Insights into structural deviations in attention deficit hyperactivity disorder (ADHD) and comorbidities using big data-derived brain charts: a cross-sectional study

Abstract

Background: Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that often coexists with other neurodevelopmental disorders. The intricate comorbidity of ADHD with depression, Tourette syndrome (TS), and autism spectrum disorder (ASD) presents substantial challenges in the screening, diagnosis, and management of these conditions. The aim of this study was to utilize big data-derived brain charts as an objective standard to assess brain development, comparing regional brain development differences between children with pure ADHD and those with comorbidities, and to explore the presumed correlation between specific structural deviations and the severity of ADHD symptoms. Methods: This is a large, population-based cross-sectional study with an observational design that prospectively enrolled 459 children with ADHD, using big data-derived brain charts as an objective standard for assessing brain development. Through normative brain chart modeling, we investigated regional brain development disparities between children with pure ADHD and those with comorbidities, exploring the associations between structural deviations and clinical symptoms. Results: Significant intergroup differences were observed in cortical thickness in the left cuneus gyrus (F=6.50, PFDR =0.03) and medial occipito-temporal gyrus (F=5.75, PFDR =0.04). The ADHD + TS group had the highest number of brain regions with extreme deviations compared to the other groups. Especially, the study found that the ADHD + TS group had a significantly higher proportion of negative deviations in the left middle frontal sulcus than the ADHD + Depression group (PFDR <0.01). Principal component 1 of structural deviations showed significant negative correlations with inattention (r=−0.17, P<0.001) and oppositional defiant disorder (r=−0.10, P=0.04). Deviation scores across multiple cortical brain regions exhibited significant correlations with the inattention score (PFDR <0.05). Conclusions: Brain charts effectively unveil structural variations in ADHD and comorbid groups, aiding in the prediction of inattention severity. These insights advance our understanding of ADHD’s neurobiology and pave the way for personalized diagnostics and therapies.