensitive mental health interventions. In Malawi, addressing functional impairments related to academic and work performance is crucial, while in China, interventions should focus on emotional and relational aspects.

The Relationship Between Cognitive Emotion Regulation Strategy and Mental Health Among University Students During Public Health Emergency: A Network Analysis

Background

Public health emergencies pose threats to mental health, and cognitive emotional regulation can be a crucial coping strategy. This study explored the relationship between cognitive emotion regulation strategies and mental health among university students during the COVID-19 pandemic using network analysis.

Methods

1100 university students completed questionnaires assessing depression, anxiety, somatization, and cognitive emotion regulation strategies. Network analysis was conducted to identify network structures and bridge symptoms.

Results

(1) In the depression network, the strongest edge is D1 (Little interest)-D2 (Feeling down), while D2 emerged as the node with the highest centrality. C1 (Self-blame), C8 (Catastrophizing), D6 (Feeling bad), and D9 (Suicide) are bridge symptoms. (2) In the anxiety network, A2 (Uncontrollable worrying)-A3 (Worrying too much) were identified as the strongest edge, and A2 exhibiting the highest centrality. C1 (Self-blame), C8 (Catastrophizing), and A6 (Easy annoyance) are bridge symptoms. (3) In the somatization network, the strongest edge is S14 (Fatigue)-S15 (Sleep disturbances) and S9 (Palpitations) exhibited the highest centrality. C1 (Self-blame), C3 (Rumination), C8 (Catastrophizing), S9 (Palpitations), and S14 (Fatigue) are bridge symptoms.

Conclusion

Self-blame and catastrophizing are important bridge symptoms for cognitive emotion regulation strategies and mental health networks, so cognitive behavioral therapy, focusing on self-blame and catastrophizing as intervention targets, could most effectively improve mental health during public health emergencies.

Reading tea leaves or tracking true constructs? An assessment of personality-based latent profiles in eating disorders

Background

Eating disorder (ED) subtyping studies have often extracted an undercontrolled, an overcontrolled and a resilient profile based on trait impulsivity and perfectionism. However, the extent to which methodological choices impact the coherence and distinctness of resulting subtypes remains unclear.

Objective

In this paper, we aimed to assess the robustness of these findings by extracting personality-based subtypes on a sample of ED patients (N = 221) under different analytic conditions.

Methods

We ran four latent profile analyses (LPA), varying the extent to which we constrained variances and covariances during model parametrization. We then performed a comparative analysis also including state ED symptom measures as indicators. Finally, we used cross-method validation via k-means clustering to further assess the robustness of our profiles.

Results

Our results demonstrated a four-profile model based on variances in impulsivity and perfectionism to fit the data well. Across model solutions, the profiles with the most and least state and trait disturbances were replicated most stably, while more nuanced variations in trait variables resulted in less consistent profiles. Inclusion of ED symptoms as indicator variables increased subtype differentiation and similarity across profiles. Validation cluster analyses aligned most with more restrictive LPA models.

Conclusion

These results suggest that ED subtypes track true constructs, since subtypes emerged method-independently. We found analytic methods to constrain the theoretical and practical conclusions that can be drawn. This underscores the importance of objective-driven analytic design and highlights its relevance in applying research findings in clinical practice.

Psychometric validity of the sum score of the Sniffin' Sticks-Extended Test

A common tool to measure olfactory function is the Sniffin' Sticks Test extended version (SSET). The SSET evaluates olfactory ability by summing the scores of three subtests: Threshold, Discrimination, and Identification. Recent meta-scientific literature revealed that many psychometric instruments currently in use have not been adequately validated, leading to a measurement crisis that raises concerns about the validity of the conclusions drawn with these instruments. Two examples of the measurement crisis are (i) the use of sum scores without testing their assumptions (e.g. unidimensionality and tau-equivalence), which indicate that all subtests have the same, stable relationship with their underlying construct, and (ii) the lack of assessment of measurement invariance across groups. Here, we aim to investigate the unidimensionality and tau-equivalence assumptions, internal consistency, and measurement invariance of sex and age groups of the SSET. We tested 988 (555 females, mean ± SD: 39.75 ± 18.60 years) participants with the Italian version of the SSET. The tau-equivalent model demonstrated excellent fit indices (CFI robust = 1, TLI robust = 1, RMSEA robust = 0, SRMR = 0.013), which best explain the data, indicating that all subtests are equally important in measuring olfactory function, but not necessarily equally precise. The results also revealed full measurement invariance across age groups and configural, partial metric, and scalar invariance across sexes, indicating that the use of latent means to compare sex groups should be chosen over raw scores. However, the SSET demonstrated moderate internal consistency. Future studies should clarify whether the reliability of the SSET can be increased.

Maintenance in relationships, satisfaction, jealousy, and violence in young couples: a network analysis

BACKGROUND

The study explores the associations among Relationship Maintenance, Satisfaction, Jealousy, and Violence in young Peruvian couples, particularly in a post-pandemic context, using a network analysis.

METHODS

Eight hundred thirty-two participants aged 18-30 (M = 20.94, SD = 2.29), with 645 females (77.50%) and 187 males (22.50%), were involved. The study aimed to discern relationships among network nodes, emphasizing the link between Relationship Maintenance dimensions and Jealousy and Violence. The research also sought the central node in the network and examined gender-specific node connections, using the SMOTE algorithm for gender data balance.

RESULTS

Findings revealed a direct connection between Complementarity and Jealousy, implying intense shared interests can lead to unhealthy dependence. An inverse relationship was seen between Companionship and Violence. Satisfaction was pivotal, showcasing its importance in romantic relationship success. Additionally, the study shows men prioritize Companionship and Sharing, possibly due to cultural norms, while women focus on the Companionship-Complementarity bond, indicating mutual support.

CONCLUSIONS

The research emphasizes the critical role of maintenance variables in determining Satisfaction, Jealousy, and Violence in relationships. The pandemic's influence on romantic dynamics is evident, emphasizing the importance of Satisfaction. Future studies should focus on gender equity and further explore these relationships.

The impact of childhood trauma on perceived stress and personality in patients with obsessive-compulsive disorder: A cross-sectional network analysis

OBJECTIVE

Little is known about the role of childhood experiences in the development of obsessive-compulsive disorder (OCD). However, the influence of childhood experiences on personality, behavior, and perceived stress may vary between OCD patients and healthy individuals. The objective of this study was to use network analysis to explore the relationship between childhood trauma, personality, perceived stress, and symptom dimensions, thus finding the difference between patients' and healthy people's network.

METHODS

488 patients with OCD and 210 healthy volunteers were recruited. All of them were assessed with the Obsessive-Compulsive Inventory - Revised (OCI-R), the Perceived Stress Scale-10, the NEO Five-Factor Inventory and the Early Trauma Inventory Self-Report Short Form. Network analysis was conducted and the centrality indices were calculated. Network comparison test was performed.

RESULTS

In patients' network, the Obsession and the Ordering behavior were the most important nodes among the OCI-R. The perceived stress showed the strongest strength centrality of all nodes and positive correlation with the Obsession and Neuroticism. Network comparison test results indicated a statistically significant difference between network structure, and post-hoc analysis found five edges significantly differed between patients and healthy controls, mainly on Obsession and Washing behaviors.

CONCLUSIONS

Emotional abuse was considered significant in both networks due to its higher strength centrality. Meanwhile, perceived stress was found to be more significant in the patient network and exhibited stronger associations with obsession. The obsessive thoughts and washing behavior were different among patients and healthy controls, which brought new understanding to the pathopsychological mechanisms of OCD.

A Network Analysis Approach on the Psychological Flexibility/Inflexibility Model

Acceptance and Commitment Therapy (ACT) is purported to work via targeting six interrelated processes summarized as the Psychological Inflexibility/Psychological Flexibility (PI/PF) model. However, the theoretical structure and interconnections of this model have not been sufficiently explored. Lacking are examinations of the interrelations among its components. Network Analysis (NA) can model PI/PF as a system of interconnected variables. We aimed at exploring the role and associations of the PI/PF model's components using NA in two different samples and sets of scales, and compare its structure across sub-samples. Sample 1 consisted of 501 individuals, who completed an online battery of questionnaires including the Multidimensional Psychological Flexibility Inventory, and Sample 2 consisted of 428 people, who completed an online set of six ACT measures, each assessing a component of the PI/PF model. NA could not verify the six ACT dimensions as distinct components. Values and Committed Action components were found to be strongly associated and combined in a group in both sets of measures and samples. Interestingly, Acceptance and Defusion were not the most central components as purported in some ACT conceptualizations, whereas Self-as-Context had a key role on both sets of measures and its items were often merged with Present Moment Awareness items. No significant differences were found in the comparison of networks across different subsamples and sets of scales. After combining different sets of scales, the six ACT components could not be completely verified as distinct entities, which might reflect problems with the theoretical model, or with the scales used. All components had critical roles in the model, particularly Self-as-Context, which reflects the need to redirect research towards this understudied construct. Findings point towards considerations of a triflex instead of a hexaflex ACT model.

Love, jealousy, satisfaction and violence in young couples: A network analysis

In recent years, couples have been affected by health measures related to COVID-19, a circumstance that forces us to examine couple interactions in terms of crucial variables of their functioning. In this sense, the present study aimed to examine the association between love, jealousy, satisfaction, and violence in young couples through network analysis. A total of 834 young people and adults between 18 and 38 years of age (Mean = 20.97, SD = 2.39) participated; 646 women (77.50%) and 188 men (22.50%), who completed the Sternberg's love scale (STLS-R), Brief Jealousy Scale (BJS), Relationship Assessment Scale (RAS) and Woman Abuse Screening Tool (WAST-2). A partial unregularized network was estimated using the ggmModSelect function. The Bridge Strength index was calculated because the aim was to identify the bridge nodes between the variables under study. The results reveal that two nodes of the love variable, Commitment, and Intimacy, had a direct and moderate relationship with the Satisfaction node. The latter is the central node in the network. However, in the male group, the most intense associations are in Satisfaction-Intimacy, Violence-Passion, Jealousy-Commitment. It is concluded that there are relevant connections between the nodes of the network, which invite further research on couple relationships after the COVID-19 pandemic.

Modeling urgency in the lab: Exploring the associations between self-reported urgency and behavioral responses to negative outcomes in laboratory gambling

Impulsivity is a multifaceted construct that relates to different behaviors in everyday life and has been associated with many psychopathological disorders and behavioral problems, such as problematic gambling behavior. One questionnaire to measure these several facets on a trait level is the UPPS-P Impulsive Behavior Scale. Specifically, the UPPS-P investigates five distinct facets: (a) negative urgency, (b) lack of premeditation, (c) lack of perseverance, (d) sensation seeking, and (e) positive urgency. Negative urgency at a trait level in particular seems to be associated with the development of psychopathological disorders. To date, there are no established state measures of negative urgency. However, it was recently proposed that speeding after losses might be a suitable measure. Thus, in this study, we explored the possible relationship between a state measure of negative urgency modeled through a behavioral gambling task and a trait measure of negative urgency through the UPPS-P questionnaire. We used correlational and network analyses in an aggregated database of eight samples (total N = 1216) to explore the potential relationships between post-loss speeding on the behavioral gambling task and UPPS-P scores (by combining trait vs. item-based analyses). We found that the degree of speeding after losses (post-loss speeding) did not correlate with the trait measure of impulsivity in general and negative urgency specifically, either at the latent trait level or on an item-based level. This null finding indicates that our state measure of post-loss speeding and negative urgency on a trait level does not seem to capture the same underlying constructs. Implications for personality research are discussed.

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.

The backbone symptoms of depression: a network analysis after the initial wave of the COVID-19 pandemic in Macao

Background

The coronavirus disease 2019 (COVID-19) pandemic disrupted the working lives of Macau residents, possibly leading to mental health issues such as depression. The pandemic served as the context for this investigation of the network structure of depressive symptoms in a community sample. This study aimed to identify the backbone symptoms of depression and to propose an intervention target.

Methods

This study recruited a convenience sample of 975 Macao residents between 20th August and 9th November 2020. In an electronic survey, depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9). Symptom relationships and centrality indices were identified using directed and undirected network estimation methods. The undirected network was constructed using the extended Bayesian information criterion (EBIC) model, and the directed network was constructed using the Triangulated Maximally Filtered Graph (TMFG) method. The stability of the centrality indices was evaluated by a case-dropping bootstrap procedure. Wilcoxon signed rank tests of the centrality indices were used to assess whether the network structure was invariant between age and gender groups.

Results

Loss of energy, psychomotor problems, and guilt feelings were the symptoms with the highest centrality indices, indicating that these three symptoms were backbone symptoms of depression. The directed graph showed that loss of energy had the highest number of outward projections to other symptoms. The network structure remained stable after randomly dropping 50% of the study sample, and the network structure was invariant by age and gender groups.

Conclusion

Loss of energy, psychomotor problems and guilt feelings constituted the three backbone symptoms during the pandemic. Based on centrality and relative influence, loss of energy could be targeted by increasing opportunities for physical activity.

Exploring the conceptual framework and measurement model of intrinsic capacity defined by the World Health Organization: A scoping review

WHO has defined intrinsic capacity (IC) as the composite of all physical and mental capacities of an individual covering five subdomains: cognition, locomotion, sensory, vitality, and psychological. Despite this well accepted definition, the conceptual and measurement model of IC remains unclear, which hampers a standardized operationalization of the construct. We performed a scoping review to give a comprehensive overview of the extent to which the current literature of IC addresses and assumes the conceptual framework and measurement model of IC as reflective or formative. For inclusion, we considered all types of articles that were published in peer-reviewed journals except for protocol articles. A systematic search of 6 databases from different disciplines led to the inclusion of 31 papers. We found inconsistency and gaps in the descriptions of IC. Most of the papers did not define the measurement model. In the conceptual background and validation articles, we identified descriptions of both reflective and formative measurement models while in empirical studies applying IC measurements the underlying assumptions remained mainly unclear. Defining a measurement model is not merely a theoretical matter but influences the operationalization and validation processes of the construct. This study raised questions about the most fundamental features of the IC construct and discusses whether IC should be considered as an underlying latent trait of all capacities (reflective construct) or an aggregate summary measure of the subdomain capacities (formative construct).

A network approach can improve eating disorder conceptualization and treatment

Eating disorders are severe mental illnesses with the second highest mortality rate of all psychiatric illnesses. Eating disorders are exceedingly deadly because of their complexity. Specifically, eating disorders are highly comorbid with other psychiatric illnesses (up to 95% of individuals with an eating disorder have at least one additional psychiatric illness), have extremely heterogeneous presentations, and individuals often migrate from one specific eating disorder diagnosis to another. In this Perspective, we propose that understanding eating disorder comorbidity and heterogeneity via a network theory approach offers substantial benefits for both conceptualization and treatment. Such a conceptualization, strongly based on theory, can identify specific pathways that maintain psychiatric comorbidity, how diagnoses vary across individuals, and how specific symptoms and comorbidities maintain illness for one individual, thereby paving the way for personalized treatment.

Exploring the Relations Among Teachers' Epistemic Theories, Work Engagement, Burnout and the Contemporary Challenges of the Teacher Profession

Current educational reforms concerning curricula and digitalization challenge educators to meet new demands for learning and schooling. What is common for current educational reforms is that they tend to emphasize competencies that are not related to the traditional subject-matters and reflect a stance which presents learning as a naturally reflective and collaborative act. It is often assumed that teachers are automatically ready to implement ideas of this kind in practice. In this study, we propose that teachers' theories about knowledge, knowing and learning, particularly their epistemic theories, may be related to how teachers approach these reforms which challenge their previous ways of working and how they perceive their wellbeing at work. To examine these matters, we explored the dynamic interrelations between teachers' epistemic theories, conformity with the novel curricular and digital reforms (ideas behind the new curriculum and digitalization program), perceptions of the school leadership, work engagement and burnout. Participants (Study 1 n = 228; Study 2 n = 200) were Finnish class teachers and subject-matter teachers. Both data sets were collected before the COVID-19 pandemic. For data analysis, we plotted correlation network figures. Results showed that if teachers' epistemic theory was in harmony with the curricular or digital reforms, there is a positive association with work engagement and negative association with burnout. In sum, results of this provided a hint of the phenomenon suggesting that teachers' epistemic theories may be a factor which buffers teachers to meet the current epistemic and developmental challenges of teachers' profession, and furthermore, serve as grounds for a positive association for teachers to feel adequate and satisfied in their work.

Predicting Academic Performance: Analysis of Students' Mental Health Condition from Social Media Interactions

Social media have become an indispensable part of peoples' daily lives. Research suggests that interactions on social media partly exhibit individuals' personality, sentiment, and behavior. In this study, we examine the association between students' mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students' psychological attributes and mental health issues will be predicted from their social media interactions. Then, students' academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students' Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users' social media usage and their psychological attributes and mental health status and (ii) users' psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students' Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.

An Exploratory Analysis of the Internal Structure of Test Through a Multimethods Exploratory Approach of the ASQ:SE in Brazil

Background  A wide range of exploratory methods is available in psychometrics as means of gathering insight on existing data and on the process of establishing the number and nature of an internal structure factor of a test. Exploratory factor analysis (EFA) and principal component analysis (PCA) remain well-established techniques despite their different theoretical perspectives. Network analysis (NA) has recently gained popularity together with such algorithms as the Next Eigenvalue Sufficiency Test. These analyses link statistics and psychology, but their results tend to vary, leading to an open methodological debate on statistical assumptions of psychometric analyses and the extent to which results that are generated with these analyses align with the theoretical basis that underlies an instrument. The current study uses a previously published data set from the Ages & Stages Questionnaires: Social-Emotional to explore, show, and discuss several exploratory analyses of its internal structure. To a lesser degree, this study furthers the ongoing debate on the interface between theoretical and methodological perspectives in psychometrics.

Methods  From a sample of 22,331 sixty-month-old children, 500 participants were randomly selected. Pearson and polychoric correlation matrices were compared and used as inputs in the psychometric analyses. The number of factors was determined via well-known rules of thumb, including the parallel analysis and the Hull method. Multidimensional solutions were rotated via oblique methods. R and Factor software were used, the codes for which are publicly available at https://luisfca.shinyapps.io/psychometrics_asq_se/ .

Results  Solutions from one to eight dimensions were suggested. Polychoric correlation overcame Pearson correlation, but nonconvergence issues were detected. The Hull method achieved a unidimensional structure. PCA and EFA achieved similar results. Conversely, six clusters were suggested via NA.

Conclusion  The statistical outcomes for determining the factor structure of an assessment diverged, varying from one to eight domains, which allowed for different interpretations of the results. Methodological implications are further discussed.

Network neuropsychology: The map and the territory

In "network neuropsychology", network modelling and graph theory is applied to the neuropsychological test scores of patients with neurological disorders to investigate cognitive functioning. This review identifies the emerging literature on several disorders before focusing on the assumptions about cognition underlying the studies; specifically, that cognition can be thought of as a network of interrelated variables and that changes in these interrelationships, or cognitive rearrangement, can occur in neurological disorders. Next the review appraises how well network models can provide a "map" of this cognitive "territory". In particular, the review considers the lack of correspondence between the variables and properties of network models and cognitive functioning. The challenges of explicitly accounting for latent cognitive constructs and making inferences about cognition based on associative, as opposed to dissociative, methods are also discussed. It is concluded that the validity of network neuropsychological models is yet to be established and that cognitive theory and experiments, as well as network models, are needed to develop and interpret better maps.

Symptom network analysis of anxiety and depression in epilepsy

PURPOSE

Anxiety symptoms and depressive symptoms are frequent in PWE and associated with poorer outcomes. Investigation of specific characteristics of anxiety and depressive symptoms in PWE is of interest.

METHODS

We used psychometric analyses in symptom networks using screening tools validated in PWE: Generalized Anxiety Disorder 7 (GAD-7) and Neurological Disorders Depression Inventory for Epilepsy (NDDI-E). We conducted an estimation to build the NDDIE and GAD-7 network, analyzed network inferences (especially centrality measures) and performed network robustness analyses as well as modularity-based community detection.

RESULTS

145 PWE were included. The criteria with highest centrality was "Trouble relaxing" (G4) from the GAD-7, which represents a possible substantial symptom at the interface of anxiety and depressive comorbidities in epilepsy. Robustness was very moderate, despite results consistent with the literature. The two communities of nodes corresponded to criteria of the two scales.

CONCLUSION

Epilepsy is a unique model for studying psychiatric symptoms since correlation with cerebral mechanisms can be assessed. "Trouble relaxing" as a key symptom is of interest, since this relates to the "Arousal" construct of the RDoC. Limitations of this study are the number of patients, single population, limits of psychometric analysis and network analysis, and a moderate robustness. Nevertheless, arousal is linked to seizure control, and thus these observations are of relevance to future investigation of pathophysiological mechanisms of psychopathology in epilepsy.

Why do sleep disorders belong to mental disorder classifications? A network analysis of the "Sleep-Wake Disorders" section of the DSM-5

This article proposes to investigate how Sleep disorders have been conceptualized within the DSM-5 through symptom network analysis of the diagnostic criteria of the "Sleep-Wake Disorders" section in the DSM-5. We hypothesize that the analysis of the most central symptoms will allow us to better analyze the position of Sleep disorders in Mental disorders. We thus i) extracted the symptoms of the DSM-5 diagnostic criteria of Sleep-Wake disorders, ii) built the Sleep-Wake disorder DSM-5 network representation, and iii) quantified its structure at local and global levels using classical symptom network analysis. Thirty-four different symptoms were identified among the 53 DSM-5 diagnostic criteria of the 9 main disorders of the "Sleep-Wake Disorders" section. The symptom network structure of this section showed that the most central sleep symptoms are "Daytime Sleepiness", the Insomnia symptoms group ("Insomnia initiating", "Insomnia maintaining" and "Non-restorative sleep"), and Behavioral sleep symptoms (such as "Altered oniric activity", "Ambulation", "Abnormal responsiveness"). This network analysis shown that the belonging of Sleep-Wake disorders in the DSM-5 have been associated with central sleep symptoms considered as "Mental", given their phenomenality (qualitative nature of the experience) and subjectivity (in personal mental lives). Such a symptom network analysis can serve as an organizing framework to better understand the complexity of Sleep-Wake disorders by promoting research to connect the architecture of the symptom network to relevant biological, psychological and sociocultural factors.

On the equivalency of factor and network loadings

Recent research has demonstrated that the network measure node strength or sum of a node's connections is roughly equivalent to confirmatory factor analysis (CFA) loadings. A key finding of this research is that node strength represents a combination of different latent causes. In the present research, we sought to circumvent this issue by formulating a network equivalent of factor loadings, which we call network loadings. In two simulations, we evaluated whether these network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the simulated factor loading matrix of factor models. Our findings suggest that the network loadings can effectively do both. In addition, we leveraged the second simulation to derive effect size guidelines for network loadings. In a third simulation, we evaluated the similarities and differences between factor and network loadings when the data were generated from random, factor, and network models. We found sufficient differences between the loadings, which allowed us to develop an algorithm to predict the data generating model called the Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the data generating model. In sum, our results suggest that network loadings can provide similar information to factor loadings when the data are generated from a factor model and therefore can be used in a similar way (e.g., item selection, measurement invariance, factor scores).

Investigating the Dimensionality of Early Numeracy Using the Bifactor Exploratory Structural Equation Modeling Framework

The few studies that have analyzed the factorial structure of early number skills have mainly used confirmatory factor analysis (CFA) and have yielded inconsistent results, since early numeracy is considered to be unidimensional, multidimensional or even underpinned by a general factor. Recently, the bifactor exploratory structural equation modeling (bifactor-ESEM)-which has been proposed as a way to overcome the shortcomings of both the CFA and the exploratory structural equation modeling (ESEM)-proved to be valuable to account for the multidimensionality and the hierarchical nature of several psychological constructs. The present study is the first to investigate the dimensionality of early number skills measurement through the application of the bifactor-ESEM framework. Using data from 644 prekindergarten and kindergarten children (4 to 6 years old), several competing models were contrasted: the one-factor CFA model; the independent cluster model (ICM-CFA); the exploratory structural equation modeling (ESEM); and their bifactor counterpart (bifactor-CFA and bifactor-ESEM, respectively). Results indicated acceptable fit indexes for the one-factor CFA and the ICM-CFA models and excellent fit for the others. Among these, the bifactor-ESEM with one general factor and three specific factors (Counting, Relations, Arithmetic) not only showed the best model fit, but also the best coherent factor loadings structure and full measurement invariance across gender. The bifactor-ESEM appears relevant to help disentangle and account for general and specific factors of early numerical ability. While early numerical ability appears to be mainly underpinned by a general factor whose exact nature still has to be determined, this study highlights that specific latent dimensions with substantive value also exist. Identifying these specific facets is important in order to increase quality of early numerical ability measurement, predictive validity, and for practical implications.

Identifying the Inter-Domain Relations Among ODD, CD, and CU Traits in Preschool Children Using Network Analysis

Oppositional defiant disorder (ODD) and conduct disorder (CD) are two of the most common forms of disruptive behavior disorders during childhood. Callous-unemotional (CU) traits are an important factor in understanding the presentation of these externalizing forms of psychopathology. ODD, CD, and CU traits are highly related constructs, yet little work has examined how these externalizing forms of psychopathology are related at the domain level. The current study utilized network analysis to identify the distinct domains that explain the associations among ODD, CD, and CU traits. In a sample of 104 preschoolers (M age = 4.76), results demonstrated that the symptom domains of (a) angry/irritable mood and argumentative/defiant behavior, (b) aggression to people and animals, and (c) callousness were the primary bridge domains for ODD, CD, and CU traits, respectively. Findings of the current study offer a more nuanced insight into the relations amongst ODD, CD, and CU traits. Identification of bridge symptom domains can inform intervention practices by targeting specific symptom domains that are contributing to the maintenance of such aggressive and disruptive behaviors.

Positive and negative urgency as a single coherent construct: Evidence from a large-scale network analysis in clinical and non-clinical samples

Aims

Negative and positive urgency are emotion‐related impulsivity traits that are thought to be transdiagnostic factors in psychopathology. However, it has recently been claimed that these two traits are closely related to each other and that considering them separately might have limited conceptual and methodological value. The present study aimed to examine whether positive and negative urgency constructs constitute separate impulsivity traits.

Methods

In contrast to previous studies that have used latent variable approaches, this study employed an item‐based network analysis conducted in two different samples: a large sample of non‐clinical participants (N = 18,568) and a sample of clinical participants with psychiatric disorders (N = 385).

Results

The network analysis demonstrated that items denoting both positive and negative urgency cohere as a single cluster of items termed “general urgency” in both clinical and non‐clinical samples, thereby suggesting that differentiating positive and negative urgency as separate constructs is not necessary.

Conclusion

These findings have important implications for the conceptualization and assessment of urgency and, more broadly, for future research on impulsivity, personality, and psychopathology.

Coping with stress and personality: A study in pregnancies complicated by hypertension

OBJECTIVE

The objective of this work is to investigate the role of personality in pregnancies complicated by hypertension, thru analysis of structure and associations between negative affect and coping strategies, and their role towards psychological distress.

METHOD

A cross-sectional study with 343 women, where 192 pregnancies complicated by hypertension, was carried out by employing the following tools: the five-factor model (Big Five), Depression, Anxiety and Stress Scale (DASS-21), and Jalowiec's Coping Inventory. Two complementary strategies were carried out: an exploratory approach on the interactions between latent variables and a confirmatory technique.

RESULTS

Coping strategies seem to be dissociated in the hypertensive group, and these participants tend to use strategies according to their personality, mostly emotion focused, extraversion, and neuroticism. The extraversion model exclusively shows an acceptable goodness-of-fit after a structural equation modelling. A multigroup analysis reached a full metric invariance level for extraversion.

CONCLUSIONS

These results are of interest for both clinical and research settings. Prenatal screening and associated interventions may reduce perinatal negative affective states and related pregnancy complications.

An alternative approach to future diagnostic standards for major depressive disorder

During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.

Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures

OBJECTIVE

Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.

METHODS

We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.

RESULTS

In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.

CONCLUSION

Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.

The Architecture of Cognitive Vulnerability to Depressive Symptoms in Adolescence: A Longitudinal Network Analysis Study

Rates of subclinical symptoms and full-blown depression significantly increase during adolescence. Hence, understanding how multiple cognitive risk factors are related to depression in adolescence is of major importance. For this purpose, we simultaneously considered multiple cognitive vulnerabilities, as proposed by three major cognitive theories for depression, namely Beck’s cognitive theory, hopelessness theory, and response style theory. In this four-wave study, we investigated the architecture, interplay, and stability of cognitive vulnerability mechanisms, depressive symptoms, and stressors in a large group of adolescents over a period of one year (n = 469; mean age = 15 years; 64% female). Network analysis was used to shed light on the structure of cognitive vulnerabilities in a data-driven fashion. Analyses revealed that different cognitive vulnerabilities were intertwined and automatic thoughts played the role of hub node in the network. Moreover, the interplay among cognitive vulnerabilities and depressive symptoms was already markedly stable in adolescence and did not change over a 12-month period. Finally, no evidence was found that cognitive vulnerabilities interacted with stressors, as proposed by diathesis-stress models. These findings advance our understanding of multiple cognitive risk factors for depression in adolescence.

Emotion Network Analysis During COVID-19 Quarantine - A Longitudinal Study

Introduction: The coronavirus disease 2019 (COVID-19) emergency has imposed important challenges in the lives of individuals, particularly since the restriction of free movement. In Spain, this mandatory home confinement started on March 14, 2020. In this scenario, some calls have been made to better understand the exact impact of the quarantine on the emotional status of individuals across time. Materials and Methods: On the first day that the Spanish government imposed the quarantine, our team launched an online longitudinal study to monitor emotional responses to the COVID-19 emergency over time. For 2 weeks, 187 people have responded to a daily diary on emotion functioning. An emotion network analysis was performed to study the network structure of 30 mood states and its changes during the first 2 weeks of the quarantine. Results: The emotional network showed critical changes in the interactions of emotions over time. An analysis of mean emotional levels did not show statistically significant changes in mood over time. Interestingly, two different network patterns were found when the sample was divided between those with favorable responses and those with unfavorable responses. Discussion: This new approach to the study of longitudinal changes of the mood state network of the population reveals different adaptation strategies reflected on the sample's emotional network. This network approach can help identify most fragile individuals (more vulnerable to external stressors) before they develop clear and identifiable psychopathology and also help identify anti-fragile individuals (those who improve their functioning in the face of external stressors). This is one of the first studies to apply an emotional network approach to study the psychological effects of pandemics and might offer some clues to psychologists and health administrators to help people cope with and adjust to this critical situation.

Network analysis of the depressive symptom profiles in Asian patients with depressive disorders: Findings from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD)

AIM

We aimed to estimate the network structures of depressive symptoms using network analysis and evaluated the geographic regional differences in theses network structures among Asian patients with depressive disorders.

METHODS

Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD), the network of the ICD-10 diagnostic criteria for depressive episode was estimated from 1174 Asian patients with depressive disorders. The node strength centrality of all ICD-10 diagnostic criteria for a depressive episode was estimated using a community-detection algorithm. In addition, networks of depressive symptoms were estimated separately among East Asian patients and South or Southeast Asian patients. Moreover, networks were estimated separately among Asian patients from high-income countries and those from middle-income countries.

RESULTS

Persistent sadness, fatigue, and loss of interest were the most centrally situated within the network of depressive symptoms in Asian patients with depressive disorders overall. A community-detection algorithm estimated that when excluding psychomotor disturbance as an outlier, the other nine symptoms formed the largest clinically meaningful cluster. Geographic and economic variations in networks of depressive symptoms were evaluated.

CONCLUSION

Our findings demonstrated that the typical symptoms of the ICD-10 diagnostic criteria for depressive episode are the most centrally situated within the network of depressive symptoms. Furthermore, our findings suggested that cultural influences related to geographic and economic distributions of participants could influence the estimated depressive symptom network in Asian patients with depressive disorders.

Symptom-based selection of antidepressants

Most evidence-based pharmacological guidelines recommend selective serotonin reuptake inhibitors, serotoninnorepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors or norepinephrine and specific serotonin antidepressants as the first-line treatment for major depression. Since the clinical factors associated with treating patients with depression are relatively complex, it can be challenging to apply the recommendations of evidence-based medicine verbatim. Furthermore, the diagnostic criteria of major depressive disorders, which are defined in a polythetic and operational manner, inevitably result in their heterogeneity. Studies have inferred that depressive syndrome may be connected with “family resemblance” rather than being shared with a neurobiological essence. Therefore, the symptom-based selection of antidepressants can be supported by a network analysis that provides a novel perspective on the symptom structure of major depression. The symptom-based treatment algorithm suggests treatment options that can be applied to the symptoms that are included in and excluded from the diagnosis criteria of major depressive disorder. The symptom-based selection of antidepressants and other psychotropic agents involves matching the deconstructed symptoms of depression to the specific neuroanatomical regions and neurotransmitters. This ensures timely and optimized treatment options for patients with depression.

Psychometric network models from time-series and panel data

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)-an undirected network model of partial correlations-between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

Posttraumatic Stress Disorder Symptom Network Structures: A Comparison Between Men and Women

This study estimated gender differences in the posttraumatic stress disorder (PTSD) symptom network structure (i.e., the unique associations across symptoms) using network analysis in a Latin American sample. Participants were 1,104 adults, taken from epidemiological studies of mental health following natural disasters and accidents in Mexico and Ecuador. Symptoms of DSM-IV PTSD were measured dichotomously with the Spanish version of the Composite International Diagnostic Interview. We estimated the PTSD symptom network of the full sample and in male and female subsamples as well as indices of centrality, the stability and accuracy of the modeled networks, and communities of nodes within each network. The male and female networks were compared statistically using the Network Comparison Test (NCT). Results indicated strength centrality was the only stable centrality measure, with correlation stability (CS) coefficients of .59, .28, and .44 for the full, male, and female networks, respectively. We found the most central symptoms, measured by strength centrality, were loss of interest and flashbacks for men; and concentration impairment, avoiding thoughts/feelings, and physiological reactivity for women. The NCT revealed that the global structure (M = 0.84), p = .704, and global strength (S = 5.04), p = .556, of the male and female networks did not differ significantly. Although some gender differences in the most central symptoms emerged, thus offering some evidence for gender differences pending replication in larger samples, on the whole, our results suggest that once PTSD develops, the way the symptoms are associated does not differ substantially between men and women.

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Cyberchondria and its Relationships with Related Constructs: a Network Analysis

Cyberchondria denotes repeated online searches for health information that are associated with increasing levels of health anxiety. The aims of this study were to apply network analysis to investigate the extent to which cyberchondria is a distinct construct, ascertain which of the related constructs have the strongest relationships with cyberchondria and investigate whether some of the symptoms of cyberchondria are more central to the construct of cyberchondria. Questionnaires assessing the severity of cyberchondria, health anxiety, obsessive-compulsive disorder symptoms, intolerance of uncertainty, problematic Internet use, anxiety, depression and somatic symptoms were administered to 751 participants who searched for health information online during a previous 3-month period and were recruited from an online crowdsourcing platform. Network analyses were used to compute the networks, perform community detection tests and calculate centrality indices. Results suggest that cyberchondria is a relatively specific syndrome-like construct, distinct from all related constructs and consisting of interrelated symptoms. It has the strongest relationships with problematic Internet use and health anxiety. No symptom of cyberchondria emerged clearly as more central to the construct of cyberchondria. Future research should aim to deepen our understanding of cyberchondria and its links with psychopathology, especially its close relationship with problematic Internet use.

Network analysis of specific psychopathology and psychiatric symptoms in patients with eating disorders

BACKGROUND

Network analysis of psychiatric symptoms describes reciprocal relationships of individual symptoms, beyond categorical diagnoses. Those with eating disorders (EDs) frequently have complex patterns of comorbid symptoms and the transdiagnostic theory includes shared common core features across diagnoses. We aim to test whether general psychiatric symptoms comprise components of these transdiagnostic features.

METHODS

Network analysis was applied on 2068 patients with EDs (955 anorexia nervosa [AN], 813 bulimia nervosa [BN], and 300 binge-eating disorder [BED]). All patients underwent clinical interviews and some self-reported questionnaires, such as the Symptom Check-List 90 (SCL-90) to measure psychiatric symptoms, the Eating Disorder Inventory (EDI) to measure ED-specific symptoms, and the Tridimensional Personality Questionnaire (TPQ) for personality traits.

RESULTS

Across EDs and within each ED, SCL-90 scores of depression, anxiety and interpersonal sensitivity, EDI ineffectiveness, interoceptive awareness, interpersonal distrust, and drive for thinness had high centrality. Notably, body mass index (BMI) and EDI bulimia played a central role when considering the whole group, whereas they did not in individual EDs.

DISCUSSION

The shared centrality of identified nodes in both individual and merged groups supported the transdiagnostic theory of EDs (diagnoses share core ED features), with a central role of BMI. Moreover, the most central nodes were general psychiatric symptoms, interpersonal domain, and self-efficacy. These findings suggest that-in addition to ED-core symptoms and BMI-depressive and anxiety symptoms, interpersonal sensitivity and ineffectiveness may be important targets to provide effective treatments across AN, BN, and BED.

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.