Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance
IntroductionAttention-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 a...
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Child and Adolescent Psychiatry |
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| author | Younes Sadat-Nejad Younes Sadat-Nejad Marlee M. Vandewouw Marlee M. Vandewouw R. Cardy J. Lerch J. Lerch J. Lerch M. J. Taylor M. J. Taylor A. Iaboni C. Hammill B. Syed J. A. Brian J. A. Brian E. Kelley E. Kelley E. Kelley M. Ayub J. Crosbie J. Crosbie R. Schachar R. Schachar S. Georgiades R. Nicolson E. Anagnostou E. Anagnostou A. Kushki A. Kushki |
| author_facet | Younes Sadat-Nejad Younes Sadat-Nejad Marlee M. Vandewouw Marlee M. Vandewouw R. Cardy J. Lerch J. Lerch J. Lerch M. J. Taylor M. J. Taylor A. Iaboni C. Hammill B. Syed J. A. Brian J. A. Brian E. Kelley E. Kelley E. Kelley M. Ayub J. Crosbie J. Crosbie R. Schachar R. Schachar S. Georgiades R. Nicolson E. Anagnostou E. Anagnostou A. Kushki A. Kushki |
| author_sort | Younes Sadat-Nejad |
| collection | DOAJ |
| description | IntroductionAttention-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.MethodsIn 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 [n = 262, median(IQR) age = 12.2(5.9), 22% female], and ADHD [n = 171, median(IQR) age = 11.1(4.0), 21% female] as well neurotypical children [n = 132, median(IQR) age = 12.1(6.7), 43% female]. We integrated cortical thickness, surface area, and cortical/subcortical volume, with a measure of single-participant structural covariance using a graph neural network approach.ResultsOur findings suggest two large clusters, which differed in measures of adaptive functioning (χ2 = 7.8, P = 0.004), inattention (χ2 = 11.169, P < 0.001), hyperactivity (χ2 = 18.44, P < 0.001), IQ (χ2 = 9.24, P = 0.002), age (χ2 = 70.87, P < 0.001), and sex (χ2 = 105.6, P < 0.001).DiscussionThese 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. |
| format | Article |
| id | doaj-art-0f612ea801b740a69623bfc32ec5821f |
| institution | Kabale University |
| issn | 2813-4540 |
| language | English |
| publishDate | 2023-09-01 |
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| series | Frontiers in Child and Adolescent Psychiatry |
| spelling | doaj-art-0f612ea801b740a69623bfc32ec5821f2025-01-20T14:22:26ZengFrontiers Media S.A.Frontiers in Child and Adolescent Psychiatry2813-45402023-09-01210.3389/frcha.2023.11713371171337Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covarianceYounes Sadat-Nejad0Younes Sadat-Nejad1Marlee M. Vandewouw2Marlee M. Vandewouw3R. Cardy4J. Lerch5J. Lerch6J. Lerch7M. J. Taylor8M. J. Taylor9A. Iaboni10C. Hammill11B. Syed12J. A. Brian13J. A. Brian14E. Kelley15E. Kelley16E. Kelley17M. Ayub18J. Crosbie19J. Crosbie20R. Schachar21R. Schachar22S. Georgiades23R. Nicolson24E. Anagnostou25E. Anagnostou26A. Kushki27A. Kushki28Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaMouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Neuroscience and Mental Health, Department of Medical Biophysics, The Hospital for Sick Children, University of Toronto, Toronto, ON, CanadaNuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United KingdomDiagnostic Imaging, The Hospital for Sick Children, Toronto, ON, CanadaDepartment of Medical Imaging, University of Toronto, Toronto, ON, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaMouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, CanadaMouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaDepartment of Paediatrics, University of Toronto, Toronto, ON, CanadaDepartment of Psychology, Queen's University, Kingston, ON, Canada0Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada1Department of Psychiatry, Queen's University, Kingston, ON, Canada1Department of Psychiatry, Queen's University, Kingston, ON, Canada2Department of Psychiatry, University of Toronto, Toronto, ON, Canada3Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada2Department of Psychiatry, University of Toronto, Toronto, ON, Canada3Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada4Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada5Department of Psychiatry, Western University, London, ON, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaDepartment of Paediatrics, University of Toronto, Toronto, ON, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, CanadaInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, CanadaIntroductionAttention-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.MethodsIn 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 [n = 262, median(IQR) age = 12.2(5.9), 22% female], and ADHD [n = 171, median(IQR) age = 11.1(4.0), 21% female] as well neurotypical children [n = 132, median(IQR) age = 12.1(6.7), 43% female]. We integrated cortical thickness, surface area, and cortical/subcortical volume, with a measure of single-participant structural covariance using a graph neural network approach.ResultsOur findings suggest two large clusters, which differed in measures of adaptive functioning (χ2 = 7.8, P = 0.004), inattention (χ2 = 11.169, P < 0.001), hyperactivity (χ2 = 18.44, P < 0.001), IQ (χ2 = 9.24, P = 0.002), age (χ2 = 70.87, P < 0.001), and sex (χ2 = 105.6, P < 0.001).DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/frcha.2023.1171337/fullneurodevelopmental conditionsautismADHDclusteringbrain structuredata driven |
| spellingShingle | Younes Sadat-Nejad Younes Sadat-Nejad Marlee M. Vandewouw Marlee M. Vandewouw R. Cardy J. Lerch J. Lerch J. Lerch M. J. Taylor M. J. Taylor A. Iaboni C. Hammill B. Syed J. A. Brian J. A. Brian E. Kelley E. Kelley E. Kelley M. Ayub J. Crosbie J. Crosbie R. Schachar R. Schachar S. Georgiades R. Nicolson E. Anagnostou E. Anagnostou A. Kushki A. Kushki Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance Frontiers in Child and Adolescent Psychiatry neurodevelopmental conditions autism ADHD clustering brain structure data driven |
| title | Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance |
| title_full | Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance |
| title_fullStr | Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance |
| title_full_unstemmed | Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance |
| title_short | Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance |
| title_sort | investigating heterogeneity across autism adhd and typical development using measures of cortical thickness surface area cortical subcortical volume and structural covariance |
| topic | neurodevelopmental conditions autism ADHD clustering brain structure data driven |
| url | https://www.frontiersin.org/articles/10.3389/frcha.2023.1171337/full |
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