Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs

A Dynamical Systems View of Psychiatric Disorders-Theory: A Review

Importance

Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems.

Observations

In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article.

Conclusions and Relevance

Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.

A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses

Grade of membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore, GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM comes at the cost of more challenging identifiability and estimation problems. In this work, we propose a singular value decomposition (SVD)-based spectral approach to GoM analysis with multivariate binary responses. Our approach hinges on the observation that the expectation of the data matrix has a low-rank decomposition under a GoM model. For identifiability, we develop sufficient and almost necessary conditions for a notion of expectation identifiability. For estimation, we extract only a few leading singular vectors of the observed data matrix and exploit the simplex geometry of these vectors to estimate the mixed membership scores and other parameters. We also establish the consistency of our estimator in the double-asymptotic regime where both the number of subjects and the number of items grow to infinity. Our spectral method has a huge computational advantage over Bayesian or likelihood-based methods and is scalable to large-scale and high-dimensional data. Extensive simulation studies demonstrate the superior efficiency and accuracy of our method. We also illustrate our method by applying it to a personality test dataset.

Subgroups of cognitively affected and unaffected breast cancer survivors after chemotherapy: a data-driven approach

PURPOSE

It is assumed that a segment of breast cancer survivors are cognitively affected after chemotherapy. Our aim is to discover whether there is a qualitatively different cognitively affected subgroup of breast cancer survivors, or whether there are only quantitative differences between survivors in cognitive functioning.

METHODS

Latent profile analysis was applied to age-corrected neuropsychological data -measuring verbal memory, attention, speed, and executive functioning- from an existing sample of 62 breast cancer survivors treated with chemotherapy. Other clustering methods were applied as sensitivity analyses. Subgroup distinctness was established with posterior mean assignment probability and silhouette width. Simulations were used to calculate subgroup stability, posterior predictive checks to establish absolute fit of the subgrouping model. Subgrouping results were compared to traditional normative comparisons results.

RESULTS

Two subgroups were discovered. One had cognitive normal scores, the other -45%- had lower scores. Subgrouping results were consistent across clustering methods. The subgroups showed some overlap; 6% of survivors could fall in either. Subgroups were stable and described the data well. Results of the subgroup clustering model matched those of a traditional normative comparison method requiring small deviations on two cognitive domains.

CONCLUSIONS

We discovered that almost half of breast cancer survivors after chemotherapy form a cognitively affected subgroup, using a data-driven approach. This proportion is higher than previous studies using prespecified cutoffs observed.

IMPLICATIONS FOR CANCER SURVIVORS

A larger group of cancer survivors may be cognitively affected than previously recognized, and a less strict threshold for cognitive problems may be needed in this population.

Detecting non-content-based response styles in survey data: An application of mixture factor analysis

It is common for some participants in self-report surveys to be careless, inattentive, or lacking in effort. Data quality can be severely compromised by responses that are not based on item content (non-content-based [nCB] responses), leading to strong biases in the results of data analysis and misinterpretation of individual scores. In this study, we propose a specification of factor mixture analysis (FMA) to detect nCB responses. We investigated the usefulness and effectiveness of the FMA model in detecting nCB responses using both simulated data (Study 1) and real data (Study 2). In the first study, FMA showed reasonably robust sensitivity (.60 to .86) and excellent specificity (.96 to .99) on mixed-worded scales, suggesting that FMA had superior properties as a screening tool under different sample conditions. However, FMA performance was poor on scales composed of only positive items because of the difficulty in distinguishing acquiescent patterns from valid responses representing high levels of the trait. In Study 2 (real data), FMA detected a minority of cases (6.5%) with highly anomalous response patterns. Removing these cases resulted in a large increase in the fit of the unidimensional model and a substantial reduction in spurious multidimensionality.

Variation in global network properties across risk factors for adolescent internalizing symptoms: evidence of cumulative effects on structure and connectivity

BACKGROUND

Identifying adolescents at risk of internalizing problems is a key priority. However, studies have tended to consider such problems in simple ways using diagnoses, or item summaries. Network theory and methods instead allow for more complex interaction between symptoms. Two key hypotheses predict differences in global network properties for those at risk: altered structure and increased connectivity.

METHODS

The current study evaluated these hypotheses for nine risk factors (e.g. income deprivation and low parent/carer support) individually and cumulatively in a large sample of 12-15 year-olds (N = 34 564). Recursive partitioning and bootstrapped networks were used to evaluate structural and connectivity differences.

RESULTS

The pattern of network interactions was shown to be significantly different via recursive partitioning for all comparisons across risk-present/absent groups and levels of cumulative risk, except for income deprivation. However, the magnitude of differences appeared small. Most individual risk factors also showed relatively small effects for connectivity. Exceptions were noted for gender and sexual minority risk groups, as well as low parent/carer support, where larger effects were evident. A strong linear trend was observed between increasing cumulative risk exposure and connectivity.

CONCLUSIONS

A robust approach to considering the effect of risk exposure on global network properties was demonstrated. Results are consistent with the ideas that pathological states are associated with higher connectivity, and that the number of risks, regardless of their nature, is important. Gender/sexual minority status and low parent/carer support had the biggest individual impacts on connectivity, suggesting these are particularly important for identification and prevention.

The ABC of happiness: Validation of the tridimensional model of subjective well-being (affect, cognition, and behavior) using Bifactor Polytomous Multidimensional Item Response Theory

Background

Happiness is often conceptualized as subjective well-being, which comprises people's evaluations of emotional experiences (i.e., the affective dimension: positive and negative feelings and emotions) and judgements of a self-imposed ideal (i.e., the cognitive dimension: life satisfaction). Recent research has established these two dimensions as primary parts of a higher order factor. However, theoretical, conceptual, and empirical work suggest that people's evaluations of harmony in their life (i.e., the sense of balance and capacity to behave and adapt with both acceptance and flexibility to inter- and intrapersonal circumstances) constitutes a third dimension (i.e., the behavioral dimension). This tridemensional conceptualization of subjective well-being has recently been verified using Unidimensional Item Response Theory (UIRT) and Classical Test Theory (CTT). Here, we use a recently developed and more robust approach that combines these two methods (i.e., Multidimensional Item Response Theory, MIRT) to simultaneously address the complex interactions and multidimensionality behind how people feel, think, and behave in relation to happiness in their life.

Method

A total of 435 participants (197 males and 238 females) with an age mean of 44.84 (sd = 13.36) responded to the Positive Affect Negative Affect Schedule (10 positive affect items, 10 negative affect items), the Satisfaction with Life Scale (five items), and the Harmony in life Scale (five items). We used Bifactor-Graded Response MIRT for the main analyses.

Result

At the general level, each of the 30 items had a strong capacity to discriminate between respondents across all three dimensions of subjective well-being. The investigation of different parameters (e.g., marginal slopes, ECV, IECV) strongly reflected the multidimensionality of subjective well-being at the item, the scale, and the model level. Indeed, subjective well-being could explain 64 % of the common variance in the whole model. Moreover, most of the items measuring positive affect (8/10) and life satisfaction (4/5) and all the items measuring harmony in life (5/5) accounted for a larger amount of variance of subjective well-being compared to that of their respective individual dimensions. The negative affect items, however, measured its own individual concept to a lager extent rather than subjective well-being. Thus, suggesting that the experience of negative affect is a more independent dimension within the whole subjective well-being model. We also found that specific items (e.g., "Alert", "Distressed", "Irritable", "I am satisfied with my life") were the recurrent exceptions in our results. Last but not the least, experiencing high levels in one dimension seems to compensate for low levels in the others and vice versa.

Conclusion

As expected, the three subjective well-being dimensions do not work separately. Interestingly, the order and magnitude of the effect by each dimension on subjective well-being mirror how people define happiness in their life: first as harmony, second as satisfaction, third as positive emotions, and fourth, albeit to a much lesser degree, as negative emotions. Ergo, we argue that subjective well-being functions as a complex biopsychosocial adaptive system mirroring our attitude towards life in these three dimensions (A: affective dimension; B: behavioral dimension; C: cognitive dimension). Ergo, researchers and practitioners need to take in to account all three to fully understand, measure, and promote people's experience of the happy life. Moreover, our results also suggest that negative affect, especially regarding high activation unpleasant emotions, need considerable changes and further analyses if it is going to be included as a construct within the affective dimension of a general subjective well-being factor.

Dyslexia, the Amsterdam Way

The current aim is to illustrate our research on dyslexia conducted at the Developmental Psychology section of the Department of Psychology, University of Amsterdam, in collaboration with the nationwide IWAL institute for learning disabilities (now RID). The collaborative efforts are institutionalized in the Rudolf Berlin Center. The first series of studies aimed at furthering the understanding of dyslexia using a gamified tool based on an artificial script. Behavioral measures were augmented with diffusion modeling in one study, and indices derived from the electroencephalogram were used in others. Next, we illustrated a series of studies aiming to assess individuals who struggle with reading and spelling using similar research strategies. In one study, we used methodology derived from the machine learning literature. The third series of studies involved intervention targeting the phonics of language. These studies included a network analysis that is now rapidly gaining prominence in the psychopathology literature. Collectively, the studies demonstrate the importance of letter-speech sound mapping and word decoding in the acquisition of reading. It was demonstrated that focusing on these abilities may inform the prediction, classification, and intervention of reading difficulties and their neural underpinnings. A final section examined dyslexia, conceived as a neurobiological disorder. This analysis converged on the conclusion that recent developments in the psychopathology literature inspired by the focus on research domain criteria and network analysis might further the field by staying away from longstanding debates in the dyslexia literature (single vs. a multiple deficit, category vs. dimension, disorder vs. lack of skill).

Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample

In the year 2022, this research conducted an in-person study involving 780 single or widowed women, aged between 20 and 70, falling within the bottom three economic deciles and possessing varying levels of education. All participants held educational qualifications below a high school diploma and were beneficiaries of charitable financial support in Khorasan province, Iran. The study aimed to investigate the predictive factors of social dysfunction in this specific demographic. Data collection spanned a 12-month period throughout 2022, with participants completing the GHQ-28 questionnaire during their visits to the charity office. Clinical in-person interviews were also conducted to gather comprehensive data. Data analysis was carried out using IBM SPSS version 27. The research employed a Multilayer Perceptron (MLP) neural network model, considering an extensive set of input factors and covariates. These factors included cognitive functioning, anxiety, depression, age, and education levels. The MLP model exhibited robust performance, achieving high overall accuracy and sensitivity in identifying cases of high social dysfunction. The findings emphasized the significance of cognitive functioning, anxiety, and depression as pivotal predictors of social dysfunction within this specific demographic, while education and age displayed relatively lower importance. The normalized importance scores provided a relative measure of each covariate's impact on the model's predictions. These results furnish valuable insights for the development of targeted interventions and evidence-based policies aimed at addressing social dysfunction and promoting societal well-being among economically disadvantaged, single or widowed women. Notably, the research underscores the potential of MLP modeling in social science research and suggests avenues for further research and refinement to enhance the model's predictive accuracy, particularly for cases of low social dysfunction.

Symptoms and prevalence of common mental disorders in a heterogenous outpatient sample: an investigation of clinical characteristics and latent subgroups

BACKGROUND

Patient-reported outcome measures (PROM) provide clinicians with information about patients' perceptions of distress. When linked with treatment and diagnostic registers, new information on common mental health disorders (CMHD) and service use, may be obtained, which might be useful clinically and for policy decision-making. This study reports the prevalence of CMHD and their association with PROM severity. Further, subgroups of self-reported symptoms of depression and anxiety were examined, and their association with clinician-assessed mental disorders, functional impairment, and service use.

METHODS

In a cohort study of 2473 (63% female) outpatients, CMHD was examined with pre-treatment scores of self-reported depression and anxiety, and the number of assessments and psychotherapy appointments one year after treatment start. Factor mixture modelling (FMM) of anxiety and depression was used to examine latent subgroups.

RESULTS

Overall, 22% of patients with a CMHD had an additional comorbid mood/anxiety disorder, making the prevalence lower than expected. This comorbid group reported higher symptoms of anxiety and depression compared to patients with non-comorbid disorders. FMM revealed three classes: "anxiety and somatic depression" (33%), "mixed depression and anxiety" (40%), and "cognitive depression" (27%). The anxiety and somatic depression class was associated with older age, being single and on sick leave, higher probability of depressive-, anxiety-, and comorbid disorders, having more appointments and higher functional impairment. Although the cognitive depression class had less somatic distress than the mixed depression and anxiety class, they reported more functional impairment and had higher service use.

CONCLUSION

The results show that higher levels of somatic symptoms of depression could both indicate higher and lower levels of functional impairment and service use. A group of patients with high somatic depression and anxiety was identified, with severe impairment and high service needs. By gaining insights into CMHD factors' relation with clinical covariates, self-reported risk factors of depression and anxiety could be identified for groups with different levels of aggravating life circumstances, with corresponding service needs. These could be important symptom targets in different groups of patients.

Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.

METHODS

This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.

RESULTS

Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.

CONCLUSIONS

Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.

Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD

The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (N = 434), and replicate the results in an independent sample (N = 219) of children (ADHD, ASD, and typically developing) aged 7-14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy-suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.

Temporal Network Analysis of Neurocognitive Functioning and Psychological Symptoms in Collegiate Athletes After Concussion

Sport-related concussion (SRC) is associated with several post-injury consequences, including neurocognitive decrements and psychological distress. Yet, how these clinical markers interact with each other, the magnitude of their interrelationships, and how they may vary over time following SRC are not well understood. Network analysis has been proposed as a statistical and psychometric method to conceptualize and map the complex interplay of interactions between observed variables (e.g., neurocognitive functioning and psychological symptoms). For each collegiate athlete with SRC (n = 565), we constructed a temporal network as a weighted graph, with nodes, edges, and the set of weights associated with each edge at three time-points (baseline, 24-48 h post-injury, and asymptomatic), that graphically depicts the interrelated nature of neurocognitive functioning and symptoms of psychological distress throughout the recovery process. This graph shows that the inter-group relationships between neurocognitive functioning and symptoms of psychological distress were stronger at the 24-48 h time-point than at baseline or at the asymptomatic time-point. Further, all symptoms of psychological distress and neurocognitive functioning significantly improved from the 24-48 h time-point to asymptomatic status. The effect sizes of these changes ranged from 0.126 (small) to 0.616 (medium). This research suggests that significant improvements in symptoms of psychological distress appear necessary to drive related improvements in neurocognitive functioning and vice versa. Therefore, clinical interventions should consider the importance of managing psychological distress during the acute care of individuals with SRC to help ameliorate negative outcomes.

Investigating the muti-scaling properties and connectedness of the sovereign bond yields: Hurst exponent and network analysis approach

Using daily yield data of 14 sovereign bond markets from emerging and developed economies from July 10, 2000, to July 10, 2022, we examine their scaling properties using generalized Hurst exponent and spectral density analysis and investigate the connectedness based on a network analysis approach. We consider the yields of 2-year and 10-year bond yields to investigate the scaling properties for short- and long-term sovereign bonds. This selection also allows us to examine sovereign bond spreads with respect to the USA. We also use regularized partial correlation network analysis to connect different countries in communities based on yields. We find that the scaling behavior of the bond yields for both terms fits well using the Hurst exponent and spectral analysis confirms this finding. Moreover, we also find that even though bonds in both cohorts show anti-persistent behavior except that of the USA, the developed economies' bond yields are relatively less anti-persistent as compared to those of emerging economies. The networks of both the 2-year and 10-year yields indicate community formation in various countries which provides diversification benefits to the investors. Most of the emerging countries are classified into one community in the long-tenure bonds as well but this concentration is more evident in the short-tenure bonds.

Categorical versus dimensional structure of autism spectrum disorder: A multi-method investigation

Background

A key question for any psychopathological diagnosis is whether the condition is continuous or discontinuous with typical variation. The primary objective of this study was to use a multi-method approach to examine the broad latent categorical versus dimensional structure of autism spectrum disorder (ASD).

Method

Data were aggregated across seven independent samples of participants with ASD, other neurodevelopmental disorders (NDD), and non-ASD/NDD controls (aggregate Ns = 512-16,755; ages 1.5-22). Scores from four distinct phenotype measures formed composite "indicators" of the latent ASD construct. The primary indicator set included eye gaze metrics from seven distinct social stimulus paradigms. Logistic regressions were used to combine gaze metrics within/across paradigms, and derived predicted probabilities served as indicator values. Secondary indicator sets were constructed from clinical observation and parent-report measures of ASD symptoms. Indicator sets were submitted to taxometric- and latent class analyses.

Results

Across all indicator sets and analytic methods, there was strong support for categorical structure corresponding closely to ASD diagnosis. Consistent with notions of substantial phenotypic heterogeneity, the ASD category had a wide range of symptom severity. Despite the examination of a large sample with a wide range of IQs in both genders, males and children with lower IQ were over-represented in the ASD category, similar to observations in diagnosed cases.

Conclusions

Our findings provide strong support for categorical structure corresponding closely to ASD diagnosis. The present results bolster the use of well-diagnosed and representative ASD groups within etiologic and clinical research, motivating the ongoing search for major drivers of the ASD phenotype. Despite the categorical structure of ASD, quantitative symptom measurements appear more useful for examining relationships with other factors.

Value-based decision-making network functional connectivity correlates with substance use and delay discounting behaviour among young adults

Substance use disorders are characterized by reduced control over the quantity and frequency of psychoactive substance use and impairments in social and occupational functioning. They are associated with poor treatment compliance and high rates of relapse. Identification of neural susceptibility biomarkers that index risk for developing a substance use disorder can facilitate earlier identification and treatment. Here, we aimed to identify the neurobiological correlates of substance use frequency and severity amongst a sample of 1,200 (652 females) participants aged 22-37 years from the Human Connectome Project. Substance use behaviour across eight classes (alcohol, tobacco, marijuana, sedatives, hallucinogens, cocaine, stimulants, opiates) was measured using the Semi-Structured Assessment for the Genetics of Alcoholism. We explored the latent organization of substance use behaviour using a combination of exploratory structural equation modelling, latent class analysis, and factor mixture modelling to reveal a unidimensional continuum of substance use behaviour. Participants could be rank ordered along a unitary severity spectrum encompassing frequency of use of all eight substance classes, with factor score estimates generated to represent each participant's substance use severity. Factor score estimates and delay discounting scores were compared with functional connectivity in 650 participants with imaging data using the Network-based Statistic. This neuroimaging cohort excludes participants aged 31 and over. We identified brain regions and connections correlated with impulsive decision-making and poly-substance use, with the medial orbitofrontal, lateral prefrontal and posterior parietal cortices emerging as key hubs. Functional connectivity of these networks could serve as susceptibility biomarkers for substance use disorders, informing earlier identification and treatment.

Stronger coupling of emotional instability with reward processing in borderline personality disorder is predicted by schema modes

BACKGROUND

Mood instability and risk-taking are hallmarks of borderline personality disorder (BPD). Schema modes are combinations of self-reflective evaluations, negative emotional states, and destructive coping strategies common in BPD. When activated, they can push patients with BPD into emotional turmoil and a dissociative state of mind. Our knowledge of the underlying neurocognitive mechanisms driving these changes is incomplete. We hypothesized that in patients with BPD, affective instability is more influenced by reward expectation, outcomes, and reward prediction errors (RPEs) during risky decision-making than in healthy controls. Additionally, we expected that these alterations would be related to schema modes.

METHODS

Thirty-two patients with BPD and thirty-one healthy controls were recruited. We used an established behavioral paradigm to measure mood fluctuations during risky decision-making. The impact of expectations and RPEs on momentary mood was quantified by a computational model, and its parameters were estimated with hierarchical Bayesian analysis. Model parameters were compared using High-Density Intervals.

RESULTS

We found that model parameters capturing the influence of RPE and Certain Rewards on mood were significantly higher in patients with BPD than in controls. These model parameters correlated significantly with schema modes, but not with depression severity.

CONCLUSIONS

BPD is coupled with altered associations between mood fluctuation and reward processing under uncertainty. Our findings seem to be BPD-specific, as they stand in contrast with the correlates of depressive symptoms. Future studies should establish the clinical utility of these alterations, such as predicting or assessing therapeutic response in BPD.

Investigating the Construct Validity and Reliability of the Test of Motor Competence Across Iranians' Lifespan

Motor competence (MC) has been extensively examined in children and adolescents, but has not been studied among adults nor across the lifespan. The Test of Motor Competence (TMC) assesses MC in people aged 5-85 years. Among Iranians, aged 5-85 years, we aimed to determine the construct validity and reliability of the TMC and to examine associations between TMC test items and the participants' age, sex, and body mass index (BMI). We conducted confirmatory factor analysis (CFA) to evaluate the TMC's factorial structure by age group and for the whole sample. We explored associations between the TMC test items and participant age, sex, and BMI using a network analysis machine learning technique (Rstudio and qgraph). CFA supported the construct validity of a unidimensional model for motor competence for the whole sample (RMSEA = 0.003; CFI = 0.998; TLI = 0.993) and for three age groups (RMSEA <0.08; CFI and TLI >0.95). Network analyses showed fine motor skills to be the most critical centrality skills, reinforcing the importance of fine motor skills for performing and participating in many daily activities across the lifespan. We found the TMC to be a valid and reliable test to measure MC across Iranians' lifespan. We also demonstrated the advantages of using a machine learning approach via network analysis to evaluate associations between skills in a complex system.

The continuum of attention dysfunction: Evidence from dynamic functional network connectivity analysis in neurotypical adolescents

The question of whether attention-related disorders such as attention-deficit/hyperactivity disorder (ADHD) are best understood as clinical categories or as extreme ends of a spectrum is an ongoing debate. Assessing individuals with varying degrees of attention problems and utilizing novel methodologies to assess relationships between attention and brain activity may provide key information to support the spectrum hypothesis. We scanned 91 neurotypical adolescents during rest using functional magnetic resonance imaging. We conducted static and dynamic functional network connectivity (FNC) analysis and correlated findings to behavioral metrics of ADHD, attention problems, and impulsivity. We found that dynamic FNC analysis detects significant differences in large-scale neural connectivity as a function of individual differences in attention and impulsivity that are obscured in static analysis. We show ADHD manifestations and attention problems are associated with diminished Salience Network-centered FNC and that ADHD manifestations and impulsivity are associated with prolonged periods of dynamically hyperconnected states. Importantly, our meta-state analysis results reveal a relationship between ADHD manifestations and exhibiting variable and volatile dynamic behavior such as changing meta-states more often and traveling over a greater dynamic range. These findings in non-clinical adolescents provide support for the continuum model of attention disorders.

Understanding the relationships between trauma type and individual posttraumatic stress symptoms: a cross-sectional study of a clinical sample of children and adolescents

BACKGROUND

Characteristics of traumatic events may be associated with the level and specific manifestation of posttraumatic stress symptoms (PTSS). This study examined the differences and similarities between overall levels, profiles and networks of PTSS after sexual trauma, domestic violence, community violence, non-interpersonal trauma, sudden loss or serious illness of a loved one, and severe bullying or threats.

METHODS

PTSS were measured in a clinical sample of 4,921 children and adolescents (6-18 years old, M = 14.0, SD = 2.7, 63.7% female) referred to Child and Adolescent Mental Health Services. We compared 95% confidence intervals (CI) for each symptom with 95% CI for overall PTSS within each trauma type (self-reported worst trauma). We also computed cross-sectional networks and searched for differences in networks according to trauma type and overall symptom level.

RESULTS

The overall frequencies of PTSS were highest following sexual trauma; somewhat lower for domestic violence and severe bullying or threats and lowest after community violence, non-interpersonal trauma and sudden loss or serious illness. Psychological cue reactivity, avoidance and difficulties with sleeping and concentrating were generally among the most frequent symptoms. Sexual trauma, domestic violence and severe bullying or threats were associated with higher frequencies of negative beliefs and persistent negative emotional states. Few differences in symptom networks across trauma type emerged.

CONCLUSION

Different types of trauma exposure may be associated with different profiles of symptom frequencies. Knowledge about this may be useful for clinicians and for the movement towards evidence-based personalized psychological treatment.

Clinical subtyping using community detection: Limited utility?

OBJECTIVES

To discover psychiatric subtypes, researchers are adopting a method called community detection. This method was not subjected to the same scrutiny in the psychiatric literature as traditional clustering methods. Furthermore, many community detection algorithms have been developed without psychiatric sample sizes and variable numbers in mind. We aim to provide clarity to researchers on the utility of this method.

METHODS

We provide an introduction to community detection algorithms, specifically describing the crucial differences between correlation-based and distance-based community detection. We compare community detection results to results of traditional methods in a simulation study representing typical psychiatry settings, using three conceptualizations of how subtypes might differ.

RESULTS

We discovered that the number of recovered subgroups was often incorrect with several community detection algorithms. Correlation-based community detection fared better than distance-based community detection, and performed relatively well with smaller sample sizes. Latent profile analysis was more consistent in recovering subtypes. Whether methods were successful depended on how differences were introduced.

CONCLUSIONS

Traditional methods like latent profile analysis remain reasonable choices. Furthermore, results depend on assumptions and theoretical choices underlying subtyping analyses, which researchers need to consider before drawing conclusions on subtypes. Employing multiple subtyping methods to establish method dependency is recommended.

Revisiting the theoretical and methodological foundations of depression measurement

Depressive disorders are among the leading causes of global disease burden, but there has been limited progress in understanding the causes and treatments for these disorders. In this Perspective, we suggest that such progress crucially depends on our ability to measure depression. We review the many problems with depression measurement, including limited evidence of validity and reliability. These issues raise grave concerns about common uses of depression measures, such as diagnosis or tracking treatment progress. We argue that shortcomings arise because depression measurement rests on shaky methodological and theoretical foundations. Moving forward, we need to break with the field's tradition that has, for decades, divorced theories about depression from how we measure it. Instead, we suggest that epistemic iteration, an iterative exchange between theory and measurement, provides a crucial avenue for depression measurement to progress.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis

BACKGROUND

Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

METHODS

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes.

RESULTS

The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.

CONCLUSION

SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

Morphological Awareness Performance Profiles of First- Through Sixth-Grade Students

PURPOSE

We examined whether diverse profiles of strengths and weaknesses would emerge when assessing different aspects of morphological awareness in first- through sixth-grade students using a recently developed standardized test, the Morphological Awareness Test for Reading and Spelling (MATRS; Apel et al., 2021).

METHOD

Four thousand fifty-nine first- through sixth-grade students completed the eight morphological awareness tasks of the MATRS. The eight tasks represent the multiple ways that morphological awareness impacts both spoken and written language skills for the English language. Exploratory finite mixture models estimated the number of latent subgroups that best reflected heterogeneity in task-level performance by grade level. Specific profiles were chosen that demonstrated strong reliability and included a set of tasks that were consistent between first- and second-grade students and between third- and sixth-grade students.

RESULTS

Different performance profiles emerged when the students completed multiple morphological awareness tasks. At each of the six grades (first through sixth), clusters of students performed differentially on specific tasks.

CONCLUSIONS

The findings demonstrate that students can differ in patterns of strength and weaknesses of their morphological awareness given a range of tasks that assess different aspects of morphological awareness. The clinical implications of these findings suggest that by identifying students struggling in specific areas of morphological awareness, clinicians can develop and implement specific prescriptive instructional plans.

Cultural Adaptation, Validation, and Psychometric Description of the Pictorial Empathy Test (PET) in the Spanish Population

Abstract. Research in psychology and social neuroscience distinguishes between dispositional and situational empathy for the cognitive and affective aspects of empathy. Yet, the Pictorial Empathy Test (PET) is one of the few brief tests focusing on situational affective empathy. This paper describes the adaptation of PET in a two-stage process, consisting of instrument translation and exploratory analysis of the construct in a university student sample, followed by confirmatory factor analysis and psychometric validation in a general population sample (Study 1, N = 79 and Study 2, N = 580). Our results indicate that the Spanish PET version constitutes a single factor structure with high internal consistency as well as high construct stability across genders and across in-person and online test administration setups. We report satisfactory convergent, discriminant, and nomological validity of the Spanish PET, as reported for the original version, in addition to when explored across new dimensions, like the Interpersonal Reactivity Index or in relation to age and prosocial tendencies. Based on the above, we discuss delineation of the distinct components of emotional empathy as measured by the instrument. The work presented supports the use of the Spanish PET version as a brief screening tool for state affective (emotional) empathy.

Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing

Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.

The psychological outcomes of COVID-19 affected the pandemic-after risk perceptions of nurse clinicians: a latent profile analysis

BACKGROUND

Risk perception among nurses after the COVID-19 pandemic is a crucial factor affecting their attitudes and willingness to work in clinics. Those with poor psychological status could perceive risks sensitively as fears or threats that are discouraging. This article aimed to determine whether psychological outcomes, including post-traumatic stress disorder (PTSD), depression, anxiety, and insomnia, following the COVID-19 pandemic were differentially related to the risk perceptions of nurses working in clinics and increased perceived risk.

METHOD

The participants were 668 nurse clinicians from five local hospitals. Risk perceptions and psychological outcomes were measured by adapted questionnaires via the Internet. Latent profile analysis (LPA) identified subgroups of individuals who showed similar profiles regarding the perceived risks in nursing. Multinomial regression and probit regression were used to examine the extent to which sociodemographic and psychological outcomes predicted class membership.

RESULTS

LPA revealed four classes: groups with low-, mild-, moderate-, and high-level risk perceptions. Membership of the high-level risk perception class was predicted by the severity of psychological outcomes. Anxiety significantly accounted for a moderate increase in risk perceptions, while the symptoms of insomnia, depression, and PTSD accelerated the increase to the high level of risk perception class.

CONCLUSIONS

By classifying groups of nurse clinicians sharing similar profiles regarding risk perceptions and then exploring associated predictors, this study shows the psychological outcomes after COVID-19 significantly impacted pandemic-associated risk perceptions and suggests intervening in nurses' psychological outcomes while simultaneously focusing on work-related worries is important following the outbreak of COVID-19.

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease

Background:

In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status.

Objective:

It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied.

Methods:

In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes.

Results:

The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset.

Conclusion:

Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

Why not both? Rethinking categorical and continuous approaches to bilingualism

AIMS AND OBJECTIVES

Bilingualism is a complex construct, and it can be difficult to define and model. This paper proposes that the field of bilingualism can draw from other fields of psychology, by integrating advanced psychometric models that incorporate both categorical and continuous properties. These models can unify the widespread use of bilingual and monolingual groups that exist in the literature with recent proposals that bilingualism should be viewed as a continuous variable.

APPROACH

In the paper, we highlight two models of potential interest: the factor mixture model and the grade-of-membership model. These models simultaneously allow for the formation of different categories of speakers and for continuous variation to exist within these categories. We discuss how these models could be implemented in bilingualism research, including how to develop these models. When using either of the two models, researchers can conduct their analyses on either the categorical or continuous information, or a combination of the two, depending on which is most appropriate to address their research question.

CONCLUSIONS

The field of bilingualism research could benefit from incorporating more complex models into definitions of bilingualism. To help various subfields of bilingualism research converge on appropriate models, we encourage researchers to pre-register their model selection and planned analyses, as well as to share their data and analysis scripts.

ORIGINALITY

The paper uniquely proposes the incorporation of advanced statistical psychometric methods for defining and modeling bilingualism.

SIGNIFICANCE

Conceptualizing bilingualism within the context of these more flexible models will allow a wide variety of research questions to be addressed. Ultimately, this will help to advance theory and lead to a fuller and deeper understanding of bilingualism.

Latent classes of maladaptive personality traits exhibit differences in social processing

OBJECTIVE

Social processing (SP) deficits manifest across numerous mental disorders. However, this research has been plagued by heterogeneity and a piecemeal approach whereby skills are examined in isolation rather than as part of an integrated cognitive system. Here, we combined two dimensional frameworks of psychopathology to address these limitations.

METHOD

We utilized the Alternative Model for Personality Disorders (AMPD) to distill trait-related heterogeneity within a community sample (n = 200), and the Research Domain Criteria (RDoC) 'Systems for Social Processes' to comprehensively assess SP. We first applied latent class analyses (LCA) to derive AMPD-based groups and subsequently contrasted the performance of these groups on a SP test battery that we developed to align with the RDoC SP constructs.

RESULTS

Our LCA yielded four distinct subgroups. The recognizable trait profiles and psychopathological symptoms of these classes suggested they were clinically meaningful. The subgroups differed in their SP profiles: one displayed deficits regarding the self, a second displayed deficits in understanding others, a third displayed more severe deficits including affiliation problems, whilst the fourth showed normal performance.

CONCLUSIONS

Our results support the link between clusters of maladaptive personality traits and distinctive profiles of SP deficits, which may inform research on disorders involving SP dysfunctions.

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology

Background

ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters.

Methods

The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics.

Conclusion

The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00457-3.

Distributed functional connectivity predicts neuropsychological test performance among older adults

Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.

Assessment of the structured clinical interview (SCID) for DSM-5 for somatic symptom disorder in general hospital outpatient clinics in China

BACKGROUND

It is still unknown whether the "Somatic symptom disorders (SSD) and related disorders" module of the Structured Clinical Interview for DSM-5, research version (SCID-5-RV), is valid in China. This study aimed to assess the SCID-5-RV for SSD in general hospital outpatient clinics in China.

METHODS

This multicentre cross-sectional study was conducted in the outpatient clinics of nine tertiary hospitals in Beijing, Jincheng, Shanghai, Wuhan, and Chengdu between May 2016 and March 2017. The "SSD and related disorders" module of the SCID-5-RV was translated, reversed-translated, revised, and used by trained clinical researchers to make a diagnosis of SSD. Several standardized questionnaires measuring somatic symptom severity, emotional distress, and quality of life were compared with the SCID-5-RV.

RESULTS

A total of 699 patients were recruited, and 236 were diagnosed with SSD. Of these patients, 46 had mild SSD, 78 had moderate SSD, 100 had severe SSD, and 12 were excluded due to incomplete data. The SCID-5-RV for SSD was highly correlated with somatic symptom severity, emotional distress, and quality of life (all P < 0.001) and could distinguish nonsevere forms of SSD from severe ones.

CONCLUSIONS

This study suggests that SCID-5-RV for SSD can distinguish SSD from non-SSD patients and severe cases from nonsevere cases. It has good discriminative validity and reflects the DSM-5 diagnostic approach that emphasizes excessive emotional, thinking, and behavioural responses related to symptoms.

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

Disruptive Mood Dysregulation Disorder: Symptomatic and Syndromic Thresholds and Diagnostic Operationalization

OBJECTIVE

To identify the most appropriate threshold for disruptive mood dysregulation disorder (DMDD) diagnosis and the impact of potential changes in diagnostic rules on prevalence levels in the community.

METHOD

Trained psychologists evaluated 3,562 preadolescents/early adolescents from the 2004 Pelotas Birth Cohort with the Development and Well-Being Behavior Assessment (DAWBA). The clinical threshold was assessed in 3 stages: symptomatic, syndromic, and clinical operationalization. The symptomatic threshold identified the response category in each DAWBA item, which separates normative misbehavior from a clinical indicator. The syndromic threshold identified the number of irritable mood and outbursts needed to capture preadolescents/early adolescents with high symptom levels. Clinical operationalization compared the impact of AND/OR rules for combining irritable mood and outbursts on impairment and levels of psychopathology.

RESULTS

At the symptomatic threshold, most irritable mood items were normative in their lowest response categories and clinically significant in their highest response categories. For outbursts, some indicated a symptom even when present at only a mild level, while others did not indicate symptoms at any level. At the syndromic level, a combination of 2 out of 7 irritable mood and 3 out of 8 outburst indicators accurately captured a cluster of individuals with high level of symptoms. Analysis combining irritable mood and outbursts delineated nonoverlapping aspects of DMDD, providing support for the OR rule in clinical operationalization. The best DMDD criteria resulted in a prevalence of 3%.

CONCLUSION

Results provide information for initiatives aiming to provide data-driven and clinically oriented operationalized criteria for DMDD.

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

BACKGROUND

In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data.

METHODS

In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants.

RESULTS

The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items.

CONCLUSION

Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

The relationship between demoralization and depressive symptoms among patients from the general hospital: network and exploratory graph analysis

INTRODUCTION

Depression and demoralization are highly prevalent among individuals with physical illnesses but their relationship is still unclear.

OBJECTIVE

To examine the relationship between clinical features of depression and demoralization with the network approach to psychopathology.

METHODS

Participants were recruited from the medical wards of a University Hospital in Italy. The Demoralization Scale (DS) was used to assess demoralization, while the Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms. The structure of the depression-demoralization symptom network was examined and complemented by the analysis of topological overlap and Exploratory Graph Analysis (EGA) to identify the most relevant groupings (communities) of symptoms and their connections. The stability of network models was estimated with bootstrap procedures and results were compared with factor analysis.

RESULTS

Life feeling pointless, low mood/discouragement, hopelessness and feeling trapped were among the most central features of the network. EGA identified four communities: (1) Neurovegetative Depression, (2) Loss of purpose, (3) Frustrated Isolation and (4) Low mood and morale. Loss of purpose and low mood/morale were largely connected with other communities through anhedonia, hopelessness and items related to isolation and lack of emotional control. Results from EGA displayed good stability and were comparable to those from factor analysis.

LIMITATIONS

Cross-sectional design; sample heterogeneity CONCLUSIONS: Among general hospital inpatients, features of depression and demoralization are independent, with the exception of low mood and self-reproach. The identification of symptom groupings around entrapment and helplessness may provide a basis for a dimensional characterization of depressed/demoralized patients, with possible implications for treatment.

Network Structure of Physical, Cognitive, and Emotional Symptoms at Preseason Baseline in Student Athletes with Attention-Deficit/ Hyperactivity Disorder

OBJECTIVE

Preexisting attention-deficit/hyperactivity disorder (ADHD) may be a risk factor for worse outcome following sport-related concussion. We used a statistical and psychometric approach known as network analysis to examine the architecture of physical, cognitive, and emotional symptoms at preseason baseline among student athletes with ADHD.

METHOD

A cohort of 44,527 adolescent student athletes completed baseline preseason testing with ImPACT® between 2009 and 2015. A subsample of athletes reporting a diagnosis of ADHD and at least one symptom were included in this study (N = 3,074; 14-18 years old, 32.7% girls). All participants completed the 22-item Post-Concussion Symptom Scale at preseason baseline.

RESULTS

Student athletes reported high frequencies of difficulty concentrating (boys/girls = 50.7%/59.4%), emotional symptoms (nervousness: boys/girls = 30.2%/51.0%; irritability: boys/girls = 23.6%/34.8%; sadness: boys/girls = 21.4%/39.7%), sleep/arousal-related symptoms (trouble falling asleep: boys/girls = 39.5%/49.4%; sleeping less than usual: boys/girls = 36.2%/43.4%; and fatigue: boys/girls = 29.8%/36.4%), and headaches (boys/girls = 27.6%/39.0%) during preseason baseline testing. The most central symptoms included dizziness, which was related to multiple somatic symptoms, and increased emotionality, which was related to a cluster of emotional symptoms. Girls reported symptoms at a greater frequency than boys, and there was evidence for variance in the global strength of the symptom network across gender, but not specific intersymptom relationships.

CONCLUSION

In the absence of injury, symptoms that commonly occur after concussion interact and potentially reinforce each other among student athletes with ADHD at preseason. Symptoms common in ADHD (i.e., difficulty concentrating) are not necessarily the most central within the symptom network. These findings may inform more precise interventions for athletes with ADHD and prolonged recovery following concussion.

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

The role of Experiential Avoidance in transdiagnostic compulsive behavior: A structural model analysis

Compulsivity is recognized as a transdiagnostic phenotype, underlying a variety of addictive and obsessive-compulsive behaviors. However, current understanding of how it should be operationalized and the processes contributing to its development and maintenance is limited. The present study investigated if there was a relationship between the affective process Experiential Avoidance (EA), an unwillingness to tolerate negative internal experiences, and the frequency and severity of transdiagnostic compulsive behaviors. A large sample of adults (N = 469) completed online questionnaires measuring EA, psychological distress and the severity of seven obsessive-compulsive and addiction-related behaviors. Using structural equation modelling, results indicated a one-factor model of compulsivity was superior to the two-factor model (addictive- vs OCD-related behaviors). The effect of EA on compulsivity was fully mediated by psychological distress, which in turn had a strong direct effect on compulsivity. This suggests distress is a key mechanism in explaining why people with high EA are more prone to compulsive behaviors. The final model explained 41% of the variance in compulsivity, underscoring the importance of these constructs as likely risk and maintenance factors for compulsive behavior. Implications for designing effective psychological interventions for compulsivity are discussed.

Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

The last series of Raven's standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.

Network Analyses of Maternal Pre- and Post-Partum Symptoms of Depression and Anxiety

BACKGROUND

Maternal mental health problems often develop prenatally and predict post-partum mental health. However, the circumstances before and following childbirth differ considerably. We currently lack an understanding of dynamic variation in the profiles of depressive and anxiety symptoms over the perinatal period.

METHODS

Depressive and anxiety symptoms were self-reported by 980 women at 26-week pregnancy and 3 months post-partum. We used network analysis of depressive and anxiety symptoms to investigate if the symptoms network changed during and after pregnancy. The pre- and post-partum depressive-anxiety symptom networks were assessed for changes in structure, unique symptom-symptom interactions, central and bridging symptoms. We also assessed if central symptoms had stronger predictive effect on offspring's developmental outcomes outcomes at birth and 24, 54, and 72 months old than non-central symptoms. Bridging symptoms between negative and positive mental health were also assessed.

RESULTS

Though the depressive-anxiety network structures were stable during and after pregnancy, the post-partum network was more strongly connected. The central depressive-anxiety symptoms were also different between prenatal and post-partum networks. During pregnancy, central symptoms were mostly related to feeling worthless or useless; after pregnancy, central symptoms were mostly related to feeling overwhelmed or being punished. Central symptoms during pregnancy were associated with poorer developmental outcomes for the child. Anxiety symptoms were strongest bridging symptoms during and after pregnancy. The interactions between negative and positive mental health symptoms were also different during and after pregnancy.

CONCLUSIONS

The differences between pre- and post-partum networks suggest that the presentation of maternal mental health problems varies over the peripartum period. This variation is not captured by traditional symptom scale scores. The bridging symptoms also suggest that anxiety symptoms may precede the development of maternal depression. Interventions and public health policies should thus be tailored to specific pre- and post-partum symptom profiles.

Study of the Factor Structure, Profiles, and Concurrent Validity of the Mindset Assessment Profile Tool for Elementary Students

This study explored the underlying latent structure of items on the Mindset Assessment Profile (MAP) tool, explored whether subgroups of students exist based on the latent structure of MAP items, and tested whether subgroups were differentiated on standardized measures of reading comprehension, vocabulary, and word reading. Participants included 431 fourth-grade students. Confirmatory factor analysis revealed that a three-factor model provided the most parsimonious fit to the data. Results of exploratory finite mixture model analysis with auxiliary regression suggested five classes of students, with the students categorized as growth mindset—high effort profile having the highest observed reading comprehension ( M = 451.98 and SD = 38.88) and vocabulary ( M = 454.37 and SD = 34.74) scores. By contrast, students categorized as fixed mindset—higher effort had the lowest observed reading comprehension and vocabulary scores. Limitations and directions for future research, and implications for using MAP assessment to inform intervention are discussed.

Dimensions over categories: a meta-analysis of taxometric research

Taxometric procedures have been used extensively to investigate whether individual differences in personality and psychopathology are latently dimensional or categorical ('taxonic'). We report the first meta-analysis of taxometric research, examining 317 findings drawn from 183 articles that employed an index of the comparative fit of observed data to dimensional and taxonic data simulations. Findings supporting dimensional models outnumbered those supporting taxonic models five to one. There were systematic differences among 17 construct domains in support for the two models, but psychopathology was no more likely to generate taxonic findings than normal variation (i.e. individual differences in personality, response styles, gender, and sexuality). No content domain showed aggregate support for the taxonic model. Six variables - alcohol use disorder, intermittent explosive disorder, problem gambling, autism, suicide risk, and pedophilia - emerged as the most plausible taxon candidates based on a preponderance of independently replicated findings. We also compared the 317 meta-analyzed findings to 185 additional taxometric findings from 96 articles that did not employ the comparative fit index. Studies that used the index were 4.88 times more likely to generate dimensional findings than those that did not after controlling for construct domain, implying that many taxonic findings obtained before the popularization of simulation-based techniques are spurious. The meta-analytic findings support the conclusion that the great majority of psychological differences between people are latently continuous, and that psychopathology is no exception.

Can network analysis shed light on predictors of lithium response in bipolar I disorder?

OBJECTIVE

To undertake a large-scale clinical study of predictors of lithium (Li) response in bipolar I disorder (BD-I) and apply contemporary multivariate approaches to account for inter-relationships between putative predictors.

METHODS

We used network analysis to estimate the number and strength of connections between potential predictors of good Li response (measured by a new scoring algorithm for the Retrospective Assessment of Response to Lithium Scale) in 900 individuals with BD-I recruited to the Consortium of Lithium Genetics.

RESULTS

After accounting for co-associations between potential predictors, the most important factors associated with the good Li response phenotype were panic disorder, manic predominant polarity, manic first episode, age at onset between 15-32 years and family history of BD. Factors most strongly linked to poor outcome were comorbid obsessive-compulsive disorder, alcohol and/or substance misuse, and/or psychosis (symptoms or syndromes).

CONCLUSIONS

Network analysis can offer important additional insights to prospective studies of predictors of Li treatment outcomes. It appears to especially help in further clarifying the role of family history of BD (i.e. its direct and indirect associations) and highlighting the positive and negative associations of different subtypes of anxiety disorders with Li response, particularly the little-known negative association between Li response and obsessive-compulsive disorder.

Architecture of Physical, Cognitive, and Emotional Symptoms at Preseason Baseline in Adolescent Student Athletes With a History of Mental Health Problems

Objective: Pre-injury mental health problems are associated with greater symptom reporting following sport-related concussion. We applied a statistical and psychometric approach known as network analysis to examine the interrelationships among symptoms at baseline in adolescent student athletes with a history of mental health problems. Design: Cross-sectional study. Setting: High schools in Maine, USA. Participants: A cohort of 44,527 adolescent student athletes completed baseline preseason testing with ImPACT® between 2009 and 2015, and those with a history of mental health problems reporting at least one symptom were included (N = 2,412; 14-18 years-old, 60.1% girls). Independent Variables: Self-reported history of treatment for a psychiatric condition. Main Outcome Measures: Physical, cognitive, and emotional symptoms from the Post-Concussion Symptom Scale. Results: Student athletes reported high frequencies of emotional symptoms (nervousness: boys = 46.6%, girls = 58.3%; irritability: boys = 37.9%, girls = 46.9%; sadness: boys = 38.7%, girls = 53.2%), sleep/arousal-related symptoms (trouble falling asleep: boys = 50.4%, girls = 55.1%; sleeping less than usual: boys = 43.8%, girls = 45.2%; and fatigue: boys = 40.3%, girls = 45.2%), headaches (boys = 27.5%, girls = 41.8%), and inattention (boys = 47.8%, girls = 46.9%) before the start of the season. Although uncommonly endorsed, dizziness was the most central symptom (i.e., the symptom with the highest aggregate connectedness with different symptoms in the network), followed by feeling more emotional and feeling slowed down. Dizziness was related to physical and somatic symptoms (e.g., balance, headache, nausea, numbness/tingling) whereas increased emotionality was related to sadness, nervousness, and irritability. Feeling slowed down was connected to cognitive (e.g., fogginess, forgetfulness), and sensory symptoms (e.g., numbness/tingling, light sensitivity). There were no gender differences in the symptom network structure. Conclusions: We examined the interconnections between symptoms reported by student athletes with mental health problems at preseason baseline, identifying how physical, cognitive, and emotional symptoms interact and potentially reinforce each other in the absence of injury. These findings are a step toward informing more precise interventions for this subgroup of athletes if they are slow to recover following concussion.

Longitudinal subtypes of disordered gambling in young adults identified using mixed modeling

OBJECTIVE

While many individuals gamble responsibly, some develop maladaptive symptoms of a gambling disorder. Gambling problems often first occur in young people, yet little is known about the longitudinal course of such symptoms and whether this course can be predicted. The aim of this study was to identify latent subtypes of disordered gambling based on symptom presentation and identify predictors of persisting gambling symptoms over time.

METHODS

575 non-treatment seeking young adults (mean age [SD] = 22.3 [3.6] years; 376 (65.4%) male) were assessed at baseline and annually, over three years, using measures of gambling severity. Latent subtypes of gambling symptoms were identified using latent mixture modeling. Baseline differences were characterized using analysis of variance and binary logistic regression respectively.

RESULTS

Three longitudinal phenotypes of disordered gambling were identified: high harm group (N = 5.6%) who had moderate-severe gambling disorder at baseline and remained symptomatic at follow-up; intermediate harm group (19.5%) who had problem gambling reducing over time; and low harm group (75.0%) who were essentially asymptomatic. Compared to the low harm group, the other two groups had worse baseline quality of life, elevated occurrence of other mental disorders and substance use, higher body mass indices, and higher impulsivity, compulsivity, and cognitive deficits. Approximately 5% of the total sample showed worsening of gambling symptoms over time, and this rate did not differ significantly between the groups.

CONCLUSIONS

Three subtypes of disordered gambling were found, based on longitudinal symptom data. Even the intermediate gambling group had a profundity of psychopathological and untoward physical health associations. Our data indicate the need for large-scale international collaborations to identify predictors of clinical worsening in people who gamble, across the full range of baseline symptom severity from minimal to full endorsement of current diagnostic criteria for gambling disorder.