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Analysis of Functional Connectivity Among Brain Networks Using FMRI

Rahmati Kargar, Behnam | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52232 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Vosughi Vahdat, Bijan; Amini, Arash
  7. Abstract:
  8. Development of the fMRI imaging method gives the scientists the opportunity to record functional images from the brain with high spatial resolution and several researches were conducted on this field. Autistic people’s brain has functional differences with normal people. In this paper these differences have been studied. At first fMRI datasets from autistic subjects and control have been recorded and preprocessed. Then the independent components from these datasets have been extracted using group ICA method. Any independent component is an image depicting a brain network. There is a time series for each image which shows the temporal variations of each component. In the next step, the components referring to the default mode network has been extracted from all of the components. Autistic people’s brain has the most difference with normal people in the default mode network. The functional connectivity and delay among the extracted brain networks has been calculated. Furthermore, a new algorithm has been proposed as an alternative for the group ICA method. Functional connectivity and delay values among the brain networks of autistic subjects and controls have been calculated using the new algorithm. Simulation results show that the new algorithm works better than the group ICA
  9. Keywords:
  10. Functional Magnetic Resonance Imaging (FMRI) ; Independent Component Analysis (ICA) ; Functional Connectivity ; Brain Functional Network ; Autism Spectrum Disorders (ASD)

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