Gray Matter Network Associated With Attention in Children With Attention Deficit Hyperactivity Disorder - Wang, Xing-Ke; Wang, Xiu-Qin; Yang, Xue; Yuan, Li-Xia (Frontiers in Psychiatry, 2022)
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. Currently, the majority of studies have analyzed the structural MRI (sMRI) with the univariate method, which fail to demonstrate the interregional covarying relationship of gray matter (GM) volumes among brain regions. Scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate method, which can detect more robust brain-behavioral phenotype association compared to univariate analysis method. This study aimed 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 (TDC) aged 7-14 years old 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 (namely 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 the current study was explored. Results: The ADHD-related pattern mainly includes 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 Conners’ Parent Rating Scale long version (CPRS-LV, r = 0.25, p = 0.0004) and DuPaul ADHD Rating Scale IV (ADHD-RS, r = 0.18, p = 0.031). Finally, we found that when sample size came to 252, the results of ADHD-related pattern were relatively reliable. Similarly, the sample size needed to be 162 when explore the relationship between ADHD-related pattern and behavioral indicator measured by CPRS-LV. Conclusions: 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.