Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry
Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be v...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158225000063 |
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| author | Linn B. Norbom Bilal Syed Rikka Kjelkenes Jaroslav Rokicki Antoine Beauchamp Stener Nerland Azadeh Kushki Evdokia Anagnostou Paul Arnold Jennifer Crosbie Elizabeth Kelley Robert Nicolson Russell Schachar Margot J. Taylor Lars T. Westlye Christian K. Tamnes Jason P. Lerch |
| author_facet | Linn B. Norbom Bilal Syed Rikka Kjelkenes Jaroslav Rokicki Antoine Beauchamp Stener Nerland Azadeh Kushki Evdokia Anagnostou Paul Arnold Jennifer Crosbie Elizabeth Kelley Robert Nicolson Russell Schachar Margot J. Taylor Lars T. Westlye Christian K. Tamnes Jason P. Lerch |
| author_sort | Linn B. Norbom |
| collection | DOAJ |
| description | Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders. Yet, no studies have attempted to identify subtypes using T1w/T2w-ratio, a magnetic resonance imaging (MRI) based proxy for intracortical myelin. Moreover, cortical variability arises from numerous biological pathways, and multimodal approaches can integrate cortical metrics into a single network. We analyzed data from 310 individuals aged 2.6–23.6 years, obtained from the Province of Ontario Neurodevelopmental (POND) Network consisting of individuals diagnosed with ASD (n = 136), ADHD (n = 100), and typically developing (TD) individuals (n = 74). We first tested for differences in T1w/T2w-ratio between diagnostic categories and controls. We then performed unimodal (T1w/T2w-ratio) and multimodal (T1w/T2w-ratio, cortical thickness, and surface area) spectral clustering to identify diagnostic-blind subgroups. Linear models revealed no statistically significant case-control differences in T1w/T2w-ratio. Unimodal clustering mostly isolated single individual- or minority clusters, driven by image quality and intensity outliers. Multimodal clustering suggested three distinct subgroups, which transcended diagnostic boundaries, showing separate cortical patterns but similar clinical and cognitive profiles. T1w/T2w-ratio features were the most relevant for demarcation, followed by surface area. While our analysis revealed no significant case-control differences, multimodal clustering incorporating the T1w/T2w-ratio among cortical features holds promise for identifying biologically similar subsets of individuals with neurodevelopmental conditions. |
| format | Article |
| id | doaj-art-2556710a6e034720bc49ec67a226d6b5 |
| institution | DOAJ |
| issn | 2213-1582 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-2556710a6e034720bc49ec67a226d6b52025-08-20T02:52:31ZengElsevierNeuroImage: Clinical2213-15822025-01-014510373610.1016/j.nicl.2025.103736Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometryLinn B. Norbom0Bilal Syed1Rikka Kjelkenes2Jaroslav Rokicki3Antoine Beauchamp4Stener Nerland5Azadeh Kushki6Evdokia Anagnostou7Paul Arnold8Jennifer Crosbie9Elizabeth Kelley10Robert Nicolson11Russell Schachar12Margot J. Taylor13Lars T. Westlye14Christian K. Tamnes15Jason P. Lerch16PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Corresponding author.The Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, CanadaDepartment of Psychology, University of Oslo, Norway; Section for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, NorwayCentre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, NorwayThe Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychiatry, Western University, London, CanadaDivision of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, NorwayAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada; University of Toronto, Institute of Biomedical Engineering, Toronto, CanadaAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, CanadaHotchkiss Brain Institute, Departments of Psychiatry & Medical Genetics, University of Calgary, Calgary, CanadaDepartment of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, CanadaDepartment of Psychology and Centre for Neuroscience Studies, Queen’s University, Kingston, CanadaDepartment of Psychiatry, Western University, London, CanadaDepartment of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, CanadaDiagnostic & Interventional Radiology, The Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, CanadaDepartment of Psychology, University of Oslo, Norway; Section for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, NorwayPROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, NorwayThe Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomAutism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders. Yet, no studies have attempted to identify subtypes using T1w/T2w-ratio, a magnetic resonance imaging (MRI) based proxy for intracortical myelin. Moreover, cortical variability arises from numerous biological pathways, and multimodal approaches can integrate cortical metrics into a single network. We analyzed data from 310 individuals aged 2.6–23.6 years, obtained from the Province of Ontario Neurodevelopmental (POND) Network consisting of individuals diagnosed with ASD (n = 136), ADHD (n = 100), and typically developing (TD) individuals (n = 74). We first tested for differences in T1w/T2w-ratio between diagnostic categories and controls. We then performed unimodal (T1w/T2w-ratio) and multimodal (T1w/T2w-ratio, cortical thickness, and surface area) spectral clustering to identify diagnostic-blind subgroups. Linear models revealed no statistically significant case-control differences in T1w/T2w-ratio. Unimodal clustering mostly isolated single individual- or minority clusters, driven by image quality and intensity outliers. Multimodal clustering suggested three distinct subgroups, which transcended diagnostic boundaries, showing separate cortical patterns but similar clinical and cognitive profiles. T1w/T2w-ratio features were the most relevant for demarcation, followed by surface area. While our analysis revealed no significant case-control differences, multimodal clustering incorporating the T1w/T2w-ratio among cortical features holds promise for identifying biologically similar subsets of individuals with neurodevelopmental conditions.http://www.sciencedirect.com/science/article/pii/S2213158225000063T1w/T2w-ratioMorphometryCortexClusteringAutismADHD |
| spellingShingle | Linn B. Norbom Bilal Syed Rikka Kjelkenes Jaroslav Rokicki Antoine Beauchamp Stener Nerland Azadeh Kushki Evdokia Anagnostou Paul Arnold Jennifer Crosbie Elizabeth Kelley Robert Nicolson Russell Schachar Margot J. Taylor Lars T. Westlye Christian K. Tamnes Jason P. Lerch Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry NeuroImage: Clinical T1w/T2w-ratio Morphometry Cortex Clustering Autism ADHD |
| title | Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry |
| title_full | Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry |
| title_fullStr | Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry |
| title_full_unstemmed | Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry |
| title_short | Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry |
| title_sort | probing autism and adhd subtypes using cortical signatures of the t1w t2w ratio and morphometry |
| topic | T1w/T2w-ratio Morphometry Cortex Clustering Autism ADHD |
| url | http://www.sciencedirect.com/science/article/pii/S2213158225000063 |
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