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注意瞬脱现象下的大脑网络可预测儿童注意缺陷多动障碍的症状。

The brain network underlying attentional blink predicts symptoms of attention deficit hyperactivity disorder in children.

机构信息

Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518073, China.

Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China.

出版信息

Cereb Cortex. 2023 Mar 10;33(6):2761-2773. doi: 10.1093/cercor/bhac240.

Abstract

Attention deficit hyperactivity disorder (ADHD) is a chronic neuropsychiatric disease that can markedly impair educational, social, and occupational function throughout life. Behavioral deficits may provide clues to the underlying neurological impairments. Children with ADHD exhibit a larger attentional blink (AB) deficit in rapid serial visual presentation (RSVP) tasks than typically developing children, so we examined whether brain connectivity in the neural network associated with AB can predict ADHD symptoms and thus serve as potential biomarkers of the underlying neuropathology. We first employed a connectome-based predictive model analysis of adult resting-state functional magnetic resonance imaging data to identify a distributed brain network for AB. The summed functional connectivity (FC) strength within the AB network reliably predicted individual differences in AB magnitude measured by a classical dual-target RSVP task. Furthermore, the summed FC strength within the AB network predicted individual differences in ADHD Rating Scale scores from an independent dataset of pediatric patients. Our findings suggest that the individual AB network could serve as an applicable neuroimaging-based biomarker of AB deficit and ADHD symptoms.

摘要

注意缺陷多动障碍(ADHD)是一种慢性神经精神疾病,可在整个生命周期中显著损害教育、社会和职业功能。行为缺陷可能为潜在的神经功能障碍提供线索。与正常发育的儿童相比,ADHD 儿童在快速序列视觉呈现(RSVP)任务中表现出更大的注意瞬脱(AB)缺陷,因此我们研究了与 AB 相关的神经网络中的脑连接是否可以预测 ADHD 症状,从而作为潜在的神经病理学的生物标志物。我们首先采用基于连接组的成人静息态功能磁共振成像数据的预测模型分析,以确定用于 AB 的神经网络。通过经典的双目标 RSVP 任务测量的 AB 幅度的个体差异,可以可靠地预测 AB 网络内的总和功能连接(FC)强度。此外,AB 网络内的总和 FC 强度可以预测来自儿科患者的独立数据集的 ADHD 评定量表评分的个体差异。我们的研究结果表明,个体 AB 网络可以作为 AB 缺陷和 ADHD 症状的适用的基于神经影像学的生物标志物。

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