Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Artif Intell Med. 2018 Jul;89:40-50. doi: 10.1016/j.artmed.2018.05.003. Epub 2018 Jul 11.
The brain connections in the different regions demonstrate the characteristics of brain activities. In addition, in various conditions and with neuropsychological disorders, the brain has special patterns in different regions. This paper presents a model to show and compare the connection patterns in different brain regions of children with autism (53 boys and 36 girls) and control children (61 boys and 33 girls). The model is designed by cellular neural networks and it uses the proper features of electroencephalography. The results show that there are significant differences and abnormalities in the left hemisphere, (p < 0.05) at the electrodes AF3, F3, P7, T7, and O1 in the children with autism compared with the control group. Also, the evaluation of the obtained connections values between brain regions demonstrated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in children with autism. It is observed that the proposed model is able to distinguish the autistic children from the control subjects with an accuracy rate of 95.1% based on the obtained values of CNN using the SVM method.
不同区域的大脑连接显示出大脑活动的特征。此外，在不同的条件下和神经心理障碍中，大脑在不同区域有特殊的模式。本文提出了一个模型来展示和比较自闭症儿童（53 名男孩和 36 名女孩）和对照组儿童（61 名男孩和 33 名女孩）不同脑区的连接模式。该模型由细胞神经网络设计，它使用脑电图的适当特征。结果表明，与对照组相比，自闭症儿童左半球（p<0.05）的电极 AF3、F3、P7、T7 和 O1 存在明显差异和异常。此外，对脑区之间获得的连接值的评估表明，自闭症儿童的额叶和顶叶以及相邻区域之间的连接异常更多。观察到，所提出的模型能够使用 SVM 方法基于 CNN 获得的值以 95.1%的准确率将自闭症儿童与对照组区分开来。
