Wang Xing-Ke, Wang Xiu-Qin, Yang Xue, Yuan Li-Xia
Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Front Psychiatry. 2022 Jul 4;13:922720. doi: 10.3389/fpsyt.2022.922720. eCollection 2022.
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.
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.
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; = 0.25, = 0.0004) and the DuPaul ADHD Rating Scale IV (ADHD-RS; = 0.18, = 0.03). Finally, we found that when the sample size was 252, the results of ADHD-related pattern were relatively reliable. Similarly, the sample size needed to be 162 when exploring the relationship between ADHD-related pattern and behavioral indicator measured by CPRS-LV.
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.
注意缺陷多动障碍（ADHD）是最常见的儿童期起病的神经发育障碍之一；然而，ADHD儿童注意力不集中症状的潜在神经机制仍不清楚。目前，大多数研究采用单变量方法分析结构磁共振成像（sMRI），未能揭示脑区灰质（GM）体积之间的区域间协变关系。主成分分析的缩放子轮廓模型（SSM-PCA）是一种多变量方法，与单变量分析方法相比，它能检测到更强健的脑-行为表型关联。本研究旨在通过将SSM-PCA应用于sMRI来识别ADHD儿童中与注意力相关的GM网络。
使用来自ADHD-200数据集的209名7至14岁的ADHD儿童和209名发育正常的对照（TDC）的sMRI进行解剖学计算，并获取每个脑区的GM体积。然后，将SSM-PCA应用于所有受试者的GM体积，以捕捉ADHD儿童的GM网络（即ADHD相关模式）。进一步研究ADHD相关模式的表达与注意力不集中症状之间的关系。最后，探讨样本量对本研究分析的影响。
ADHD相关模式主要包括壳核、大脑皮层、尾状核、丘脑、右侧伏隔核、额上/中/下回皮层、枕上回、顶上回和左侧枕中回。此外，ADHD相关模式的表达与Conners父母评定量表长版（CPRS-LV；r = 0.25，p = 0.0004）和DuPaul ADHD评定量表第四版（ADHD-RS；r = 0.18，p = 0.03）测量的注意力不集中得分相关。最后，我们发现当样本量为252时，ADHD相关模式的结果相对可靠。同样，在探索ADHD相关模式与CPRS-LV测量的行为指标之间的关系时，样本量需要为162。
我们捕捉到了ADHD儿童中与注意力相关的GM网络，这与ADHD青少年和成人中的网络不同。我们的发现可能有助于揭示注意力不集中的多种神经机制，并为ADHD儿童提供治疗靶点。
