IMR Press / JIN / Volume 17 / Issue 3 / DOI: 10.31083/JIN-180075
Open Access Research article
Computational model for detection of abnormal brain connections in children with autism
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1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
5 Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran
*Correspondence: (Seyed Kamaledin Setarehdan)
J. Integr. Neurosci. 2018, 17(3), 237–248;
Submitted: 13 May 2017 | Accepted: 16 November 2017 | Published: 15 August 2018

In neuropsychological disorders significant abnormalities in brain connectivity are observed in some regions. A novel model demonstrates connectivity between different brain regions in children with autism. Wavelet decomposition is used to extract features such as relative energy and entropy from electroencephalograph signals. These features are used as input to a 3D-cellular neural network model that indicates brain connectivity. Results show significant differences and abnormalities in the left hemisphere, (p < 0.05) at electrodes AF3, F3, P7, T7, and O1 in the alpha band, AF3, F7, T7, and O1 in the beta band, and T7 and P7 in the gamma band for children with autism when compared with non-autistic controls. Abnormalities in the connectivity of frontal and parietal lobes and the relations of neighboring regions for all three bands (particularly the gamma band) were detected for autistic children. Evaluation demonstrated the alpha frequency band had the best level of distinction (96.6%) based on the values obtained from a cellular neural network that employed support vector machine methods.

3D-cellular neural network
wavelet transform
Emotiv epoch
Fig. 1.
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