Scheinost Dustin, Dadashkarimi Javid, Finn Emily S, Wambach Caroline G, MacGillivray Caroline, Roule Alexandra L, Niendam Tara A, Pine Daniel S, Brotman Melissa A, Leibenluft Ellen, Tseng Wan-Ling
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Department of Computer Science, Yale University, New Haven, CT, USA.
Neuropsychopharmacology. 2021 Jun;46(7):1300-1306. doi: 10.1038/s41386-020-00954-8. Epub 2021 Jan 21.
Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability is aberrant responses to frustrative nonreward. Here, we conducted a preliminary fMRI study to examine the ability of functional connectivity during frustrative nonreward to predict irritability in a transdiagnostic sample. This study included 69 youths (mean age = 14.55 years) with varying levels of irritability across diagnostic groups: disruptive mood dysregulation disorder (n = 20), attention-deficit/hyperactivity disorder (n = 14), anxiety disorder (n = 12), and controls (n = 23). During fMRI, participants completed a frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring cognitive flexibility, "frustration" blocks had a 50% error rate and some rigged feedback, while "nonfrustration" blocks had a 10% error rate. Frustration and nonfrustration blocks were randomly interspersed. Child and parent reports of the affective reactivity index were used as dimensional measures of irritability. Connectome-based predictive modeling, a machine learning approach, with tenfold cross-validation was conducted to identify networks predicting irritability. Connectivity during frustration (but not nonfrustration) blocks predicted child-reported irritability (ρ = 0.24, root mean square error = 2.02, p = 0.03, permutation testing, 1000 iterations, one-tailed). Results were adjusted for age, sex, medications, motion, ADHD, and anxiety symptoms. The predictive networks of irritability were primarily within motor-sensory networks; among motor-sensory, subcortical, and salience networks; and between these networks and frontoparietal and medial frontal networks. This study provides preliminary evidence that individual differences in irritability may be associated with functional connectivity during frustration, a phenotype-relevant state.
易怒在多种儿科疾病中都有体现，是儿童精神病学中常见的就诊主诉；然而，其神经机制仍不清楚。易怒的一个核心病理生理缺陷是对挫折性无奖励的异常反应。在此，我们进行了一项初步的功能磁共振成像（fMRI）研究，以检验在挫折性无奖励期间功能连接预测跨诊断样本中易怒情况的能力。本研究纳入了69名青少年（平均年龄 = 14.55岁），他们来自不同诊断组，易怒程度各异：破坏性行为障碍（n = 20）、注意力缺陷多动障碍（n = 14）、焦虑症（n = 12）以及对照组（n = 23）。在功能磁共振成像扫描期间，参与者完成一项令人沮丧的认知灵活性任务。通过操纵任务难度来引发挫折感，即在需要认知灵活性的试验中，“挫折”组块有50%的错误率以及一些预先设定的反馈，而“无挫折”组块有10%的错误率。挫折组块和无挫折组块随机穿插出现。儿童和家长报告的情感反应指数被用作易怒程度的量化指标。采用基于连接组的预测模型（一种机器学习方法）并进行十折交叉验证，以识别预测易怒情况的神经网络。挫折（而非无挫折）组块期间的连接性可预测儿童报告的易怒程度（ρ = 0.24，均方根误差 = 2.02，p = 0.03，置换检验，1000次迭代，单尾）。结果针对年龄、性别、药物治疗、运动、注意缺陷多动障碍及焦虑症状进行了调整。易怒的预测网络主要存在于运动感觉网络内；在运动感觉、皮层下和突显网络之间；以及这些网络与额顶叶和内侧额叶网络之间。本研究提供了初步证据，表明易怒的个体差异可能与挫折期间的功能连接有关，而挫折是一种与表型相关的状态。
