Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis

• Published in: Artificial intelligence in medicine Vol. 89; pp. 40 - 50
• Main Authors: Askari, Elham, Setarehdan, Seyed Kamaledin, Sheikhani, Ali, Mohammadi, Mohammad Reza, Teshnehlab, Mohammad
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2018
• Subjects: Autism; Cellular neural network; Electroencephalography; Emotiv Epoch; Autism; Emotiv Epoch; Electroencephalography; Cellular neural network
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2018.05.003

•Brain regional connections in children with autism spectrum disorder (ASD) are analyzed.•Brain regional connections in ASD and control children are compared.•Brain connections are modeled using cellular neural networks and electroencephalography.•The proposed model successfully classifies ASD and control children. 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.