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Journal article
Quantifying the Optimal Structure of the Autism Phenotype: A Comprehensive Comparison of Dimensional, Categorical, and Hybrid Models
Journal of the American Academy of Child and Adolescent Psychiatry, v 58(9), pp 876-886.e2
01 Sep 2019
PMID: 30768420
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Objective: The two primary-seemingly contradictory-strategies for classifying child psychiatric syndromes are categorical and dimensional; conceptual ambiguities appear to be greatest for polythetic syndromes such as autism spectrum disorder (ASD). Recently, a compelling alternative has emerged that integrates both categorical and dimensional approaches (ie, a hybrid model), thanks to the increasing sophistication of analytic procedures. This study aimed to quantify the optimal phenotypic structure of ASD by comprehensively comparing categorical, dimensional, and hybrid models.
Method: The sample comprised 3,825 youth, who were consecutive referrals to a university developmental disabilities or child psychiatric outpatient clinic. Caregivers completed the Child and Adolescent Symptom Inventory - 4R (CASI-4R), which includes an ASD symptom rating scale. A series of latent class analyses, exploratory and confirmatory factor analyses, and factor mixture analyses was conducted. Replication analyses were conducted in an independent sample (N = 2,503) of children referred for outpatient evaluation.
Results: Based on comparison of 44 different models, results indicated that the ASD symptom phenotype is best conceptualized as multidimensional versus a categorical or categorical-dimensional hybrid construct. ASD symptoms were best characterized as falling along three dimensions (ie, social interaction, communication, and repetitive behavior) on the CASI-4R.
Conclusion: Findings reveal an optimal structure with which to characterize the ASD phenotype using a single, parent-report measure, supporting the presence of multiple correlated symptom dimensions that traverse formal diagnostic boundaries and quantify the heterogeneity of ASD. These findings inform understanding of how neurodevelopmental disorders can extend beyond discrete categories of development and represent continuously distributed traits across the range of human behaviors.
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Details
- Title
- Quantifying the Optimal Structure of the Autism Phenotype: A Comprehensive Comparison of Dimensional, Categorical, and Hybrid Models
- Creators
- Hyunsik Kim - Stony Brook UniversityCara Keifer - Stony Brook UniversityCraig Rodriguez-Seijas - Stony Brook UniversityNicholas Eaton - Stony Brook UniversityMatthew Lerner - Stony Brook UniversityKenneth Gadow - Stony Brook University
- Publication Details
- Journal of the American Academy of Child and Adolescent Psychiatry, v 58(9), pp 876-886.e2
- Publisher
- Elsevier
- Number of pages
- 13
- Grant note
- SFARI 381283 / Simons Foundation Autism Research Initiative R01MH073967 / National Institute of Mental Health (NIMH); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH) M01RR10710 / National Institutes of Health (NIH) General Clinical Research Center; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Center for Research Resources (NCRR) 24890 / NARSAD Young Investigator Award; NARSAD 8UL1TR000090-05; UL1 RR024153; UL1TR000005 / Clinical and Translational Science Awards from the National Center for Advancing Translational Sciences grants Matt and Debra Cody Center for Autism and Developmental Disabilities R01 MH077750 / Case Western Reserve University R01 MH 077997 / Stony Brook University R01MH110585; R01 MH077907 / NIMH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH) R01 MH077676 / University of Pittsburgh
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- A.J. Drexel Autism Institute
- Web of Science ID
- WOS:000518531500008
- Scopus ID
- 2-s2.0-85061571812
- Other Identifier
- 991021862306104721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Pediatrics
- Psychiatry
- Psychology, Developmental