Possible Futures for Network Psychometrics

Stress from early life to adulthood: Is there a protective role of cognitive control?

Early life events can have long-lasting effects that may impact the quality of life into adulthood. The link between childhood adversities and adult mental and physical health is well documented, however, the underlying mechanisms remain poorly understood. Executive functions are assumed to be a key factor in successfully dealing with cognitive-emotional challenges thereby contributing to stress resilience and mental health across the lifespan. Here, we examined whether cognitive control moderates the link between early life adversity and depression. Data was available from a sample of 424 participants aged 20-70 years (Clinicaltrials.gov: NCT05155397). They performed in the Stroop task and behavior as well as frontal theta power were recorded. Negative childhood experiences were assessed with the Childhood Trauma Questionnaire (CTQ), chronic stress was measured with the Trier Inventory for Chronic Stress (TICS) and depression symptoms with Beck's Depression Inventory (BDI). The CTQ predicted symptoms of chronic stress and depression. Regression models pointed to the TICS as a crucial mediator in the relationship between CTQ and BDI. However, parameters of cognitive control demonstrated a rather weak effect as moderators. These results indicate that chronic stress is an important mediator linking childhood trauma to depression but suggest only a limited role for cognitive control.

Brazilian Scale for Evaluation of Mental Health Care Needs: Additional Evidence

OBJECTIVE

To investigate validity evidence of the Brazilian Scale for Evaluation of Mental Health Care Needs (CuidaSM).

METHODS

This is a psychometric study, which seeks additional evidence of internal structure. Data collection was carried out in 11 Primary Health Care (PHC) services , which implement the Health Care Planning (HCP) methodology, distributed across the five Brazilian regions. The preliminary version of CuidaSM, containing a block self-referred by the user and another block evaluated by PHC professionals, was applied to users aged 18 or over who attended the PHC services for consultation with a higher education professional. The techniques of confirmatory factor analysis and network analysis were used to investigate validity evidence. For the primary data of the confirmatory factor analysis, the factorial loads and the item's predictive power (R2) were used. Six model adjustment indices were adopted and reliability was measured by three indicators using Bayesian estimation.

RESULTS

A total of 879 users participated in the study. By confirmatory factor analysis, factorial loads ranged from 0.43 to 0.99 and R2 from 0.19 to 0.98. Both the primary indicators and the model adequacy indices were established at satisfactory and consistent levels. The network analysis showed that the items were appropriately associated with their peers, respecting the established dimensions, which again indicates the sustainability and stability of the proposed model.

CONCLUSIONS

The study findings confirm a consistent and reliable model of the instrument, through a combination of techniques. Considering the importance of using solid instruments in clinical practice, CuidaSM is a promising tool for population-based management and network care organization, aligned with HCP proposals.

Validation of the internal structure of the Brazilian Dental Vulnerability Scale

OBJECTIVE

This study aimed to evaluate evidence of validity of internal structure of the Brazilian Dental Vulnerability Scale (EVO-BR) when applied in Brazil.

METHODS

This is a psychometric study that seeks to validate a scale elaborated by evidence of internal structure. Data collection was conducted in 18 basic health units that implement the Brazilian Healthcare Planning (PAS) methodology, across the five regions of Brazil. The initial version of the EVO-BR contained 41 items that measured dental vulnerability and was applied to users of the Brazilian Unified Health System (SUS) aged 18 years or older who were in basic health units for consultation with higher education professionals. To evaluate the evidence, the following statistical analyses were performed: exploratory factor analysis, confirmatory factor analysis, and network analysis.

RESULTS

A total of 1,753 users participated in the study. To adjust the sample, we considered the factorability obtained from Kaiser-Meyer-Olkin (KMO) test = 0.65, Bartlett sphericity test = 8019.7, and a matrix determinant of 0.008. The initial parallel analysis indicated a four-dimensional model and had the items adjusted according to factor loading (ranging from 0.38 to 0.99), common factors (0.13 to 0.89), and Pratt's measure, until the model presented congruence in the statistical and interpretative principles simultaneously. The final model contained 15 items, maintaining the four dimensions indicated by the parallel analysis, and held an explained variance of 68.56%.

CONCLUSIONS

The EVO-BR is a validated scale to measure dental vulnerability and, thus, can contribute to the organization of access to the oral health team in primary health care (PHC) by stratifying the population, as recommended in the Brazilian Healthcare Planning.

Family Vulnerability Scale: validity evidence in primary health care

OBJECTIVE

Investigate evidence of validity of the Family Vulnerability Scale (EVFAM-BR) as an instrument to support population-based management in primary health care (PHC), in the scope of Health Care Planning (PAS).

METHODS

This is a psychometric study to assess any additional evidence of the internal structure of EVFAM-BR using confirmatory factor analysis (CFA) and network analysis (NA). A preliminary version of the scale with 38 items was submitted to patients of PHC facilities that use the PAS methodology, distributed across the five regions of Brazil. For the primary CFA data, factor loadings and predictive power (R2) of the item were used. Seven model adjustment indices were adopted and reliability was measured by three indicators, using Bayesian estimation.

RESULTS

The preliminary version of the scale was applied to 1,255 patients. Using the AFC, factor loadings ranged from 0.66 to 0.90 and R2 from 0.44 to 0.81. Both the primary indicators and the model adequacy indices presented satisfactory and consistent levels. According to the NA, the items were appropriately associated with their peers, respecting the established dimensions, thus demonstrating sustainability and stability of the proposed model.

CONCLUSIONS

The evidence of validity presented by EVFAM-BR indicates, for the first time in Brazil, a concise instrument that is able to assertively measure family vulnerability, potentially supporting population-based management.

A Network Study of Family Affect Systems in Daily Life

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.

Why Do Bi-Factor Models Outperform Higher-Order g Factor Models? A Network Perspective

Bi-factor models of intelligence tend to outperform higher-order g factor models statistically. The literature provides the following rivalling explanations: (i) the bi-factor model represents or closely approximates the true underlying data-generating mechanism; (ii) fit indices are biased against the higher-order g factor model in favor of the bi-factor model; (iii) a network structure underlies the data. We used a Monte Carlo simulation to investigate the validity and plausibility of each of these explanations, while controlling for their rivals. To this end, we generated 1000 sample data sets according to three competing models-a bi-factor model, a (nested) higher-order factor model, and a (non-nested) network model-with 3000 data sets in total. Parameter values were based on the confirmatory analyses of the Wechsler Scale of Intelligence IV. On each simulated data set, we (1) refitted the three models, (2) obtained the fit statistics, and (3) performed a model selection procedure. We found no evidence that the fit measures themselves are biased, but conclude that biased inferences can arise when approximate or incremental fit indices are used as if they were relative fit measures. The validity of the network explanation was established while the outcomes of our network simulations were consistent with previously reported empirical findings, indicating that the network explanation is also a plausible one. The empirical findings are inconsistent with the (also validated) hypothesis that a bi-factor model is the true model. In future model selection procedures, we recommend that researchers consider network models of intelligence, especially when a higher-order g factor model is rejected in favor of a bi-factor model.

Conceptualising compulsivity through network analysis: A two-sample study

Compulsivity is a transdiagnostic construct crucial to understanding multiple psychiatric conditions and problematic repetitive behaviours. Despite being identified as a clinical- and research-relevant construct, there are limited insights into the internal conceptual structure of compulsivity. To provide a more nuanced understanding of compulsivity, the current study estimated the structure of compulsivity (indexed using the previously validated Cambridge-Chicago Compulsivity Trait Scale, CHI-T) among two large-scale and geographically distinct samples using the network estimation method. The samples consisted of a United Kingdom cohort (n = 122,346, 51.4% female, Mean age = 43.7, SD = 16.5, range = 9-86 years) and a South Africa cohort (n = 2674, 65.6% female, Mean age = 24.6, SD = 8.6, range = 18-65 years). Network community analysis demonstrated that compulsivity was constituted of three interrelated dimensions, namely: perfectionism, cognitive rigidity and reward drive. Further, 'Completion leads to soothing' and 'Difficulty moving from task to task' were identified as core (central nodes) to compulsivity. The dimensional structure and central nodes of compulsivity networks were consistent across the two samples. These findings facilitate the conceptualisation and measurement of compulsivity and may contribute to the early detection and treatment of compulsivity-related disorders.

Central psychological symptoms from a network analysis of patients with anxiety, somatoform or personality disorders before psychotherapy

BACKGROUND

Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare.

METHODS

Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980-2015.

RESULTS

Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients.

LIMITATION

Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students.

CONCLUSIONS

Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.

Formalizing psychological interventions through network control theory

Despite the growing deployment of network representation to comprehend psychological phenomena, the question of whether and how networks can effectively describe the effects of psychological interventions remains elusive. Network control theory, the engineering study of networked interventions, has recently emerged as a viable methodology to characterize and guide interventions. However, there is a scarcity of empirical studies testing the extent to which it can be useful within a psychological context. In this paper, we investigate a representative psychological intervention experiment, use network control theory to model the intervention and predict its effect. Using this data, we showed that: (1) the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network control theoretic metrics (average and modal controllability), (2) the size of change following intervention negatively correlates with a whole-network topology that quantifies the "ease" of change as described by control theory (control energy), and (3) responses after intervention can be predicted based on formal results from control theory. These insights assert that network control theory has significant potential as a tool for investigating psychological interventions. Drawing on this specific example and the overarching framework of network control theory, we further elaborate on the conceptualization of psychological interventions, methodological considerations, and future directions in this burgeoning field.

Investigating the Structure of the French WISC-V (WISC-VFR) for Five Age Groups Using Psychometric Network Modeling

Since the seminal work of Spearman, exploratory and confirmatory factor analysis represents the standard method of examining the dimensionality of psychological instruments. Recently, within the network psychometrics approach, a new procedure was proposed to estimate the dimensionality of psychological instruments: exploratory graph analysis (EGA). This study investigated the structure of the French Wechsler Intelligence Scale for Children-Fifth Edition (WISC-VFR) with five standardization sample age groups (6-7, 8-9, 10-11, 12-13, and 14-16 years) using EGA. The primary research questions include (a) how many WISC-VFR dimensions are identified in each age subgroup? (b) how are subtest scores associated within the dimensions? Because the number and the content of the dimensions identified by EGA could vary with samples, the secondary research questions include (c) is there evidence of reproducibility and generalizability of the dimensions identified by EGA? We used another procedure called bootstrap exploratory graph analysis (bootEGA). EGA and bootEGA suggested only three dimensions, which are consistent with processing speed, verbal comprehension, and the "old" perceptual reasoning factor. Results did not support the distinction between visual-spatial and fluid reasoning dimensions. EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC-VFR.

Bootstrap Exploratory Graph Analysis of the WISC-V with a Clinical Sample

One important aspect of construct validity is structural validity. Structural validity refers to the degree to which scores of a psychological test are a reflection of the dimensionality of the construct being measured. A factor analysis, which assumes that unobserved latent variables are responsible for the covariation among observed test scores, has traditionally been employed to provide structural validity evidence. Factor analytic studies have variously suggested either four or five dimensions for the WISC-V and it is unlikely that any new factor analytic study will resolve this dimensional dilemma. Unlike a factor analysis, an exploratory graph analysis (EGA) does not assume a common latent cause of covariances between test scores. Rather, an EGA identifies dimensions by locating strongly connected sets of scores that form coherent sub-networks within the overall network. Accordingly, the present study employed a bootstrap EGA technique to investigate the structure of the 10 WISC-V primary subtests using a large clinical sample (N = 7149) with a mean age of 10.7 years and a standard deviation of 2.8 years. The resulting structure was composed of four sub-networks that paralleled the first-order factor structure reported in many studies where the fluid reasoning and visual-spatial dimensions merged into a single dimension. These results suggest that discrepant construct and scoring structures exist for the WISC-V that potentially raise serious concerns about the test interpretations of psychologists who employ the test structure preferred by the publisher.

Network analysis of body-related complaints in patients with neurotic or personality disorders referred to psychotherapy

Background

Psychopathology theory and clinical practice require the most complex knowledge about patients' complaints. In patients seeking for psychotherapy, body-related symptoms often complicate treatment.

Aim

This study aimed at examining connections between body-related symptoms, and identification of symptoms which may be responsible for emergency and sustaining of anxiety, somatoform and personality disorders with the use of network analysis.

Methods

In our retrospective research we used data from a sample of 4616 patients of the Department of Psychotherapy, University Hospital in Cracow, diagnosed with anxiety, somatoform or personality disorders. We constructed the Triangulated Maximally Filtered Graph (TMFG) networks of 44 somatoform symptoms endorsed in the symptom checklist "O" (SCL-O) and identified the most central symptoms within the network for all patients and in subgroups of women vs. men, older vs. younger, and diagnosed in 1980-2000 vs. 2000-2015. We used bootstrap to determine the accuracy and stability of five networks' parameters: strength, expected influence, eigenvector, bridge strength and hybrid centrality.

Results

The most central symptoms within the overall network, and in six subnetworks were dyspnea and migratory pains. We identified some gender-related differences, but no differences were observed for the age and time of diagnosis.

Conclusions

Self-reported dyspnea and migratory pains are potential important targets for treatment procedures.

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.

Network controllability measures of subnetworks: implications for neurosciences

Objective:Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand psychiatric disorders, a potentially large set of related variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics estimated over subnetworks would deviate, if access to the complete set of variables, as is common in neurosciences, cannot be taken for granted.Approach:In this paper, we use a host of simulations based on synthetic as well as structural magnetic resonance imaging (MRI) data to study the potential deviation of controllability metrics in sub- compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density.Main results:We find out, across all network types we test, that average and modal controllability are systematically, over- or underestimated depending on the number of nodes in the sub- and full network and the edge density.Significance:Finally, we provide formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.

Psychological Network of Stress, Coping and Social Support in an Aboriginal Population

Over the past decades, increasing research interest has been directed towards the psychosocial factors that impact Aboriginal health, including stress, coping and social support. However, there has been no study that examined whether the behaviours, cognitions and emotions related to stress, coping and social support constitute a psychological network in an Aboriginal population and that examined its properties. To address this gap, the current study employed a new methodology, network psychometrics, to evaluate stress, coping and social support in an Aboriginal Australian population. This study conducted a secondary analysis of the South Australian Aboriginal Birth Cohort (SAABC) study, a randomised controlled trial in South Australia, which included 367 pregnant Aboriginal women at study baseline. The Gaussian Graphical Model was estimated with least absolute shrinkage and selection operator (LASSO). Node centrality was evaluated with eigencentrality, strength and bridge centrality. Network communities were investigated with the walktrap algorithm. The findings indicated that stress, coping and social support constituted a connected psychological network in an Aboriginal population. Furthermore, at the centre of the network were the troubles experienced by the Aboriginal pregnant women, bridging their perceptions of stress and coping and constituting a potential target for future interventions.