Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Neuropsychopharmacology. 2023 Jan;48(2):281-289. doi: 10.1038/s41386-022-01408-z. Epub 2022 Sep 13.
We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen's d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.
我们试图确定与注意缺陷/多动障碍相关的静息状态特征，包括作为一种分类诊断和特征特征，使用经过标准化管道处理的大规模样本。在分类分析中，我们考虑了 1301 名被诊断为 ADHD 的患者，与 1301 名未受影响的对照者进行了对比（总 N=2602；1710 名男性（65.72%）；平均年龄为 10.86 岁，标准差为 2.05）。病例和对照者按扫描内运动和关键人口统计学变量进行 1:1 最近邻匹配，来自多个大型队列。还在一个大型多队列样本（N=10113）中评估了 ADHD 特征与静息状态连通性之间的关联。ADHD 诊断与默认模式和突显/腹侧注意力之间的反相关减少有关（B=0.009，t=3.45，p-FDR=0.004，d=0.14，95%CI=0.004，0.014），躯体运动（B=0.008，t=3.49，p-FDR=0.004，d=0.14，95%CI=0.004，0.013）和背侧注意力网络（B=0.01，t=4.28，p-FDR<0.001，d=0.17，95%CI=0.006，0.015）。这些结果在考虑合并的内化问题、外化问题和精神兴奋剂药物的敏感性分析中是稳健的。当检查 ADHD 特征时，观察到类似的发现，在默认模式网络和背侧注意力网络之间的连通性上观察到最大的效应量（B=0.0006，t=5.57，p-FDR<0.001，部分-r=0.06，95%CI=0.0004，0.0008）。我们报告了与 ADHD 干扰模型一致的默认模式网络与任务正性网络之间相互作用的显著 ADHD 相关差异。效应量（Cohen's d 和部分-r，从 mega分析模型中估计）较小，表明存在细微的组间差异。临床和一般人群样本中受影响的大脑网络之间的重叠支持了 ADHD 连续体上存在大脑表型的概念。
