ArticleFrontiers in neurology
Gray matter volume alterations in adolescents with ADHD are associated with cell type-specific transcriptional signatures.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Attention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain development, yet the molecular and cellular mechanisms underlying its characteristic gray matter volume (GMV) alterations remain poorly understood. This study aimed to integrate neuroimaging and transcriptomic data to identify cell-type-specific transcriptional signatures associated with GMV changes in adolescents with ADHD. Methods: Voxel-based morphometry was performed on structural MRI data from 27 adolescents with ADHD and 34 typically developing (TD) controls to map regional GMV differences. The spatial pattern of these alterations was then correlated with whole-brain gene expression profiles from the Allen Human Brain Atlas using partial least squares (PLS) regression. Functional and cell-type enrichment analyses were conducted on significant gene sets. Finally, three machine-learning models (support vector machine, random forest, and decision tree) were developed to evaluate the diagnostic utility of GMV changes. Results: Increased GMV was observed in the bilateral precuneus, and decreased GMV was found in the left middle occipital gyrus and orbital part of the right inferior frontal gyrus in ADHD compared to TD. The spatial distribution of these GMV changes was significantly correlated with a specific gene expression pattern. Functional enrichment analysis revealed that positively correlated genes were involved in fundamental cellular processes, while negatively correlated genes were associated with synaptic organization and brain development. Cell-type analysis demonstrated significant enrichment of positively correlated genes in microglia, and negatively correlated genes in excitatory and inhibitory neurons. Random Forest achieved the highest accuracy in distinguishing between ADHD and TD (AUC = 0.871 ± 0.029). Conclusion: In summary, this study provides unique insights into the brain structural development of attention-deficit/hyperactivity disorder (ADHD) and offers new perspectives for the future diagnosis and treatment of ADHD.
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