Subgrouping autism and ADHD based on structural MRI population modelling centiles

• Published in: Molecular autism Vol. 16; no. 1; pp. 33 - 19
• Main Authors: Pecci-Terroba, Clara, Lai, Meng-Chuan, Lombardo, Michael V., Chakrabarti, Bhismadev, Ruigrok, Amber N. V., Suckling, John, Anagnostou, Evdokia, Lerch, Jason P., Taylor, Margot J., Nicolson, Rob, Georgiades, Stelios, Crosbie, Jennifer, Schachar, Russell, Kelley, Elizabeth, Jones, Jessica, Arnold, Paul D., Seidlitz, Jakob, Alexander-Bloch, Aaron F., Bullmore, Edward T., Baron-Cohen, Simon, Bedford, Saashi A., Bethlehem, Richard A. I.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.06.2025; BioMed Central; BMC
• Subjects: ADHD; Adolescent; Algorithms; Attention Deficit Disorder with Hyperactivity - classification; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention Deficit Disorder with Hyperactivity - pathology; Attention-deficit hyperactivity disorder; Autism; Autistic Disorder - classification; Autistic Disorder - diagnostic imaging; Autistic Disorder - pathology; Child; Cluster Analysis; Diagnosis; Female; Humans; Identification and classification; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical research; Medicine, Experimental; Neuroimaging; Population modelling; Structural MRI; Subgrouping; United Kingdom; Connecticut; Neuroimaging; Structural MRI; ADHD; Autism; Population modelling; Subgrouping
• DOI: 10.1186/s13229-025-00667-z
• ISSN: 2040-2392

Autism and attention deficit hyperactivity disorder (ADHD) are two highly heterogeneous neurodevelopmental conditions with variable underlying neurobiology. Imaging studies have yielded varied results, and it is now clear that there is unlikely to be one characteristic neuroanatomical profile of either condition. Parsing this heterogeneity could allow us to identify more homogeneous subgroups, either within or across conditions, which may be more clinically informative. This has been a pivotal goal for neurodevelopmental research using both clinical and neuroanatomical features, though results thus far have again been inconsistent with regards to the number and characteristics of subgroups. Here, we use population modelling to cluster a multi-site dataset based on global and regional centile scores of cortical thickness, surface area and grey matter volume. We use HYDRA, a novel semi-supervised machine learning algorithm which clusters based on differences to controls and compare its performance to a traditional clustering approach. We identified distinct subgroups within autism and ADHD, as well as across diagnosis, often with opposite neuroanatomical alterations relatively to controls. These subgroups were characterised by different combinations of increased or decreased patterns of morphometrics. We did not find significant clinical differences across subgroups. Crucially, however, the number of subgroups and their membership differed vastly depending on chosen features and the algorithm used, highlighting the impact and importance of careful method selection. We highlight the importance of examining heterogeneity in autism and ADHD and demonstrate that population modelling is a useful tool to study subgrouping in autism and ADHD. We identified subgroups with distinct patterns of alterations relative to controls but note that these results rely heavily on the algorithm used and encourage detailed reporting of methods and features used in future studies.