Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Comput Biol Med. 2022 Sep;148:105791. doi: 10.1016/j.compbiomed.2022.105791. Epub 2022 Jul 10.
Analysis of effective connectivity among brain regions is an important key to decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity Disorder (ADHD). We previously introduced a new method, called nCREANN (nonlinear Causal Relationship Estimation by Artificial Neural Network), for estimating linear and nonlinear components of effective connectivity, and provided novel findings about effective connectivity of EEG signals of children with autism. Using the nCREANN method in the present study, we assessed effective connectivity patterns of ADHD children based on their EEG signals recorded during a visual attention task, and compared them with the aged-matched Typically Developing (TD) subjects.
In addition to the nCREANN method for estimating linear and nonlinear aspects of effective connectivity, the direct Directed Transfer Function (dDTF) was utilized to extract the spectral information of connectivity patterns.
The dDTF results did not suggest a specific frequency band for distinguishing between the two groups, and different patterns of effective connectivity were observed in all bands. Both nCREANN and dDTF methods showed decreased connectivity between temporal/frontal and temporal/occipital regions, and increased connection between frontal/parietal regions in ADHDs than TDs. Furthermore, the nCREANN results showed more left-lateralized connections in ADHDs compared to the symmetric bilateral inter-hemispheric interactions in TDs. In addition, by fusion of linear and nonlinear connectivity measures of nCREANN method, we achieved an accuracy of 99% in classification of the two groups.
These findings emphasize the capability of nCREANN method to investigate the brain functioning of neural disorders and its strength in preciously distinguish between healthy and disordered subjects.
分析大脑区域之间的有效连接是破解注意力缺陷多动障碍（ADHD）等神经障碍机制的重要关键。我们之前介绍了一种新方法，称为 nCREANN（基于人工神经网络的非线性因果关系估计），用于估计有效连接的线性和非线性分量，并提供了关于自闭症儿童 EEG 信号有效连接的新发现。在本研究中，我们使用 nCREANN 方法，根据 ADHD 儿童在视觉注意任务期间记录的 EEG 信号，评估他们的有效连接模式，并将其与年龄匹配的典型发育（TD）受试者进行比较。
除了用于估计有效连接的线性和非线性方面的 nCREANN 方法外，还利用直接定向传递函数（dDTF）提取连接模式的频谱信息。
dDTF 结果没有提示区分两组的特定频带，并且在所有频带中都观察到了不同的有效连接模式。nCREANN 和 dDTF 方法均显示 ADHD 组中颞/额叶和颞/枕叶之间的连接减少，而额叶/顶叶之间的连接增加。此外，nCREANN 结果显示 ADHD 组的连接更偏向左侧，而 TD 组的双侧半球间相互作用则更对称。此外，通过融合 nCREANN 方法的线性和非线性连接测量值，我们实现了对两组的分类准确率为 99%。
这些发现强调了 nCREANN 方法研究神经障碍大脑功能的能力及其在精确区分健康和障碍受试者方面的优势。
