Electroencephalography (EEG)-based attention disorder research seeks to understand brain activity patterns associated with attention. Previous studies have mainly focused on identifying brain regions involved in cognitive processes or classifying Attention-Deficit Hyperactivity Disorder (ADHD) and control subjects. However, analyzing effective brain connectivity networks for specific attentional processes and comparing them has not been explored. Therefore, in this study, we propose multivariate transfer entropy-based connectivity networks for cognitive events and introduce a new similarity measure, 'SimBrainNet', to assess these networks. A high similarity score suggests similar brain dynamics during cognitive events, indicating less attention variability. Our experiment involves 12 individuals with attention disorders (7 children and 5 adolescents). Noteworthy that child participants exhibit lower similarity scores compared to adolescents, indicating greater changes in attention. We found strong connectivity patterns in the left pre-frontal cortex for adolescent individuals compared to the child. Our study highlights the changes in attention levels across various cognitive events, offering insights into the underlying cognitive mechanisms, brain dynamics, and potential deficits in individuals with this disorder.

翻译：基于脑电图（EEG）的注意力障碍研究致力于理解与注意力相关的大脑活动模式。先前的研究主要集中于识别参与认知过程的脑区，或对注意缺陷多动障碍（ADHD）患者与对照组进行分类。然而，针对特定注意过程的有效脑连接网络进行分析与比较的研究尚未得到探索。因此，在本研究中，我们提出了基于多元传递熵的认知事件连接网络，并引入了一种新的相似性度量方法——"SimBrainNet"，用于评估这些网络。较高的相似性得分表明认知事件期间的大脑动态相似，意味着注意力变异性较低。我们的实验涉及12名注意力障碍个体（7名儿童和5名青少年）。值得注意的是，与青少年相比，儿童参与者表现出较低的相似性得分，表明其注意力变化更大。我们发现，相较于儿童，青少年个体在左前额叶皮层表现出更强的连接模式。本研究揭示了不同认知事件中注意力水平的变化，为理解该障碍个体的潜在认知机制、大脑动态及可能缺陷提供了见解。