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Artigo em Inglês | MEDLINE | ID: mdl-33440203


OBJECTIVE: Irritability is a multifaceted construct in pediatric psychopathology. It has been conceptualized as having a 'phasic' dimension and a 'tonic' dimension. Disruptive mood dysregulation disorder (DMDD) is defined by the presence of both dimensions. Severe irritability, or DMDD, is highly comorbid with attention-deficit/hyperactivity disorder (ADHD). However, it is unknown whether the presence of ADHD modulates the expression of phasic and tonic irritability. METHOD: A data-driven, latent variable approach was used to examine irritability and ADHD symptoms in a transdiagnostic pediatric sample (N=489) with primary DMDD, ADHD, subclinical irritability symptoms, or no diagnosis. Using latent profile analyses (LPA), we identified four classes: high levels of both irritability and ADHD symptoms, high levels of irritability and moderate levels of ADHD symptoms, moderate levels of irritability and high levels of ADHD symptoms, and low levels of both irritability and ADHD symptoms. Confirmatory factor analysis operationalized phasic irritability and tonic irritability. RESULTS: As expected, the two latent classes characterized by high overall irritability exhibited the highest levels of both phasic and tonic irritability. However, between these two high irritability classes, highly comorbid ADHD symptoms were associated with significantly greater phasic irritability than were moderately comorbid ADHD symptoms. In contrast, the two high irritability groups did not differ on levels of tonic irritability. CONCLUSION: These findings suggest that phasic, but not tonic, irritability has a significant association with ADHD symptoms, and that phasic and tonic might be distinct, though highly related, irritability dimensions. Future research should investigate potential mechanisms underlying this differential association.

Hum Brain Mapp ; 2020 Jul 03.
Artigo em Inglês | MEDLINE | ID: mdl-32618421


Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.

Hum Brain Mapp ; 2020 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-32596977


The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.