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Journal of Autism and Developmental Disorders
Gepubliceerd in:

25-06-2022 | Original Paper

Distinct Symptom Network Structure and Shared Central Social Communication Symptomatology in Autism and Schizophrenia: A Bayesian Network Analysis

Auteurs: Gloria T. Han, Dominic A. Trevisan, Jennifer Foss-Feig, Vinod Srihari, James C. McPartland

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 9/2023

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Abstract

Autism (ASD) and schizophrenia spectrum disorders (SCZ) are neurodevelopmental conditions with overlapping and interrelated symptoms. A network analysis approach that represents clinical conditions as a set of “nodes” (symptoms) connected by “edges” (relations among symptoms) was used to compare symptom organization in the two conditions. Gaussian graphical models were estimated using Bayesian methods to model separate symptom networks for adults with confirmed ASD or SCZ diagnoses. Though overall symptom organization differed by diagnostic group, both symptom networks demonstrated high centrality of social communication difficulties. Autism-relevant restricted and repetitive behaviors and schizophrenia-related cognitive-perceptual symptoms were uniquely central to the ASD and SCZ networks, respectively. Results offer recommendations to improve differential diagnosis and highlight potential treatment targets in ASD and SCZ.
vorige artikel Are there gender-based variations in the presentation of Autism amongst female and male children?
volgende artikel Layer-Specific Changes in the Prefrontal Glia/Neuron Ratio Characterizes Patches of Gene Expression Disorganization in Children with Autism
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Metagegevens
Titel
Distinct Symptom Network Structure and Shared Central Social Communication Symptomatology in Autism and Schizophrenia: A Bayesian Network Analysis
Auteurs
Gloria T. Han
Dominic A. Trevisan
Jennifer Foss-Feig
Vinod Srihari
James C. McPartland
Publicatiedatum
25-06-2022
Uitgeverij
Springer US
Gepubliceerd in
Journal of Autism and Developmental Disorders / Uitgave 9/2023
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI
https://doi.org/10.1007/s10803-022-05620-0

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