Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Psychosis superspectrum II: neurobiology, treatment, and implications

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Psychosis superspectrum I: Nosology, etiology, and lifespan development

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study

BACKGROUND

Polygenic risk scores (PRSs) capture genetic vulnerability to psychiatric conditions. However, PRSs are often associated with multiple mental health problems in children, complicating their use in research and clinical practice. The current study is the first to systematically test which PRSs associate broadly with all forms of childhood psychopathology, and which PRSs are more specific to one or a handful of forms of psychopathology.

METHODS

The sample consisted of 4717 unrelated children (mean age = 9.92, s.d. = 0.62; 47.1% female; all European ancestry). Psychopathology was conceptualized hierarchically as empirically derived general factor (p-factor) and five specific factors: externalizing, internalizing, neurodevelopmental, somatoform, and detachment. Partial correlations explored associations between psychopathology factors and 22 psychopathology-related PRSs. Regressions tested which level of the psychopathology hierarchy was most strongly associated with each PRS.

RESULTS

Thirteen PRSs were significantly associated with the general factor, most prominently Chronic Multisite Pain-PRS (r = 0.098), ADHD-PRS (r = 0.079), and Depression-PRS (r = 0.078). After adjusting for the general factor, Depression-PRS, Neuroticism-PRS, PTSD-PRS, Insomnia-PRS, Chronic Back Pain-PRS, and Autism-PRS were not associated with lower order factors. Conversely, several externalizing PRSs, including Adventurousness-PRS and Disinhibition-PRS, remained associated with the externalizing factor (|r| = 0.040-0.058). The ADHD-PRS remained uniquely associated with the neurodevelopmental factor (r = 062).

CONCLUSIONS

PRSs developed to predict vulnerability to emotional difficulties and chronic pain generally captured genetic risk for all forms of childhood psychopathology. PRSs developed to predict vulnerability to externalizing difficulties, e.g. disinhibition, tended to be more specific in predicting behavioral problems. The results may inform translation of existing PRSs to pediatric research and future clinical practice.

Pleasant and unpleasant odor identification ability is associated with distinct dimensions of negative symptoms transdiagnostically in psychotic disorders

Negative symptoms are among the greatest sources of functional impairment for individuals with schizophrenia, yet their mechanisms remain poorly understood. Olfactory impairment is associated with negative symptoms. The processing of pleasant olfactory stimuli is subserved by reward-related neural circuitry while unpleasant olfactory processing is subserved by emotion-related neural circuitry, suggesting that these two odor dimensions may offer a window into differential mechanisms of negative symptoms. We examined whether pleasant and unpleasant odor identification bears differential relationships with avolition and inexpressivity dimensions of negative symptoms, whether these relationships are transdiagnostic, and whether pleasant and unpleasant odor processing also relate differently to other domains of functioning in a sample of individuals diagnosed with schizophrenia (N = 54), other psychotic disorders (N = 65), and never-psychotic adults (N = 160). Hierarchical regressions showed that pleasant odor identification was uniquely associated with avolition, while unpleasant odor identification was uniquely associated with inexpressivity. These relationships were largely transdiagnostic across groups. Additionally, pleasant and unpleasant odor identification displayed signs of specificity with other functional and cognitive measures. These results align with past work suggesting dissociable pathomechanisms of negative symptoms and provide a potential avenue for future work using valence-specific olfactory dysfunction as a semi-objective and low-cost marker for understanding and predicting the severity of specific negative symptom profiles.

Dynamic interplay between life events and course of psychotic disorders: 10-year longitudinal study following first admission

BACKGROUND

Life events (LEs) are a risk factor for first onset and relapse of psychotic disorders. However, the impact of LEs on specific symptoms - namely reality distortion, disorganization, negative symptoms, depression, and mania - remains unclear. Moreover, the differential effects of negative v. positive LEs are poorly understood.

METHODS

The present study utilizes an epidemiologic cohort of patients (N = 428) ascertained at first-admission for psychosis and followed for a decade thereafter. Symptoms were assessed at 6-, 24-, 48-, and 120-month follow-ups.

RESULTS

We examined symptom change within-person and found that negative events in the previous 6 months predicted an increase in reality distortion (β = 0.07), disorganized (β = 0.07), manic (β = 0.08), and depressive symptoms (β = 0.06), and a decrease in negative symptoms (β = -0.08). Conversely, positive LEs predicted fewer reality distortion (β = -0.04), disorganized (β = -0.04), and negative (β = -0.13) symptoms, and were unrelated to mood symptoms. A between-person approach to the same hypotheses confirmed that negative LEs predicted change in all symptoms, while positive LEs predicted change only in negative symptoms. In contrast, symptoms rarely predicted future LEs.

CONCLUSIONS

These findings confirm that LEs have an effect on symptoms, and thus contribute to the burden of psychotic disorders. That LEs increase positive symptoms and decrease negative symptoms suggest at least two different mechanisms underlying the relationship between LEs and symptoms. Our findings underscore the need for increased symptom monitoring following negative LEs, as symptoms may worsen during that time.

The Hierarchical Taxonomy of Psychopathology (HiTOP) in psychiatric practice and research

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Development of the Thought Disorder Measure for the Hierarchical Taxonomy of Psychopathology

The Hierarchical Taxonomy of Psychopathology consortium aims to develop a comprehensive self-report measure to assess psychopathology dimensionally. The current research describes the initial conceptualization, development, and item selection for the thought disorder spectrum and related constructs from other spectra. The thought disorder spectrum is defined primarily by the positive and disorganized traits and symptoms of schizophrenia-spectrum disorders. The Thought Disorder Sub-Workgroup identified and defined 16 relevant constructs and wrote 10 to 15 items per each construct. These items were administered, along with detachment and mania items, to undergraduates and people with serious mental illness. Three hundred and sixty-five items across 25 scales were administered. An exploratory factor analysis of the scale scores suggested a two-factor structure corresponding to positive and negative symptoms for two samples. The mania scales loaded with the positive factor, while the detachment scales loaded with the negative factor. Item-level analyses resulted in 19 preliminary scales, including 215 items that cover the range of thought disorder pathology, and will be carried forward for the next phase of data collection/analysis.

Global Information for Multidimensional Tests

New measures of test information, termed global information, quantify test information relative to the entire range of the trait being assessed. Estimating global information relative to a non-informative prior distribution results in a measure of how much information could be gained by administering the test to an unspecified examinee. Currently, such measures have been developed only for unidimensional tests. This study introduces measures of multidimensional global test information and validates them in simulated data. Then, the utility of global test information is tested in neuropsychological data collected as part of Rush University's Memory and Aging Project. These measures allow for direct comparison of complex tests calibrated in different samples, facilitating test development and selection.

A comparison of cognitive performance in the Suffolk County cohort and their unaffected siblings

People diagnosed with schizophrenia and other psychoses demonstrate impaired neuropsychological performance. Their unaffected siblings exhibit mild impairments relative to unrelated controls, suggesting genetic and shared environmental risk for psychosis account for some portion of cognitive impairments observed in cases. However, most sibling studies were conducted early in illness course. Studying cases and unaffected siblings later in life is valuable because diagnostic misclassification is common early in illness, possibly leading to spurious conclusions. This study compared neuropsychological performance of individuals with psychotic disorders (schizophrenia and other psychoses), their unaffected siblings, and controls. Assessments were conducted 20 years after case enrollment in the Suffolk County Mental Health Project, when siblings and controls were added to the protocol. Results showed individuals with schizophrenia and other psychoses performed worse than their matched siblings across domains. Relative to controls, siblings of participants with schizophrenia showed mild deficits in executive function and processing speed, while no significant differences were observed between siblings of those with other psychoses and controls. These findings suggest pre- and post-onset factors impact cognitive deficits in psychosis, but pre-onset factors are more salient in schizophrenia. Additionally, schizophrenia and other psychoses exist on a neurodevelopmental continuum, with schizophrenia being a more severe manifestation.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

Les progrès dans la réalisation de la classification quantitative de la psychopathologie☆

Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad "spectrum level" dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the "problem of comorbidity" by explicitly modeling patterns of co-occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis-related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

Lead-Time Bias Confounds Association Between Duration of Untreated Psychosis and Illness Course in Schizophrenia

OBJECTIVE

At first hospitalization, a long duration of untreated psychosis (DUP) predicts illness severity and worse treatment outcomes. The mechanism of this association, however, remains unclear. It has been hypothesized that lengthy untreated psychosis is toxic or that it reflects a more severe form of schizophrenia. Alternatively, the association may be an artifact of lead-time bias. These hypotheses are tested in a longitudinal study of schizophrenia with 2,137 observations spanning from childhood to 20 years after first admission.

METHODS

Data were from the Suffolk County Mental Health Project. The cohort included 287 individuals with schizophrenia or schizoaffective disorder. DUP was defined as days from first psychotic symptom to first psychiatric hospitalization. Psychosocial function was assessed using the Premorbid Adjustment Scale and the Global Assessment of Functioning Scale. Psychosocial function trajectories were estimated using multilevel spline regression models adjusted for gender, occupational status, race, and antipsychotic medication.

RESULTS

Both long- and short-DUP patients experienced similar declines in psychosocial function, but declines occurred at different times relative to first admission. Long-DUP patients experienced most of these declines prior to first admission, while short-DUP patients experienced declines after first admission. When psychosocial function was analyzed relative to psychosis onset, DUP did not predict illness course.

CONCLUSIONS

The association between DUP and psychosocial function may be an artifact of early detection, creating the illusion that early intervention is associated with improved outcomes. In other words, DUP may be better understood as an indicator of illness stage than a predictor of course.

Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology

Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Common Taxonomy of Traits and Symptoms: Linking Schizophrenia Symptoms, Schizotypy, and Normal Personality

The associations among normal personality and many mental disorders are well established, but it remains unclear whether and how symptoms of schizophrenia and schizotypal traits align with the personality taxonomy. This study examined the joint factor structure of normal personality, schizotypy, and schizophrenia symptoms in people with psychotic disorders (n = 288) and never-psychotic adults (n = 257) in the Suffolk County Mental Health Project. First, we evaluated the structure of schizotypal (positive schizotypy, negative schizotypy, and mistrust) and normal traits. In both the psychotic-disorder and never-psychotic groups, the best-fitting model had 5 factors: neuroticism, extraversion, conscientiousness, agreeableness, and psychoticism. The schizotypy traits were placed on different dimensions: negative schizotypy went on (low) extraversion, whereas positive schizotypy and mistrust went on psychoticism. Next, we added symptoms to the model. Numerous alternatives were compared, and the 5-factor model remained best-fitting. Reality distortion (hallucinations and delusions) and disorganization symptoms were placed on psychoticism, and negative symptoms were placed on extraversion. Models that separated symptom dimensions from trait dimensions did not fit well, arguing that taxonomies of symptoms and traits are aligned. This is the first study to show that symptoms of psychosis, schizotypy, and normal personality reflect the same underlying dimensions. Specifically, (low) extraversion, negative schizotypy, and negative symptoms form one spectrum, whereas psychoticism, positive schizotypy, and positive and disorganized symptoms form another. This framework helps to understand the heterogeneity of psychosis and comorbidity patterns found in psychotic disorders. It also underscores the importance of traits to understanding these disorders.

Are fit indices used to test psychopathology structure biased? A simulation study

Structural models of psychopathology provide dimensional alternatives to traditional categorical classification systems. Competing models, such as the bifactor and correlated factors models, are typically compared via statistical indices to assess how well each model fits the same data. However, simulation studies have found evidence for probifactor fit index bias in several psychological research domains. The present study sought to extend this research to models of psychopathology, wherein the bifactor model has received much attention, but its susceptibility to bias is not well characterized. We used Monte Carlo simulations to examine how various model misspecifications produced fit index bias for 2 commonly used estimators, WLSMV and MLR. We simulated binary indicators to represent psychiatric diagnoses and positively skewed continuous indicators to represent symptom counts. Across combinations of estimators, indicator distributions, and misspecifications, complex patterns of bias emerged, with fit indices more often than not failing to correctly identify the correlated factors model as the data-generating model. No fit index emerged as reliably unbiased across all misspecification scenarios. Although, tests of model equivalence indicated that in one instance fit indices were not biased-they favored the bifactor model, albeit not unfairly. Overall, results suggest that comparisons of bifactor models to alternatives using fit indices may be misleading and call into question the evidentiary meaning of previous studies that identified the bifactor model as superior based on fit. We highlight the importance of comparing models based on substantive interpretability and their utility for addressing study aims, the methodological significance of model equivalence, as well as the need for implementation of statistical metrics that evaluate model quality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research

For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

Modeling Response Style Using Vignettes and Person-Specific Item Response Theory

Responses to survey data are determined not only by item characteristics and respondents' trait standings but also by response styles. Recently, methods for modeling response style with personality and attitudinal data have turned toward the use of anchoring vignettes, which provide fixed rating targets. Although existing research is promising, a few outstanding questions remain. First, it is not known how many vignettes and vignette ratings are necessary to identify response style parameters. Second, the comparative accuracy of these models is largely unexplored. Third, it remains unclear whether correcting for response style improves criterion validity. Both simulated data and data observed from a population-representative sample responding to a measure of personality pathology (the Personality Inventory for DSM-5 [PID-5]) were modeled using an array of response style models. In simulations, most models estimating response styles outperformed the graded response model (GRM), and in observed data, all response style models were superior to the GRM. Correcting for response style had a small, but in some cases significant, effect on the prediction of self-reported social dysfunction.

Progress in achieving quantitative classification of psychopathology

Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad "spectrum level" dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the "problem of comorbidity" by explicitly modeling patterns of co-occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach.

Enhancing Psychosis-Spectrum Nosology Through an International Data Sharing Initiative

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.

Structure as cause and representation: Implications of descriptivist inference for structural modeling across multiple levels of analysis

What does a structural model reflect? Different answers to this question implicitly underlie different nosological paradigms. Traditionally, structural analysis has been seen as a process of identifying true or causative values, states, or conditions. This paradigm has faced mounting challenges, however, as psychopathology theory and research has come to encompass different levels of analysis, with concomitant questions about what constructs are most "correct." Here, we discuss an alternative descriptivist paradigm, in which models are seen as the process of identifying optimally parsimonious, generalizable representations of observations. This paradigm allows for an integration of theoretical and methodological approaches that are often seen in mutual opposition, and recasts traditional measurement and structural models in a new light. In this article, we explain the descriptivist perspective, illustrating important concepts using empirical examples from the Human Connectome Project and this issue. We address structural theory within the context of varying levels of analysis, demonstrating how the descriptivist approach can elucidate the nature of hierarchical features and provide a framework for empirically delineating psychopathology structure. (PsycINFO Database Record

Modeling trait depression amplifies the effect of childbearing on postpartum depression

BACKGROUND

The literature on the relative risk for depression in the postpartum period has largely focused on state (or episodic) depression, and has not addressed trait depression (a woman's general tendency to experience depressed mood). The present study evaluates the association between childbirth and depression in the postpartum period, taking into account the role of stable differences in women's vulnerability for depression across a 10-year span.

METHODS

Data from the National Longitudinal Survey of Youth 1997 Cohort (N = 4385) were used. The recency of childbirth was used as a predictor of state depression in two models: one that modeled stable depressive symptoms over time (a multi-state single-trait model; LST), and one that did not (an autoregressive cross-lagged model; ARM).

RESULTS

Modeling trait depression, in addition to state depression, improved model fit and had the effect of increasing the magnitude of the association between childbirth and state depression in the postpartum period.

LIMITATIONS

The secondary nature of the data limited the complexity of analyses (e.g., models with multivariate predictors were not possible), as the data were not collected with the present study in mind.

CONCLUSIONS

These findings may reflect the fact that some of the covariance between childbirth and episodic depression is obscured by the effect of trait depression, and it is not until trait depression is explicitly modeled that the magnitude of the relationship between childbirth and depression becomes clear.

A descriptivist approach to trait conceptualization and inference

In their recent article, How Functionalist and Process Approaches to Behavior Can Explain Trait Covariation, Wood, Gardner, and Harms (2015) underscore the need for more process-based understandings of individual differences. At the same time, the article illustrates a common error in the use and interpretation of latent variable models: namely, the misuse of models to arbitrate issues of causation and the nature of latent variables. Here, we explain how latent variables can be understood simply as parsimonious summaries of data, and how statistical inference can be based on choosing those summaries that minimize information required to represent the data using the model. Although Wood, Gardner, and Harms acknowledge this perspective, they underestimate its significance, including its importance to modeling and the conceptualization of psychological measurement. We believe this perspective has important implications for understanding individual differences in a number of domains, including current debates surrounding the role of formative versus reflective latent variables.

A meta-analytic evaluation of the endophenotype hypothesis: effects of measurement paradigm in the psychiatric genetics of impulsivity

Recent transitions in psychiatric nosology have stimulated discussion about what constructs and what level of analysis are most appropriate for the study of psychopathology. The endophenotype hypothesis suggests that neurobiological and neuropsychological phenotypes will be superior to trait or diagnostic measures in elucidating the substrates of psychopathology, as the former are more proximal, and therefore more sensitive, to underlying etiology. This meta-analysis explores these issues by comparing the magnitude of genetic effects associated with phenotypes at different levels of analysis. Studies of 3 common polymorphisms-the short and long variants of the serotonin-transporter-linked polymorphic region (5-HTTLPR), the variable number tandem repeat polymorphism in the 3' untranslated region of the dopamine active transporter gene (DAT1 3' UTR VNTR), and the 48 base-pair VNTR in exon-3 of the dopamine D4 receptor gene (DRD4)-and their effects on phenotypes of impulsivity were examined. Consistent with endophenotype theory, level of phenotype moderated the magnitude of genetic effects. Diagnostic, trait and neuropsychological, then neurobiological phenotypes yielded successively larger effects. However, consistent with emerging meta-analytic findings, neurobiological phenotypes were most susceptible to bias and inflation, raising questions about the validity of reported effects.

A model of psychosis and its relationship with impairment

PURPOSE

Some studies suggest that positive symptoms of psychosis-clinical and sub-clinical alike-reflect a single, continuously distributed dimension in the population. It is unknown, however, whether such a spectrum of positive psychotic experiences is non-linearly related to outcomes such as daily functioning. This work aims to characterize the relationship between positive psychosis and impairment.

METHODS

Data from the Office of National Statistics National Psychiatric Morbidity Surveys of Great Britain were used to establish measurement models of psychosis and impairment. Competing linear and nonlinear models of the relationship between the two latent variables were evaluated using mixture structural equation models.

RESULTS

Positive psychosis is best modeled by a continuous, normal distribution. Increases in positive psychosis correlate with roughly linear increases in impairment.

CONCLUSIONS

Positive psychotic symptoms occur throughout the population without a discrete, pathological threshold. Functional deficits are linearly associated with the psychosis at all points along the continuum, and a significant portion of the population experiences subclinical psychosis.