Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. The main objective of the thesis is to identify this underlying brain
activation over time, using fMRI signal by detecting active and passive voxels. We performed two sub goals sequentially in order to realize the main objective. First, by using simple, data-driven Fourier Wavelet Regularized Deconvolution (ForWaRD) method, we extracted hemodynamic response function (HRF) which is the information that shows either a voxel is active or passive from fMRI signal. Second,
the extracted HRFs of voxels are classified as active and passive using Laplacian Eigenmaps. By this, the active and passive voxels in the brain are identified, and so are the activation areas. The ForWaRD method is directly applied to fMRI signals for the first time. The extraction method is tested on simulated and real block design fMRI signals, contaminated with noise from a time series of real MR images. The output of ForWaRD contains the HRF for each voxel. After HRF extraction, using Laplacian Eigenmaps algorithm, active and passive voxels are classified according to their HRFs. Also with this study, Laplacian Eigenmaps are used for HRF clustering for the first time. With the parameters used in this thesis, the extraction and clustering methods presented here are found to be robust to changes in signal properties. Performance analyses of the underlying methods are explained in terms of sensitivity and specificity metrics. These measurements prove the strength of our presented methods against different kinds of noises and changing signal properties.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12613870/index.pdf |
| Date | 01 November 2011 |
| Creators | Adli Yilmaz, Emine |
| Contributors | Erkmen, Aydan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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