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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/696D8EEA-74E8-4667-8A4F-6F4529C0F258 | ||
| Year | 2018 | ||
| Type of Item | Diploma Work | ||
| License |
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| Bibliographic Citation | Eleni Petrou, "Brain networks of maximum synchronization from magneto-encephalographic signals", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2018 https://doi.org/10.26233/heallink.tuc.76211 | ||
| Appears in Collections |
The human brain, as one of the most complex biological systems and an inseparable part of our daily actions, has been the object of study over the last few decades to understand its basic functions and, more specifically, to identify its fundamental dysfunctions and disorders. For this reason, there are many systems that have been invented for spatial and temporal imaging of human brain activity. Magnetoencephalography (MEG) is a non-invasive neuroimaging method for detecting spatial information of the brain by recording the magnetic field, generated by electrical activity of the brain. In this thesis are studied the MEG recordings, that were obtained from children with Reading Difficulties (RD Group) and from Non-Impaired children (NI Group). In order to detect and restrict non-cerebral activity, the Independent Component Analysis (ICA) is used. As a result of this method is the export of Independent Components (ICs), that represent cerebral or non-cerebral activity. After that the Functional Connectivity Graph (FCG) is built, through the Phase Lag Index (PLI), which represents the phase synchronization between two signals. In order to ensure the maintenance of the most significant links between the nodes of each FCG, the Global Cost Efficiency (GCE) is calculated. Moreover, in the field of study is also included the estimation of Rich Club topologies, aiming at the pointing out of nodes with high rate of information and the statistical differences on topologies between the two groups are estimated. The statistically significant differences, that occurred between the two groups are mainly appeared above the frontal lobe side and on the left side above the parietal and temporal lobe. These elements are in agreement with previous studies, since the main language centres are located in these areas. Therefore, the use of MEG, in combination with phase synchronization and Rich Club topologies may help in the future in non-invasive pointing out of disorders and consequently in the diagnosis of reading difficulties.
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