Computational model for detection of abnormal brain connections in children with autism
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 el...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IMR Press
2018-08-01
|
| Series: | Journal of Integrative Neuroscience |
| Subjects: | |
| Online Access: | https://jin.imrpress.com/fileup/1757-448X/PDF/1545985307347-1197219250.pdf |
| _version_ | 1811234298569687040 |
|---|---|
| author | Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad Teshnehlab |
| author_facet | Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad Teshnehlab |
| author_sort | Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad Teshnehlab |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T11:34:11Z |
| format | Article |
| id | doaj.art-43cf591c47324697b09f5bb2eb6862dc |
| institution | Directory Open Access Journal |
| issn | 1757-448X |
| language | English |
| last_indexed | 2024-04-12T11:34:11Z |
| publishDate | 2018-08-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Journal of Integrative Neuroscience |
| spelling | doaj.art-43cf591c47324697b09f5bb2eb6862dc2022-12-22T03:34:54ZengIMR PressJournal of Integrative Neuroscience1757-448X2018-08-0117323724810.31083/JIN-180075Computational model for detection of abnormal brain connections in children with autismElham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad TeshnehlabIn 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.https://jin.imrpress.com/fileup/1757-448X/PDF/1545985307347-1197219250.pdf|autism|electroencephalography|3d-cellular neural network|wavelet transform|emotiv epoch |
| spellingShingle | Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad Teshnehlab Computational model for detection of abnormal brain connections in children with autism Journal of Integrative Neuroscience |autism|electroencephalography|3d-cellular neural network|wavelet transform|emotiv epoch |
| title | Computational model for detection of abnormal brain connections in children with autism |
| title_full | Computational model for detection of abnormal brain connections in children with autism |
| title_fullStr | Computational model for detection of abnormal brain connections in children with autism |
| title_full_unstemmed | Computational model for detection of abnormal brain connections in children with autism |
| title_short | Computational model for detection of abnormal brain connections in children with autism |
| title_sort | computational model for detection of abnormal brain connections in children with autism |
| topic | |autism|electroencephalography|3d-cellular neural network|wavelet transform|emotiv epoch |
| url | https://jin.imrpress.com/fileup/1757-448X/PDF/1545985307347-1197219250.pdf |
| work_keys_str_mv | AT elhamaskariseyedkamaledinsetarehdanalisheikhanimohammadrezamohammadimohammadteshnehlab computationalmodelfordetectionofabnormalbrainconnectionsinchildrenwithautism |