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Functional Connectivity Network in Rest-State fMRI Baseline in High Functioning Autism Disorder

Akbarian Aghdam, Amir | 2014

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45551 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Fatemizadeh, Emad
  7. Abstract:
  8. Autism spectrum disorders (ASD) have been defined as developmental disorders characterized by abnormalities in social interaction, communication skills, and behavioral flexibility. Over the past decades, studies using various genetic, neurobiological, cognitive and behavioral approaches have sought a single explanation for the heterogeneous manifestations of ASD, but no consensus on the etiology of ASD has emerged. Further studies aim to clarify the mechanism of disease.
    Functional Magnetic Resonance Imaging (fMRI) is a new way of imaging which evaluates activity of brain by measuring magnetic difference caused by oscillation in blood oxygen level. fMRI has been widely used in recent studies due to its favorable features like good spatial resolution.
    Brain areas do not work independently and are correlated strongly. The connectivity of brain exists even in anatomically remote areas. There have been several studies on diseases like Alzheimer and ADHD. using map of functional connectivity in order to know the disease better.
    Various methods have been used to derive functional connectivity map from fMRI images. In this project we have used a data driven method of group Independent Component Analysis and clustering methods like Fuzzy Clustering and Spectral Clustering as well to evaluate connectivity between brain regions. A dimension reduction stage is necessary for fMRI data analysis. We have used RPCA as a recent and robust approach to present data in a low rank subspace.
    The spatial components have been compared for two groups and similarities and differences have been discussed. There are several similarities in brain networks and some differences in Default Mode Network, Motor areas, and Occipital areas.
  9. Keywords:
  10. Functional Connectivity ; Functional Magnetic Resonance Imaging (FMRI) ; Resting State Brain ; Grouped Independent Component Analysis ; Spectral Clustering ; Fuzzy Clustering ; Autism Spectrum Disorders (ASD) ; Robust Principal Component Analysis (RPCA)

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