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Mental health for cancer survivors in both research and clinical applications has strongly adopted a traditional nosological approach, involving the classification of psychopathology into discrete disorders. However, this approach has recently faced considerable criticism due to issues such as high comorbidity and within-disorder symptom heterogeneity across populations. Moreover, there are additional specific issues impacting the validity of traditional approaches in cancer survivorship populations, including the physiological effects of cancer and its treatments. In response, we provide the case for the hierarchical dimensional approach within psycho-oncology, in particular the Hierarchical Taxonomy of Psychopathology (HiTOP). We discuss not only the potential utility of HiTOP to research and clinical applications within psycho-oncology, but also its limitations, and what is required to apply this approach within cancer survivorship., Competing Interests: Declaration of interests D.H., R.K., R.F.K., A.G.C.W., and M.K.F. are members of the HiTOP Consortium. Otherwise, the authors declare no conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Quantitative, empirical approaches to establishing the structure of psychopathology hold promise to improve on traditional psychiatric classification systems. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a framework that summarizes the substantial and growing body of quantitative evidence on the structure of psychopathology. To achieve its aims, HiTOP must incorporate emerging research in a systematic, ongoing fashion. In this article, we describe the historical context and grounding of the principles and procedures for revising the HiTOP framework. Informed by strengths and shortcomings of previous classification systems, the proposed revisions protocol is a formalized system focused around three pillars: (a) prioritizing systematic evaluation of quantitative evidence by a set of transparent criteria and processes, (b) balancing stability with flexibility, and (c) promoting inclusion over gatekeeping in all aspects of the process. We detail how the revisions protocol will be applied in practice, including the scientific and administrative aspects of the process. Additionally, we describe areas of the HiTOP structure that will be a focus of early revisions and outline challenges for the revisions protocol moving forward. The proposed revisions protocol is designed to ensure that the HiTOP framework reflects the current state of scientific knowledge on the structure of psychopathology and fulfils its potential to advance clinical research and practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Confirmatory factor analysis (CFA) and its bifactor models are popular in empirical investigations of the factor structure of psychological constructs. CFA offers straightforward hypothesis testing but has notable pitfalls, such as the imposition of strict assumptions (i.e., simple structure) that obscure unmodeled complexity. Due to the limitations of bifactor CFAs, they have yielded anomalous results across samples and studies that suggest model misspecification (e.g., evaporating specific factors and unexpected loadings). We propose the use of exploratory factor analysis (EFA) to evaluate the structural validity of CFA solutions-either before or after the estimation of more restrictive CFA models-to (a) identify model misspecifications that may drive anomalous estimates and (b) confirm CFA models by examining whether hypothesized structures emerge with limited researcher input. We evaluated the degree to which predominant factor structures were invariant across contexts along the exploratory-confirmatory continuum and demonstrate how poor methodological choices can distort results and impede theory development. All CFA models fit well, but there were numerous differences in replicability and substantive interpretability. Several similarities emerged between bifactor CFA and EFA models, including evidence of overextraction, the collapse of specific factors onto the general factor, and subsequent shifts in how the general factor was defined. We situate these methodological shortcomings within the broader literature on structural models of psychopathology, articulate implications for theories (such as the p -factor) that are borne out of factor analysis, outline several remedies for problems encountered when performing exploratory bifactor analysis, and propose alternative specifications for confirmatory bifactor models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a novel diagnostic system grounded in empirical research into the architecture of mental illness. Its basic units are continuous dimensions-as opposed to categories-that are organized into a hierarchy according to patterns of symptom co-occurrence observed in quantitative studies. Previous HiTOP discussions have focused on existing evidence regarding the model's structure and ability to account for neurobiological, social, cultural, and clinical variation. The present article looks ahead to the next decade of applied research and clinical practice using the HiTOP rubric. We highlight 10 topics where HiTOP has the potential to make significant breakthroughs. Research areas include genetic influences, environmental contributions, neural mechanisms, real-time dynamics, and lifespan development of psychopathology. We also discuss development of novel assessments, forecasting methods, and treatments. Finally, we consider implications for clinicians and educators. For each of these domains, we propose directions for future research and venture hypotheses as to what HiTOP will reveal about psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: Most psychiatric disorders are associated with several risk factors, but a few underlying psychopathological dimensions account for the common co-occurrence of disorders. If these underlying psychopathological dimensions mediate associations of the risk factors with psychiatric disorders, it would support a trans-diagnostic orientation to etiological research and treatment development., Method: An analysis was performed of the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions III (NESARC-III), a US nationally representative sample of non-institutionalized civilian adults, focusing on respondents who were aged ⩾21 ( n = 34 712). Structural equation modeling was used to identify the psychopathological dimensions underlying psychiatric disorders; to examine associations between risk factors, psychopathological dimensions and individual disorders; and to test whether associations of risk factors occurring earlier in life were mediated by risk factors occurring later in life., Results: A bifactor model of 13 axis I disorders provided a good fit (CFI = 0.987, TLI = 0.982, and RMSEA = 0.011) including an overall psychopathology factor as measured by all 13 disorders and 2 specific factors, one for externalizing disorders and one for fear-related disorders. A substantial proportion of the total effects of the risk factors occurring early in life were indirectly mediated through factors occurring later in life. All risk factors showed a significant total effect on the general psychopathology, externalizing and fear-related factors. Only 23 of 325 direct associations of risk factors with psychiatric disorders achieved statistical significance., Conclusion: Most risk factors for psychiatric disorders are mediated through broad psychopathological dimensions. The central role of these dimensions supports trans-diagnostic etiological and intervention research.

Explicitly or implicitly, psychopathology is often defined in terms of statistical deviance, requiring that an affected individual be sufficiently distant from the norm in some dimension of psychological or neural function. In recent decades, the dominant paradigm in psychiatric research has focused primarily on deviance in neural function, treating psychopathology as disease of the brain. We argue that these conceptualizations are misguided. We recently proposed a novel theory of psychopathology, based in cybernetics and drawing additionally from neuroscience, psychometrics, and personality theory (DeYoung & Krueger, 2018a). In this theory, deviations from the norm in psychological and neural functioning serve as important risk factors for psychopathology but are not in themselves necessary or sufficient to identify psychopathology, which requires the presence of cybernetic dysfunction. Psychopathology is defined as persistent failure to move toward one's goals, due to failure to generate effective new goals, interpretations, or strategies when existing ones prove unsuccessful. We argue that adopting a cybernetic theory to replace conceptualizations of psychopathology as statistical deviance or brain disease would facilitate improvements in measurement, diagnosis, prevention, and treatment of psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

As evidenced by our exchange with Bader and Moshagen (2022), the degree to which model fit indices can and should be used for the purpose of model selection remains a contentious topic. Here, we make three core points. First, we discuss the common misconception about fit statistics' abilities to identify the "best model," arguing that mechanical application of model fit indices contributes to faulty inferences in the field of quantitative psychopathology. We illustrate the consequences of this practice through examples in the literature. Second, we highlight the parsimony-adjacent concept of fitting propensity, which is not accounted for by commonly used fit statistics. Finally, we present specific strategies to overcome interpretative bias and increase generalizability of study results and stress the importance of carefully balancing substantive and statistical criteria in model selection scenarios. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Traditional diagnostic systems went beyond empirical evidence on the structure of mental health. Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited. The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence. It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability. We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework. The system is not yet comprehensive, and we describe the processes for improving and expanding it. We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response. We describe progress in the development of HiTOP-based measures and in clinical implementation of the system. Finally, we review outstanding challenges and the research agenda. HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology.

Background: Neurocognitive deficits are common among youth with mental disorders, and patterns of aberrant brain function generally cross diagnostic boundaries. This study investigated associations between functional neurocircuitry and broad transdiagnostic psychopathology dimensions in the critical preadolescent period when psychopathology is emerging., Methods: Participants were 9- to 10-year-olds from the Adolescent Brain Cognitive Development Study. Factor scores of general psychopathology, externalizing, internalizing, and thought disorder dimensions were calculated from a higher-order model of psychopathology using confirmatory factor analysis (N = 11,721) and entered as explanatory variables into linear mixed models to examine associations with resting-state functional connectivity (n = 9074) and neural activation during the emotional n-back task (n = 6146) when covarying for sex, race/ethnicity, parental education, and cognitive function., Results: All dimensions of psychopathology were commonly characterized by hypoconnectivity within the dorsal attention and retrosplenial-temporal networks, hyperconnectivity between the frontoparietal and ventral attention networks and between the dorsal attention network and amygdala, and hypoactivation of the caudal middle frontal gyrus. Externalizing pathology was uniquely associated with hyperconnectivity between the salience and ventral attention networks and hyperactivation of the cingulate and striatum. Internalizing pathology was uniquely characterized by hypoconnectivity between the default mode and cingulo-opercular networks. Connectivity between the cingulo-opercular network and putamen was uniquely higher for internalizing pathology and lower for thought disorder pathology., Conclusions: These findings provide novel evidence that broad psychopathology dimensions are characterized by common and dissociable patterns, particularly for externalizing pathology, of functional connectivity and task-evoked activation throughout neurocognitive networks in preadolescence., (Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

The present study compared the primary models used in research on the structure of psychopathology (i.e., correlated factor, higher-order, and bifactor models) in terms of structural validity (model fit and factor reliability), longitudinal measurement invariance, concurrent and prospective predictive validity in relation to important outcomes, and longitudinal consistency in individuals' factor score profiles. Two simpler operationalizations of a general factor of psychopathology were also examined-a single-factor model and a count of diagnoses. Models were estimated based on structured clinical interview diagnoses in two longitudinal waves of nationally representative data from the United States ( n = 43,093 and n = 34,653). Models that included narrower factors (fear, distress, and externalizing) were needed to capture the observed multidimensionality of the data. In the correlated factor and higher-order models these narrower factors were reliable, largely invariant over time, had consistent associations with indicators of adaptive functioning, and had moderate stability within individuals over time. By contrast, the fear- and distress-specific factors in the bifactor model did not show good reliability or validity throughout the analyses. Notably, the general factor of psychopathology ( p factor) performed similarly well across tests of reliability and validity regardless of whether the higher-order or bifactor model was used; the simplest (single factor) model was also comparable across most tests, with the exception of model fit. Given the limitations of categorical diagnoses, it will be important to repeat these analyses using dimensional measures. We conclude that when aiming to understand the structure and correlates of psychopathology it is important to (a) look beyond model fit indices to choose between different models, (b) examine the reliability of latent variables directly, and (c) be cautious when isolating and interpreting the unique effects of specific psychopathology factors, regardless of which model is used. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Most psychiatric disorders, when examined individually, are associated with a broad range of adverse outcomes. However, psychiatric disorders often co-occur and their co-occurrence is well explained by a limited number of transdiagnostic factors. Yet it remains unclear whether the risk of these adverse outcomes is due to specific psychiatric disorders, specific dimensions of psychopathology (i.e., internalizing and externalizing dimensions), a general psychopathology factor, or a combination of these explanations. In a large nationally representative prospective survey, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), we used structural equation modeling to examine the shared and specific effects of common Axis I and Axis II disorders on the risk of ten adverse outcomes (unemployment; financial crisis; low income; poorer general health; worse mental and physical health; legal problems; divorce; problems with a neighbor, friend, or relative; and violence) in the general adult population. Effects of psychiatric disorders were exerted mostly through a general psychopathology factor representing the shared effect across all disorders, independent of sociodemographic characteristics and the presence of the adverse outcomes at baseline. Violence and legal problems were further associated with the externalizing factor, but there were no independent associations of the internalizing factor or any individual psychiatric disorders with any of the adverse outcomes. Our findings reveal that associations between psychiatric disorders and adverse outcomes occur through broad psychological dimensions. Understanding the biological and psychological mechanisms underlying these dimensions should yield key intervention targets to decrease the individual suffering and societal burden associated with common psychiatric disorders.

We recently wrote an article comparing the conclusions that followed from two different approaches to quantifying the reliability and replicability of psychopathology symptom networks. Two commentaries on the article have raised five core criticisms, which are addressed in this response with supporting evidence. 1) We did not over-generalize about the replicability of symptom networks, but rather focused on interpreting the contradictory conclusions of the two sets of methods we examined. 2) We closely followed established recommendations when estimating and interpreting the networks. 3) We also closely followed the relevant tutorials, and used examples interpreted by experts in the field, to interpret the bootnet and NetworkComparisonTest results. 4) It is possible for statistical control to increase reliability, but that does not appear to be the case here. 5) Distinguishing between statistically significant versus substantive differences makes it clear that the differences between the networks affect the inferences we would make about symptom-level relationships (i.e., the basis of the purported utility of symptom networks). Ultimately, there is an important point of agreement between our article and the commentaries: All of these applied examples of cross-sectional symptom networks are demonstrating unreliable parameter estimates. While the commentaries propose that the resulting differences between networks are not genuine or meaningful because they are not statistically significant, we propose that the unreplicable inferences about the symptom-level relationships of interest fundamentally undermine the utility of the symptom networks.

The present study (a) tested whether a structure of common mental disorders within the hierarchical taxonomy of psychopathology was invariant from late childhood to adolescence in a sample of Mexican-origin youth, (b) examined the developmental course of psychopathology at different levels of the hierarchy, and (c) tested the degree to which changes in psychopathology were associated with changes in the Big Five personality domains. Results were consistent with the longitudinal hierarchical invariance of common mental disorders from age 12 to 17 ( n = 674). Further, initial levels of conscientiousness, agreeableness, and emotional stability were positively associated with lower initial levels of a higher order factor of psychopathology, and increases in extraversion and decreases in neuroticism were associated with decreases in a higher order factor of psychopathology, which captured the general tendency for externalizing, internalizing, and attention-hyperactivity-related dimensions of psychopathology to correlate. Results of the present study indicate that a hierarchical model of common mental disorders extends to Mexican-origin youth and that developmental change in Big Five personality are related to developmental change in psychopathology. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical structural model of psychological symptoms formulated to improve the reliability and validity of clinical assessment. Neurobiology can inform assessments of early risk and intervention strategies, and the HiTOP model has greater potential to interface with neurobiological measures than traditional categorical diagnoses given its enhanced reliability. However, one complication is that observed biological correlates of clinical symptoms can reflect various factors, ranging from dispositional risk to consequences of psychopathology. In this paper, we argue that the HiTOP model provides an optimized framework for conducting research on the biological correlates of psychopathology from an ontogenetic perspective that distinguishes among indicators of liability, current symptoms, and consequences of illness. Through this approach, neurobiological research can contribute more effectively to identifying individuals at high dispositional risk, indexing treatment-related gains, and monitoring the consequences of mental illness, consistent with the aims of the HiTOP framework.
., (© 2019, AICHServier GroupCopyright © 2019 AICH Servier Group. All rights reserved.)

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).

Objective: Diagnosis is a cornerstone of clinical practice for mental health care providers, yet traditional diagnostic systems have well-known shortcomings, including inadequate reliability, high comorbidity, and marked within-diagnosis heterogeneity. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a data-driven, hierarchically based alternative to traditional classifications that conceptualizes psychopathology as a set of dimensions organized into increasingly broad, transdiagnostic spectra. Prior work has shown that using a dimensional approach improves reliability and validity, but translating a model like HiTOP into a workable system that is useful for health care providers remains a major challenge., Method: The present work outlines the HiTOP model and describes the core principles to guide its integration into clinical practice., Results: Potential advantages and limitations of the HiTOP model for clinical utility are reviewed, including with respect to case conceptualization and treatment planning. A HiTOP approach to practice is illustrated and contrasted with an approach based on traditional nosology. Common barriers to using HiTOP in real-world health care settings and solutions to these barriers are discussed., Conclusions: HiTOP represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background: Normative and pathological personality traits have rarely been integrated into a joint large-scale structural analysis with psychiatric disorders, although a recent study suggested they entail a common individual differences continuum., Methods: We explored the joint factor structure of 11 psychiatric disorders, five personality-disorder trait domains (DSM-5 Section III), and five normative personality trait domains (the 'Big Five') in a population-based sample of 2796 Norwegian twins, aged 19‒46., Results: Three factors could be interpreted: (i) a general risk factor for all psychopathology, (ii) a risk factor specific to internalizing disorders and traits, and (iii) a risk factor specific to externalizing disorders and traits. Heritability estimates for the three risk factor scores were 48% (95% CI 41‒54%), 35% (CI 28‒42%), and 37% (CI 31‒44%), respectively. All 11 disorders had uniform loadings on the general factor (congruence coefficient of 0.991 with uniformity). Ignoring sign and excluding the openness trait, this uniformity of factor loadings held for all the personality trait domains and all disorders (congruence 0.983)., Conclusions: Based on our findings, future research should investigate joint etiologic and transdiagnostic models for normative and pathological personality and other psychopathology.

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).

This paper explores the concept that reducing general psychopathology early in the life course provides unprecedented opportunities to prevent the development of all forms of psychopathology later in life. We review empirical evidence for the existence of the general factor of psychopathology and theories regarding the psychological nature of the factor. We then highlight specific examples of environmental risk factors for general psychopathology and discuss translational implications for the transdiagnostic prevention of psychopathology beginning in early childhood. Ultimately, we propose a developmentally informed and transdiagnostic stepped care approach to intervention in which reduction of general psychopathology in early childhood represents the foundational step for prevention and intervention of subsequent psychopathology. This model heralds three key benefits over the current treatment zeitgeist: (1) Reducing the burden and confusion in healthcare and education systems by providing a coherent and systematic structure for early intervention across a child's development, (2) maximising the breadth of the impact of intervention by focusing on common shared risks across psychopathology, and (3) increasing the efficiency of intervention by corresponding with the development of psychopathology and leveraging the emergence of general psychopathology in early childhood., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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.

In our target article, we tested the replicability of 4 popular psychopathology network estimation methods that aim to reveal causal relationships among symptoms of mental illness. We started with the focal data set from the 2 foundational psychopathology network papers (i.e., the National Comorbidity Survey-Replication) and identified the National Survey of Mental Health and Wellbeing as a close methodological match for comparison. We compared the psychopathology networks estimated in each data set-as well as in 10 sets of random split-halves within each data set-with the goal of quantifying the replicability of the network parameters as they are interpreted in the extant psychopathology network literature. We concluded that current psychopathology network methods have limited replicability both within and between samples and thus have limited utility. Here we respond to the 2 commentaries on our target article, concluding that the findings of Steinley, Hoffman, Brusco, and Sher (2017)-along with other recent developments in the literature-provide further conclusive evidence that psychopathology networks have poor replicability and utility. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Narcissistic personality disorder (NPD) shows high rates of comorbidity with mood, anxiety, substance use, and other personality disorders. Previous bivariate comorbidity investigations have left NPD multivariate comorbidity patterns poorly understood. Structural psychopathology research suggests that two transdiagnostic factors, internalizing (with distress and fear subfactors) and externalizing, account for comorbidity among common mental disorders. NPD has rarely been evaluated within this framework, with studies producing equivocal results. We investigated how NPD related to other mental disorders in the internalizing-externalizing model using diagnoses from a nationally representative sample (N = 34,653). NPD was best conceptualized as a distress disorder. NPD variance accounted for by transdiagnostic factors was modest, suggesting its variance is largely unique in the context of other common mental disorders. Results clarify NPD multivariate comorbidity, suggest avenues for classification and clinical endeavors, and highlight the need to understand vulnerable and grandiose narcissism subtypes' comorbidity patterns and structural relations.

Threat sensitivity (THT) and weak inhibitory control (or disinhibition; DIS) are trait constructs that relate to multiple types of psychopathology and can be assessed psychoneurometrically (i.e., using self-report and physiological indicators combined). However, to establish that psychoneurometric assessments of THT and DIS index biologically-based liabilities, it is important to clarify the etiologic bases of these variables and their associations with clinical problems. The current work addressed this important issue using data from a sample of identical and fraternal adult twins (N=454). THT was quantified using a scale measure and three physiological indicators of emotional reactivity to visual aversive stimuli. DIS was operationalized using scores on two scale measures combined with two brain indicators from cognitive processing tasks. THT and DIS operationalized in these ways both showed appreciable heritability (0.45, 0.68), and genetic variance in these traits accounted for most of their phenotypic associations with fear, distress, and substance use disorder symptoms. Our findings suggest that, as indices of basic dispositional liabilities for multiple forms of psychopathology with direct links to neurophysiology, psychoneurometric assessments of THT and DIS represent novel and important targets for biologically-oriented research on psychopathology., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

The Personality Inventory for DSM-5 (PID-5) measures the trait part (Criterion B) of the alternative model for personality disorders proposed in Section III of DSM-5. Although its psychometric properties have proven adequate thus far, evidence is limited in other languages and in clinical samples. The Spanish PID-5 was examined in two samples comprising 446 clinical and 1,036 community subjects. Facet scales showed good internal consistency in both samples (median α = .86 and .79) and were unidimensional under exploratory and confirmatory approaches. They were also able to distinguish between clinical and community subjects with a mean standardized difference of z = 0.81. All facets except for Risk Taking were unipolar, such that the upper poles indicated pathology and the lower poles reflected normality, rather than the opposite pole of abnormality. The entire PID-5 hierarchical structure, from one to five factors, was confirmed in both samples with Tucker's congruence coefficients over .95.

We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a hierarchy of increasingly specific etiologic influences . Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the heterogeneity of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the 3 levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

We propose a novel developmentally informed framework to push research beyond a focus on comorbidity between discrete diagnostic categories and to move toward research based on the well-validated dimensional and hierarchical structure of psychopathology. For example, a large body of research speaks to the validity and utility of the internalizing and externalizing spectra as organizing constructs for research on common forms of psychopathology. The internalizing and externalizing spectra act as powerful explanatory variables that channel the psychopathological effects of genetic and environmental risk factors, predict adaptive functioning, and account for the likelihood of disorder-level manifestations of psychopathology. As such, our proposed theoretical framework uses the internalizing and externalizing spectra as central constructs to guide future psychopathology research across the life span. The framework is particularly flexible, because any of the facets or factors from the dimensional and hierarchical structure of psychopathology can form the focus of research. We describe the utility and strengths of this framework for developmental psychopathology in particular and explore avenues for future research.

The National Institute of Mental Health Research Domain Criteria initiative (Insel et al., 2010) calls for a focus on biologically meaningful dimensional constructs in the study of clinical problems. Examples are needed of how Research Domain Criteria constructs can be linked to clinical problems. We examined how two such constructs, threat sensitivity (THT+) and weak inhibitory control (INH-), operationalized using scale measures of fear/fearlessness and inhibition/disinhibition dimensions from established structural models, predicted symptoms of multiple Diagnostic and Statistical Manual of Mental Disorders (4th edition) clinical disorders in 471 community adults. Robust relationships with internalizing disorder symptoms were evident for both trait variables, with THT+ more predictive of fear disorder symptoms and INH- more predictive of distress disorder symptoms. For substance-related problems, prediction was evident only for INH-. Additionally, interactive effects of THT+ and INH- were found for distress disorders, and to a lesser extent, fear disorders. Given their well-established physiological correlates, these dispositional variables represent prime targets for combined psychometric-neurophysiological assessment of broad liabilities to multiple forms of psychopathology., (© The Author(s) 2015.)

Background: Co-morbidity among use of different substances can be explained by a shared underlying dimensional factor. What remains unknown is whether the relationship between substance use and various co-morbid mental disorders can be explained solely by the general factor or whether there remain unique contributions of specific substances., Method: Data were from the 2007 Australian National Survey of Mental Health and Wellbeing (NSMHWB). A unidimensional latent factor was constructed that represented general substance use. The shared and specific relationships between lifetime substance use indicators and internalizing disorders, suicidality and psychotic-like experiences (PLEs) were examined using Multiple Indicators Multiple Causes (MIMIC) models in the total sample. Additional analyses then examined the shared and specific relationships associated with substance dependence diagnoses as indicators of the latent trait focusing on a subsample of substance users., Results: General levels of latent substance use were significantly and positively related to internalizing disorders, suicidality and psychotic-like experiences. Similar results were found when examining general levels of latent substance dependence in a sample of substance users. There were several direct effects between specific substance use/dependence indicators and the mental health correlates that significantly improved the overall model fit but they were small in magnitude and had relatively little impact on the general relationship., Conclusions: The majority of pairwise co-morbid relationships between substance use/dependence and mental health correlates can be explained through a general latent factor. Researchers should focus on investigating the commonalities across all substance use and dependence indicators when studying mental health co-morbidity.

Purpose: Classification is the cornerstone of clinical diagnostic practice and research. However, the extant psychiatric classification systems are not well supported by research evidence. In particular, extensive comorbidity among putatively distinct disorders flags an urgent need for fundamental changes in how we conceptualize psychopathology. Over the past decade, research has coalesced on an empirically based model that suggests many common mental disorders are structured according to two correlated latent dimensions: internalizing and externalizing., Methods: We review and discuss the development of a dimensional-spectrum model which organizes mental disorders in an empirically based manner. We also touch upon changes in the DSM-5 and put forward recommendations for future research endeavors., Results: Our review highlights substantial empirical support for the empirically based internalizing-externalizing model of psychopathology, which provides a parsimonious means of addressing comorbidity., Conclusions: As future research goals, we suggest that the field would benefit from: expanding the meta-structure of psychopathology to include additional disorders, development of empirically based thresholds, inclusion of a developmental perspective, and intertwining genomic and neuroscience dimensions with the empirical structure of psychopathology.

Purpose: Research on the structure of mental disorders and comorbidity indicates that many forms of psychopathology and substance use disorders are manifestations of relatively few transdiagnostic latent factors. These factors have important consequences for mental disorder research and applied practice., Methods: We provide an overview of the transdiagnostic factor literature, with particular focus on recent advances., Results: Internalizing and externalizing transdiagnostic factors have been well characterized in terms of their structures, links with disorders, stability, and statistical properties (e.g., invariance and distributions). Research on additional transdiagnostic factors, such as thought disorder, is quickly advancing latent structural models, as are integrations of transdiagnostic constructs with personality traits. Genetically informed analyses continue to clarify the origins of transdiagnostic factor levels, and links between these factors and important environmental exposures provide promising new avenues of inquiry., Conclusions: Transdiagnostic factors account for the development and continuity of disorders and comorbidity over time, function as the primary links between disorders and important outcomes such as suicide, mediate associations between environmental exposures and disorders, provide an empirically supported classification system, and serve as foci for efficient, broadband intervention approaches. Overall, transdiagnostic factor research indicates the paramount importance of understanding these constructs and, thereby, broadening our understanding of mental disorder in general.

Objective: To construct a virtual space of common adolescent psychiatric disorders, spanned by factors reflecting major psychopathological dimensions; and to locate psychiatric disorders in that space, examine whether the major psychopathological dimensions can be hierarchically organized, and determine the distribution of the latent scores of individuals in the space spanned by those dimensions., Method: Exploratory factor analyses of data from the National Comorbidity Survey Adolescent Supplement (NCS-A) using the psychiatric diagnoses as indicators were used to identify the latent major psychopathological dimensions. The loadings of the disorders on those dimensions were used as coordinates to calculate the distance among disorders. The distribution of individuals in the space was based on the latent scores on the factors reflecting the major psychopathological conditions., Results: A model with 3 correlated factors provided an excellent fit (Comparative Fit Index [CFI] = 0.97, Tucker-Lewis Index [TLI] = 0.95, the root mean squared error of approximation [RMSEA] = 0.008) for the structure of disorders and a 4-factor model could be hierarchically organized, ultimately yielding a general psychopathology factor. Distances between disorders ranged from 0.079 (between social phobia and generalized anxiety disorder [GAD]) and 1.173 (between specific phobia and conduct disorder [CD]). At the individual level, there were 546 distinct liabilities observed (22% of all 2,455 potential liabilities)., Conclusion: A novel way of understanding psychiatric disorders in adolescents is as existing in a space with a limited number of dimensions with no disorder aligning along 1 single dimension. These dimensions are hierarchically organized, allowing analyses at different levels of organization. Furthermore, individuals with psychiatric disorders present with a broad range of liabilities, reflecting the diversity of their clinical presentations., (Copyright © 2015 American Academy of Child and Adolescent Psychiatry. All rights reserved.)

The DSM-5 creation process and outcome underlines a core tension in psychiatry between empirical evidence that mental pathologies tend to be dimensional and a historical emphasis on delineating categorical disorders to frame psychiatric thinking. The DSM has been slow to reflect dimensional evidence because doing so is often perceived as a disruptive paradigm shift. As a result, other authorities are making this shift, circumventing the DSM in the process. For example, through the Research Domain Criteria (RDoC), NIMH now encourages investigators to focus on a dimensional and neuroscientific conceptualization of mental disorder research. Fortunately, the DSM-5 contains a dimensional model of maladaptive personality traits that provides clinical descriptors that align conceptually with the neuroscience-based dimensions delineated in the RDoC and in basic science research. Through frameworks such as the DSM-5 trait model, the DSM can evolve to better incorporate evidence of the dimensionality of mental disorder.

Importance: Mental disorders predict future occurrences of both the same disorder (homotypic continuity) and other disorders (heterotypic continuity). Heterotypic continuity is inconsistent with a view of mental disorders as fixed entities. In contrast, hierarchical-dimensional conceptualizations of psychopathology, in which each form of psychopathology is hypothesized to have both unique and broadly shared etiologies and mechanisms, predict both homotypic and heterotypic continuity., Objective: To test predictions derived from a hierarchical-dimensional model of psychopathology that (1) heterotypic continuity is widespread, even controlling for homotypic continuity, and that (2) the relative magnitudes of heterotypic continuities recapitulate the relative magnitudes of cross-sectional correlations among diagnoses at baseline., Design, Setting, and Participants: Ten prevalent diagnoses were assessed in the same person twice (ie, in 2 waves separated by 3 years). We used a representative sample of adults in the United States (i.e., 28,958 participants 18-64 years of age in the National Epidemiologic Study of Alcohol and Related Conditions who were assessed in both waves)., Main Outcomes and Measures: Diagnoses from reliable and valid structured interviews., Results: Adjusting for sex and age, we found that bivariate associations of all pairs of diagnoses from wave 1 to wave 2 exceeded chance levels (P < .05) for all homotypic (median tetrachoric correlation of ρ = 0.54 [range, 0.41-0.79]) and for nearly all heterotypic continuities (median tetrachoric correlation of ρ = 0.28 [range, 0.07-0.50]). Significant heterotypic continuity was widespread even when all wave 1 diagnoses (including the same diagnosis) were simultaneous predictors of each wave 2 diagnosis. The rank correlation between age- and sex-adjusted tetrachoric correlation for cross-sectional associations among wave 1 diagnoses and for heterotypic associations from wave 1 to wave 2 diagnoses was ρ = 0.86 (P < .001)., Conclusions and Relevance: For these prevalent mental disorders, heterotypic continuity was nearly universal and not an artifact of failure to control for homotypic continuity. Furthermore, the relative magnitudes of heterotypic continuity closely mirrored the relative magnitudes of cross-sectional associations among these disorders, consistent with the hypothesis that both sets of associations reflect the same factors. Mental disorders are not fixed and independent entities. Rather, each diagnosis is robustly related to other diagnoses in a correlational structure that is manifested both concurrently and in patterns of heterotypic continuity across time.

Recent years have witnessed tremendous growth in the scope and sophistication of statistical methods available to explore the latent structure of psychopathology, involving continuous, discrete, and hybrid latent variables. The availability of such methods has fostered optimism that they can facilitate movement from classification primarily crafted through expert consensus to classification derived from empirically based models of psychopathological variation. The explication of diagnostic constructs with empirically supported structures can then facilitate the development of assessment tools that appropriately characterize these constructs. Our goal in this article is to illustrate how new statistical methods can inform conceptualization of personality psychopathology and therefore its assessment. We use magical thinking as an example, because both theory and earlier empirical work suggested the possibility of discrete aspects to the latent structure of personality psychopathology, particularly forms of psychopathology involving distortions of reality testing, yet other data suggest that personality psychopathology is generally continuous in nature. We directly compared the fit of a variety of latent variable models to magical thinking data from a sample enriched with clinically significant variation in psychotic symptomatology for explanatory purposes. Findings generally suggested a continuous latent variable model best represented magical thinking, but results varied somewhat depending on different indexes of model fit. We discuss the implications of the findings for classification and applied personality assessment. We also highlight some limitations of this type of approach that are illustrated by these data, including the importance of substantive interpretation, in addition to use of model fit indexes, when evaluating competing structural models.

Previous research using confirmatory factor analysis to model psychopathology comorbidity has supported the hypothesis of a broad general factor (i.e., a "bifactor"; Holzinger & Swineford, 1937) of psychopathology in children, adolescents, and adults, with more specific higher order internalizing and externalizing factors reflecting additional shared variance in symptoms (Lahey et al., 2012; Lahey, van Hulle, Singh, Waldman, & Rathouz, 2011). The psychological nature of this general factor has not been explored, however. The current study tested a prediction, derived from the spectrum hypothesis of personality and psychopathology, that variance in a general psychopathology bifactor overlaps substantially-at both phenotypic and genetic levels-with the dispositional trait of negative emotionality. Data on psychopathology symptoms and dispositional traits were collected from both parents and youth in a representative sample of 1,569 twin pairs (ages 9-17 years) from Tennessee. Predictions based on the spectrum hypothesis were supported, with variance in negative emotionality and the general factor overlapping substantially at both phenotypic and etiologic levels. Furthermore, stronger correlations were found between negative emotionality and the general psychopathology factor than among other dispositions and other psychopathology factors., (PsycINFO Database Record (c) 2013 APA, all rights reserved.)

At the outset of the golden era of cartography, fifteenth-century Europeans drew the earth as they wished it to be. Some maps were so fanciful as to show four continents like petals centered around Jerusalem. It would take a century of adventurers, navigators, mathematicians, and artists to align the map with the actual places where the rocks met the seas. In this effort, there were many lost voyages and sailors. Fictitious islands and isthmuses arose and fell. Yet even in their nascent form, the maps suggested important destinations and passages. They did not have to be perfect to be useful. Doubtless, there were times in the Age of Discovery when navigators were forced to choose between an old and dear map that had not been fully discredited, and a new and unknown picture of the world that remained yet unproved. How then did they decide which to trust? This special section of the Journal of Abnormal Psychology is designed to explore this question with three review papers and a commentary all composed by leading psychopathologists, the navigators and cartographers of our discipline. In the end, the DSM-5 became something more akin to a DSM-IV-TR-revised, as opposed to enshrining the new diagnostic paradigm the task force chairs originally sought. While there were several innovations, such as a chapter structure that better reflected data on the empirical organization of psychopathology, the old map was largely retained with a few of the sea monsters relocated., (PsycINFO Database Record (c) 2013 APA, all rights reserved.)

The relationships between two measures proposed to describe personality pathology, that is the Revised NEO Personality Inventory (NEO-PI-3) and the Personality Inventory for DSM-5 (PID-5), are examined in an undergraduate sample (N = 240). The NEO inventories are general trait measures, also considered relevant to assess disordered personality, whereas the PID-5 measure is specifically designed to assess pathological personality traits, as conceptualized in the DSM-5 proposal. A structural analysis of the 25 PID-5 traits confirmed the factor structure observed in the U.S. derivation sample, with higher order factors of Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism. A joint factor analysis of, respectively, the NEO domains and their facets with the PID-5 traits showed that general and maladaptive traits are subsumed under an umbrella of five to six major dimensions that can be interpreted from the perspective of the five-factor model or the Personality Psychopathology Five. Implications for the assessment of personality pathology and the construction of models of psychopathology grounded in personality are discussed.

Section 3 of the DSM-5 will include a pathological personality trait system rooted in the quantitative epistemology of personality and clinical psychology. This system has the potential to enhance the clinical utility of the diagnostic nosology by providing a means for the dimensional assessment of individuals with psychopathology. However, there is limited research on the associations of DSM-5 traits with common mental disorders and related clinical phenomena as measured by currently popular assessment instruments. The purpose of this article was to evaluate the convergence of the DSM-5 trait system with a well-validated broadband clinical instrument, the Personality Assessment Inventory (PAI). Bivariate correlations were examined and factor analytic methods were used to examine the degree to which the DSM-5 traits and PAI capture common variance in personality and mental health. In a student sample (N = 1,001), we found broad convergence between the DSM-5 traits and PAI, which could be organized effectively using five factors. The implications of these findings for using traits to address issues related to diagnostic co-occurrence and heterogeneity in routine clinical assessment are discussed.

Purpose: Prevalence rates, and bivariate comorbidity patterns, of many common mental disorders differ significantly across ethnic groups. While studies have examined multivariate comorbidity patterns by gender and age, no studies to our knowledge have examined such patterns by ethnicity. Such an investigation could aid in understanding the nature of ethnicity-related health disparities in mental health and is timely given the likely implementation of multivariate comorbidity structures (i.e., internalizing and externalizing) to frame key parts of DSM-5., Methods: We investigated whether multivariate comorbidity of 11 common mental disorders, and their associated latent comorbidity factors, differed across five ethnic groups in a large, nationally representative sample (n = 43,093). We conducted confirmatory factor analyses and factorial invariance analyses in White (n = 24,507), Hispanic/Latino (n = 8,308), Black (n = 8,245), Asian/Pacific Islander (n = 1,332), and American Indian/Alaska Native (n = 701) individuals., Results: Results supported a two-factor internalizing-externalizing comorbidity factor model in both lifetime and 12-month diagnoses. This structure was invariant across ethnicity, but factor means differed significantly across ethnic groups., Conclusions: These findings, taken together, indicated that observed prevalence rate differences between ethnic groups reflect ethnic differences in latent internalizing and externalizing factor means. We discuss implications for classification (DSM-5 and ICD-11 meta-structure), health disparities research, and treatment.

CONTEXT Clinical experience and factor analytic studies suggest that some psychiatric disorders may be more closely related to one another, as indicated by the frequency of their co-occurrence, which may have etiologic and treatment implications. OBJECTIVE To construct a virtual space of common psychiatric disorders, spanned by factors reflecting major psychopathologic dimensions, and locate psychiatric disorders in that space, as well as to examine whether the location of disorders at baseline predicts the prevalence and incidence of disorders at 3-year follow-up. DESIGN, SETTING, AND PATIENTS A total of 34 653 individuals participated in waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. MAIN OUTCOME MEASURES The distance between disorders at wave 1, calculated using the loadings of the factors spanning the space of disorders as coordinates. This distance was correlated with the adjusted odds ratios for age, sex, and race/ethnicity of the prevalence and incidence of Axis I disorders in wave 2, with the aim of determining whether smaller distances between disorders at wave 1 predicts higher disorder prevalence and incidence at wave 2. RESULTS A model with 3 correlated factors provided an excellent fit (Comparative Fit Index = 0.99, Tucker-Lewis Index = 0.98, root mean square error of approximation = 0.008) for the structure of common psychiatric disorders and was used to span the space of disorders. Distances ranged from 0.070 (between drug abuse and alcohol dependence) to 1.032 (between drug abuse and dysthymia). The correlation of distance between disorders in wave 1 with adjusted odds ratios of prevalence in wave 2 was -0.56. The correlation of distance in wave 1 with adjusted odds ratios of incidence in wave 2 was -0.57. CONCLUSIONS Mapping psychiatric disorders can be used to quantify the distances among disorders. Proximity in turn can be used to predict prospectively the incidence and prevalence of Axis I disorders.

Background: Previous research suggests that various types of childhood maltreatment frequently co-occur and confer risk for multiple psychiatric diagnoses. This non-specific pattern of risk may mean that childhood maltreatment increases vulnerability to numerous specific psychiatric disorders through diverse, specific mechanisms or that childhood maltreatment engenders a generalised liability to dimensions of psychopathology. Although these competing explanations have different implications for intervention, they have never been evaluated empirically., Aims: We used a latent variable approach to estimate the associations of childhood maltreatment with underlying dimensions of internalising and externalising psychopathology and with specific disorders after accounting for the latent dimensions. We also examined gender differences in these associations., Method: Data were drawn from a nationally representative survey of 34 653 US adults. Lifetime DSM-IV psychiatric disorders were assessed using the AUDADIS-IV. Physical, sexual and emotional abuse and neglect were assessed using validated measures. Analyses controlled for other childhood adversities and sociodemographics., Results: The effects were fully mediated through the latent liability dimensions, with an impact on underlying liability levels to internalising and externalising psychopathology rather than specific psychiatric disorders. Important gender differences emerged with physical abuse associated only with externalising liability in men, and only with internalising liability in women. Neglect was not significantly associated with latent liability levels., Conclusions: The association between childhood maltreatment and common psychiatric disorders operates through latent liabilities to experience internalising and externalising psychopathology, indicating that the prevention of maltreatment may have a wide range of benefits in reducing the prevalence of many common mental disorders. Different forms of abuse have gender-specific consequences for the expression of internalising and externalising psychopathology, suggesting gender-specific aetiological pathways between maltreatment and psychopathology.

Epidemiological studies of categorical mental disorders consistently report that gender differences exist in many disorder prevalence rates and that disorders are often comorbid. Can a dimensional multivariate liability model be developed to clarify how gender impacts diverse, comorbid mental disorders? We pursued this possibility in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; N = 43,093). Gender differences in prevalence were systematic such that women showed higher rates of mood and anxiety disorders, and men showed higher rates of antisocial personality and substance use disorders. We next investigated patterns of disorder comorbidity and found that a dimensional internalizing-externalizing liability model fit the data well, where internalizing is characterized by mood and anxiety disorders, and externalizing is characterized by antisocial personality and substance use disorders. This model was gender invariant, indicating that observed gender differences in prevalence rates originate from women and men's different average standings on latent internalizing and externalizing liability dimensions. As hypothesized, women showed a higher mean level of internalizing, while men showed a higher mean level of externalizing. We discuss implications of these findings for understanding gender differences in psychopathology and for classification and intervention., (PsycINFO Database Record (c) 2012 APA, all rights reserved.)

Marital distress is linked to many types of mental disorders; however, no study to date has examined this link in the context of empirically based hierarchical models of psychopathology. There may be general associations between low levels of marital quality and broad groups of comorbid psychiatric disorders as well as links between marital adjustment and specific types of mental disorders. The authors examined this issue in a sample (N = 929 couples) of currently married couples from the Minnesota Twin Family Study who completed self-report measures of relationship adjustment and were also assessed for common mental disorders. Structural equation modeling indicated that (a) higher standing on latent factors of internalizing (INT) and externalizing (EXT) psychopathology was associated with lower standing on latent factors of general marital adjustment for both husbands and wives, (b) the magnitude of these effects was similar across husbands and wives, and (c) there were no residual associations between any specific mental disorder and overall relationship adjustment after controlling for the INT and EXT factors. These findings point to the utility of hierarchical models in understanding psychopathology and its correlates. Much of the link between mental disorder and marital distress operated at the level of broad spectrums of psychopathological variation (i.e., higher levels of marital distress were associated with disorder comorbidity), suggesting that the temperamental core of these spectrums contributes not only to symptoms of mental illness but to the behaviors that lead to impaired marital quality in adulthood.

Context: Patterns of comorbidity among mental disorders are thought to reflect the natural organization of mental illness. Factor analysis can be used to investigate this structure and construct a quantitative classification system. Prior studies identified 3 dimensions of psychopathology: internalizing, externalizing, and thought disorder. However, research has largely relied on common disorders and community samples. Consequently, it is unclear how well the identified organization applies to patients and how other major disorders fit into it., Objective: To analyze comorbidity among a wide range of Axis I disorders and personality disorders (PDs) in the general outpatient population., Design: Clinical cohort study., Setting: A general outpatient practice, the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project., Participants: Outpatients (N = 2900) seeking psychiatric treatment., Main Outcome Measures: The Structured Clinical Interview for DSM-IV and the Structured Interview for DSM-IV Personality., Results: We tested several alternative groupings of the 25 target disorders. The DSM-IV organization fit the data poorly. The best-fitting model consisted of 5 factors: internalizing (anxiety and eating disorders, major depressive episode, and cluster C, borderline, and paranoid PDs), externalizing (substance use disorders and antisocial PD), thought disorder (psychosis, mania, and cluster A PDs), somatoform (somatoform disorders), and antagonism (cluster B and paranoid PDs)., Conclusions: We confirmed the validity of the 3 previously found spectra in an outpatient population. We also found novel somatoform and antagonism dimensions, which this investigation was able to detect because, to our knowledge, this is the first study to include a variety of somatoform and personality disorders. The findings suggest that many PDs can be placed in Axis I with related clinical disorders. They also suggest that unipolar depression may be better placed with anxiety disorders than with bipolar disorders. The emerging quantitative nosology promises to provide a more useful guide to clinicians and researchers.