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Constructing EEG-Based Brain Functional Connectome Using Network-based Statistics

Barzegaran, Elham | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44697 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jalili, Mahdi
  7. Abstract:
  8. In recent years, there have been increasing attempts to study brain connectivity. Among a number of brain mapping techniques, Electroencephalography is an easy to use and cheap method that can be used in the study of brain function. One way of understanding the intricate wiring pattern and functions of brain is to consider it as a complex network. In this approach, a graph of brain functions, based on the functional relation of recorded electric signals, is constructed and then the network is evaluated with a number of network metrics that measure its different aspect of structure. Different neurological and psychological diseases can affect the connectivity power within the brain; as a result, network structure may alter. These differences can be detected by comparing the connectivity between control and patient groups. In this study, our goal is to investigate the effects of diseases such as Alzheimer and PNES and how they change the brain functional connectivity. To this end, complex networks approach as an evaluation measure of whole structure of brain, alongside Network Based statistic as a method to directly assess connections is utilized. With this approach, a significant correlation between PNES network metrics and clinical data was obtained. In addition, in Alzheimer disease connectivity differences between some brain regions was detected. Synchronization measures as a base of functional connectivity was studied and classification accuracy of datasets using different synchronization measures were evaluated
  9. Keywords:
  10. Brain Functional Network ; Electroencephalography ; Network Analysis ; Synchronisation ; Network Based Statistics

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