Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD.

The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this stud...

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Published in:European Child & Adolescent Psychiatry Vol. 32; no. 10; pp. 1909 - 1924
Main Authors: Deserno, M. K., Bathelt, J., Groenman, A. P., Geurts, H. M.
Format: pictorial research tables/charts Journal Article
Published: Springer Nature Oct2023
Online Access:View this record in EBSCOhost
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      dt: Oct2023
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s00787-022-01986-9
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        atl: Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD.
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        au:
          Deserno, M. K.
          Bathelt, J.
          Groenman, A. P.
          Geurts, H. M.
        affil: https://ror.org/04dkp9463 Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
      sug:
        subj:
          Autism Spectrum Disorder Therapy
          Attention Deficit Hyperactivity Disorder Therapy
          Phenotype
          Human
          Child
          Adolescence
          Male
          Female
          Descriptive Statistics
          Funding Source
          Child: 6-12 years
          Adolescent: 13-18 years
          Male
          Female
      ab: The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (N = 434), and replicate the results in an independent sample (N = 219) of children (ADHD, ASD, and typically developing) aged 7–14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy—suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.
      pubtype: Academic Journal
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    language: English
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