Studies have shown that specific networks (default mode network [DMN] and task positive network [TPN]) activate in an anticorrelated manner when sustaining attention. Related EEG studies are scarce and often lack behavioral validation. We performed independent component analysis (ICA) across different frequencies (source-level), using eLORETA-ICA, to extract brain-network activity during resting-state and sustained attention. We applied ICA to the voxel domain, similar to functional magnetic resonance imaging methods of analyses. The obtained components were contrasted and correlated to attentional performance (omission errors) in a large sample of healthy subjects (N = 1397). We identified one component that robustly correlated with inattention and reflected an anticorrelation of delta activity in the anterior cingulate and precuneus, and delta and theta activity in the medial prefrontal cortex and with alpha and gamma activity in medial frontal regions. We then compared this component between optimal and suboptimal attentional performers. For the latter group, we observed a greater change in component loading between resting-state and sustained attention than for the optimal performers. Following the National Institute of Mental Health Research Domain Criteria (RDoC) approach, we prospectively replicated and validated these findings in subjects with attention deficit/hyperactivity disorder. Our results provide further support for the "default mode interference hypothesis."

译文

研究表明,特定网络 (默认模式网络 [DMN] 和任务正向网络 [TPN]) 在保持注意力时以反相关的方式激活。相关的脑电图研究很少,而且往往缺乏行为验证。我们使用eLORETA-ICA进行了不同频率 (源级) 的独立成分分析 (ICA),以提取静息状态和持续注意力期间的脑网络活动。我们将ICA应用于体素域,类似于功能磁共振成像分析方法。在健康受试者的大样本 (N = 1397) 中,对获得的组分进行对比并与注意表现 (遗漏误差) 相关。我们确定了一个与注意力不集中密切相关的成分,并反映了前扣带回和前神经的delta活动以及内侧前额叶皮层的delta和theta活动以及内侧额叶区域的alpha和gamma活动的反相关性。然后,我们比较了最佳注意力执行者和次优注意力执行者之间的这一分量。对于后一组,我们观察到静息状态和持续注意力之间的组件负荷变化比最佳表演者更大。遵循美国国家精神卫生研究所研究领域标准 (RDoC) 方法,我们前瞻性地在注意力缺陷/多动障碍患者中复制并验证了这些发现。我们的结果为 “默认模式干扰假设” 提供了进一步的支持。

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