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Activation Detection in fMRI Using Nonlinear Time Series Analysis

Taalimi, Ali | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39196 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Fatemizadeh, Emadeddin
  7. Abstract:
  8. Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the relation between input and output of system to be linear time invariant. After extending the Event-Related experiments the importance of non-linear analysis of fMRI were more than before. Also, these methods have the powerful ability to obtain hemodynamic response function. The nonlinear methods devide into two groups: physiological and nonphysiological. In first division, the physiological parameters which enroll between input and output of the system as state variables are considered. In contrast, in second devision, the methods do not refer to all the hidden state variables that mediate between the input and output (e.g., blood flow). This renders them very powerful because they provide for a complete specification of the dynamical behaviour of a system without ever having to measure the state variables or making any assumptions about how these variables interact to produce a response. In this thesis we analyze fMRI data with both methods to find active regions and propose new methods to extract active regions using nonlinear time series.
  9. Keywords:
  10. Nonlinear Model ; Time Series Analysis ; Nonlinear Analysis ; Activation Detection ; Functional Magnetic Resonance Imaging (FMRI)

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