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      Towards a definitive symptom structure of obsessive−compulsive disorder: a factor and network analysis of 87 distinct symptoms in 1366 individuals

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          Abstract

          Background

          The symptoms of obsessive−compulsive disorder (OCD) are highly heterogeneous and it is unclear what is the optimal way to conceptualize this heterogeneity. This study aimed to establish a comprehensive symptom structure model of OCD across the lifespan using factor and network analytic techniques.

          Methods

          A large multinational cohort of well-characterized children, adolescents, and adults diagnosed with OCD ( N = 1366) participated in the study. All completed the Dimensional Yale-Brown Obsessive−Compulsive Scale, which contains an expanded checklist of 87 distinct OCD symptoms. Exploratory and confirmatory factor analysis were used to outline empirically supported symptom dimensions, and interconnections among the resulting dimensions were established using network analysis. Associations between dimensions and sociodemographic and clinical variables were explored using structural equation modeling (SEM).

          Results

          Thirteen first-order symptom dimensions emerged that could be parsimoniously reduced to eight broad dimensions, which were valid across the lifespan: Disturbing Thoughts, Incompleteness, Contamination, Hoarding, Transformation, Body Focus, Superstition, and Loss/Separation. A general OCD factor could be included in the final factor model without a significant decline in model fit according to most fit indices. Network analysis showed that Incompleteness and Disturbing Thoughts were most central (i.e. had most unique interconnections with other dimensions). SEM showed that the eight broad dimensions were differentially related to sociodemographic and clinical variables.

          Conclusions

          Future research will need to establish if this expanded hierarchical and multidimensional model can help improve our understanding of the etiology, neurobiology and treatment of OCD.

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          Most cited references46

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          Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.

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            A tutorial on regularized partial correlation networks.

            Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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              The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies.

              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
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                Author and article information

                Journal
                Psychol Med
                Psychol Med
                PSM
                Psychological Medicine
                Cambridge University Press (Cambridge, UK )
                0033-2917
                1469-8978
                October 2022
                09 February 2021
                : 52
                : 14
                : 3267-3279
                Affiliations
                [1 ]Department of Clinical Sciences Lund, Lund University , Lund, Sweden
                [2 ]Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo , Sao Paulo, SP, Brazil
                [3 ]Department of Psychology, Beykent University , Istanbul, Turkey
                [4 ]Department of Clinical Medicine (Neurosciences), Porto Alegre Health Sciences Federal University , Porto Alegre, Brazil
                [5 ]Department of Child and Adolescent Psychiatry, Marmara University , Istanbul, Turkey
                [6 ]Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic, IDIBAPS, CIBERSAM, University of Barcelona , Barcelona, Spain
                [7 ]Department of Child and Adolescent Psychiatry, Maltepe University , Istanbul, Turkey
                [8 ]Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School , Boston, Massachusetts, USA
                [9 ]Department of Psychology, Gelişim University , Istanbul, Turkey
                [10 ]Department of Psychology, Hasan Kalyoncu University , Gaziantep, Turkey
                [11 ]Department of Child and Adolescent Psychiatry, İnönü University , Malatya, Turkey
                [12 ]Department of Child and Adolescent Psychiatry, Ondokuz Mayıs University , Samsun, Turkey
                [13 ]Turner Institute for Brain and Mental Health, Monash University , Victoria, Australia
                [14 ]D'Or Institute for Research and Education (IDOR) and Institute of Psychiatry, Federal University of Rio de Janeiro , Rio de Janeiro, Brazil
                [15 ]Güzel Günler Clinic , Istanbul, Turkey
                [16 ]Yale Child Study Center , New Haven, CT, USA
                [17 ]Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine , Houston, TX, USA
                [18 ]Departments of Psychiatry, Pediatrics & Psychology, Child Study Center, Yale University , New Haven, CT, USA
                [19 ]Department of Psychiatry, Federal University of São Paulo (UNIFESP) , Brazil
                [20 ]Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet , Stockholm, Sweden
                [21 ]Health Care Services, Region Stockholm , Stockholm, Sweden
                Author notes
                Author for correspondence: Matti Cervin, E-mail: matti.cervin@ 123456med.lu.se
                Author information
                https://orcid.org/0000-0003-1188-8706
                Article
                S0033291720005437
                10.1017/S0033291720005437
                9693708
                33557980
                3cc96a12-72d6-4035-9ce5-c0d434844e20
                © The Author(s) 2021

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 August 2020
                : 18 November 2020
                : 22 December 2020
                Page count
                Figures: 2, Tables: 3, References: 41, Pages: 13
                Categories
                Original Article

                Clinical Psychology & Psychiatry
                obsessive–compulsive disorder,symptom dimensions,heterogeneity,factor analysis,network analysis

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