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Analysis of magnetoencephalography recordings via complexity of quantized resting states

Zoidis Nikolaos

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Summary

Magnetoencephalography (MEG) is a modern, non-invasive method of measuring the neuronal brain activity by calculating the generated magnetic fields after an external stimulation. MEG has contributed significantly to the study and understanding of the mechanisms and the way the brain functions during its normal operation but also in cases where neurological, psychiatric or degenerative disorders occur, issues that are of great concern to the field of Neuroscience. This thesis aims at the analysis of magnetoencephalographic recordings of children with reading difficulties (RD) and non-impaired (NI) children and the extraction of conclusions on the functional connectivity of brain areas of the children belonging to these two groups. For this purpose methods of compressing or encoding a time signal into a symbolic time series were utilized, facilitating the interpretation of time – recorded information with a finite number of symbols. The method of Independent Components Analysis (ICA) as well as with some metrics from the field of Information Theory were used to detect the non-brain activity, following an appropriate signal pre-processing process. The data was then analyzed into eight different brain rhythms. After that, the data was encoded using the Neural Gas algorithm and the metrics of mutual information and connectivity degree were then used on the encoded data. Statistical analysis was applied to the results of those metrics to detect significant statistical differences between the two groups of children. It is possible that these differences demonstrate differences between the functional connectivity of specific brain areas. These differences confirm findings from the relative literature and indicate some dysfunction in the communication between the two hemispheres, as a decrease in synchronization and connectivity in the RD group is observed.

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