ArticleFrontiers in child and adolescent psychiatry2023
Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance.
Article in Frontiers in child and adolescent psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- From heterogeneity to translation: data‑driven subtyping of autism in a multilevel framework.Translational psychiatry · 2026Review
- Real-world application of an eye-tracking device for autism screening and diagnosis: a short report from public demonstrations in Qatar, Dubai and the U.S.BMC psychiatry · 2026Article
- Using deep learning to predict internalizing problems from brain structure in youth.Translational psychiatry · 2025Article
- Assessing the stability of evoked, induced, and passive MEG responses for repeat testing with optically pumped magnetometers.Scientific reports · 2025Article
- Using deep learning to predict internalizing problems from brain structure in youth.bioRxiv : the preprint server for biology · 2025Article
- Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions.Human brain mapping · 2024Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Attention-deficit/hyperactivity disorder (ADHD) and autism are multi-faceted neurodevelopmental conditions with limited biological markers. The clinical diagnoses of autism and ADHD are based on behavioural assessments and may not predict long-term outcomes or response to interventions and supports. To address this gap, data-driven methods can be used to discover groups of individuals with shared biological patterns. Methods: In this study, we investigated measures derived from cortical/subcortical volume, surface area, cortical thickness, and structural covariance investigated of 565 participants with diagnoses of autism [ Results: Our findings suggest two large clusters, which differed in measures of adaptive functioning ( Discussion: These clusters did not align with existing diagnostic labels, suggesting that brain structure is more likely to be associated with differences in adaptive functioning, IQ, and ADHD features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.