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Functional Connectivity Detection in Resting-State Brain using functional Magnetic Resonance Imaging

Ramezani, Mahdi | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40183 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Fatemizadeh, Emadeddin; Soltanianzadeh, Hamid
  7. Abstract:
  8. The functional network of the human brain is altered in many neurological and psychiatric disorders. Characterizing brain activity in terms of functionally segregated regions does not reveal anything about the communication among different brain regions and how such inter-communication could influence neural activity in each local region. The aim of this project is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the simulated, realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. The simulated data were used to investigate the influence of the noise. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis. The proposed method has revealed the functionally connected regions without any prior knowledge.
  9. Keywords:
  10. Functional Magnetic Resonance Imaging (FMRI) ; Functional Connectivity ; Resting State Brain ; Spectral Clustering ; Sparsifying

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