ArticleFrontiers in psychiatry2022
Gray Matter Network Associated With Attention in Children With Attention Deficit Hyperactivity Disorder.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Advances in neuroimaging studies of thalamic abnormalities in children with attention deficit hyperactivity disorder.Psychoradiology · 2026Review
- Biomarkers and Neuropsychological Tools in Attention-Deficit/Hyperactivity Disorder: From Subjectivity to Precision Diagnosis.Medicina (Kaunas, Lithuania) · 2025Review
- INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.Nature communications · 2025Article
- From vision to cognition: potential contributions of cerebral visual impairment to neurodevelopmental disorders.Journal of neural transmission (Vienna, Austria : 1996) · 2023Review
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4 authors.
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Abstract
Background: Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent childhood-onset neurodevelopmental disorders; however, the underlying neural mechanisms for the inattention symptom remain elusive for children with ADHD. At present, the majority of studies have analyzed the structural MRI (sMRI) with the univariate method, which fails to demonstrate the interregional covarying relationship of gray matter (GM) volumes among brain regions. The scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate method, which can detect more robust brain-behavioral phenotype association compared to the univariate analysis method. This study aims to identify the GM network associated with attention in children with ADHD by applying SSM-PCA to the sMRI. Methods: The sMRI of 209 children with ADHD and 209 typically developing controls (TDCs) aged 7-14 years from the ADHD-200 dataset was used for anatomical computation, and the GM volume in each brain region was acquired. Then, SSM-PCA was applied to the GM volumes of all the subjects to capture the GM network of children with ADHD (i.e., ADHD-related pattern). The relationship between the expression of ADHD-related pattern and inattention symptom was further investigated. Finally, the influence of sample size on the analysis of this study was explored. Results: The ADHD-related pattern mainly included putamen, pallium, caudate, thalamus, right accumbens, superior/middle/inferior frontal cortex, superior occipital cortex, superior parietal cortex, and left middle occipital cortex. In addition, the expression of the ADHD-related pattern was related to inattention scores measured by the Conners' Parent Rating Scale long version (CPRS-LV; Conclusion: We captured a GM network associated with attention in children with ADHD, which is different from that in adolescents and adults with ADHD. Our findings may shed light on the diverse neural mechanisms of inattention and provide treatment targets for children with ADHD.
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