Dataset Information

In search of disorders: internalizing symptom networks in a large clinical sample.

ABSTRACT:

Background

The co-occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence.

Method

Symptom-level data were obtained for service users in publicly funded mental health services in England between 2011 and 2015 (N = 37,162). A symptom network (i.e. Gaussian graphical model) was estimated, and a community detection algorithm was used to explore the clustering of symptoms.

Results

The estimated network was densely connected and characterized by a multitude of weak associations between symptoms. Six communities of symptoms were identified; however, they were weakly demarcated. Two of these communities corresponded to social phobia and panic disorder, and four did not clearly correspond with DSM diagnostic categories. The network structure was largely consistent by sex and across three age groups (8-11, 12-14 and 15-18 years). Symptom connectivity in the two older age groups was significantly greater compared to the youngest group and there were differences in centrality across the age groups, highlighting the age-specific relevance of certain symptoms.

Conclusions

These findings clearly demonstrate the interconnected nature of internalizing symptoms, challenging the view that such pathology takes the form of distinct disorders.

SUBMITTER: McElroy E

PROVIDER: S-EPMC6767473 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

In search of disorders: internalizing symptom networks in a large clinical sample.

McElroy Eoin E Patalay Praveetha P

Journal of child psychology and psychiatry, and allied disciplines 20190321 8

<h4>Background</h4>The co-occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence.<h4>Method< ...[more]

PMID: 30900257

Similar Datasets

Project description:A growing literature suggests robust associations between dimensions of emotion regulation and emotional disorder psychopathology. However, limited research has investigated associations of emotion regulation dimensions across several emotional disorders (transdiagnostic associations), or the incremental validity of emotion regulation versus the higher-order construct of neuroticism. The current study used exploratory structural equation modeling and a large clinical sample (N = 1,138) to: (a) develop a multidimensional emotion regulation measurement model, (b) evaluate the differential associations between latent emotion regulation dimensions and five latent emotional disorder symptom dimensions (social anxiety, depression, agoraphobia/panic, obsessions/compulsions, generalized worry), and (c) determine the incremental contribution of emotion regulation in predicting symptom dimensions beyond neuroticism. The best-fitting measurement model of emotion regulation included four dimensions: Problematic Responses, Poor Recognition/Clarity, Negative Thinking, and Emotional Inhibition/Suppression. Although many zero-order associations between the four latent emotion regulation dimensions and five latent symptom dimensions were significant, few associations remained significant in a structural regression model that included neuroticism. Specifically, Negative Thinking and Problematic Responses incrementally predicted depression symptoms, while Emotional Inhibition/Suppression predicted both social anxiety and depression symptoms. Associations between neuroticism and the emotional disorder dimensions were similar regardless of whether the emotion regulation dimensions were held constant. These results suggest that self-reported emotion regulation dimensions are associated with the severity and expression of a range of emotional disorder symptoms, but that some emotion regulation dimensions have limited incremental validity after accounting for general emotional reactivity. Studies of emotion regulation should assess neuroticism as a key covariate.

Project description:BACKGROUND:Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different. RESULTS:To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer's disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes. CONCLUSIONS:In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.

Project description:Much research has demonstrated that psychopathology can be described in terms of broad dimensions, representing liability for multiple psychiatric disorders. Broad spectra of psychopathology (e.g., internalizing and externalizing) are increasingly used as targets for research investigating the development, etiology, and course of psychopathology because they account for patterns of relatedness among disorders that were once presumed distinct. Thus, these spectra represent alluring targets due to their comprehensive and parsimonious nature. Nevertheless, little research has established the role of individual disorders over and above broad dimensions in the study of psychopathology. In the current study, we investigate whether there are unique etiological associations between individual internalizing disorders and personality traits after accounting for their etiological associations with a broad internalizing dimension. We used a community sample of twins (Npairs = 448) ages 4 to 19 to examine the etiological associations between internalizing psychopathology and Big Five personality dimensions. In terms of genetic covariation, a broad internalizing dimension was positively associated with neuroticism and negatively associated with extraversion, agreeableness, and conscientiousness. Moreover, internalizing accounted for most of the genetic variance shared between individual internalizing disorders and personality traits. Nevertheless, there were unique genetic associations between the following pairs of personality traits and disorders: neuroticism and social anxiety, extraversion and social anxiety, agreeableness and depression, and conscientiousness and compulsions. There was little evidence of environmental influences shared between internalizing and personality. In sum, a broad internalizing dimension adequately accounted for almost all of the etiologic covariation between internalizing disorders and personality, with several interesting exceptions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dataset Information

In search of disorders: internalizing symptom networks in a large clinical sample.

Background

Method

Results

Conclusions

Publications

In search of disorders: internalizing symptom networks in a large clinical sample.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets