Study Of Signal Model Of Magnetic Resonance Response And Localization Method Of Brain Functional MRI

Functional magnetic resonance imaging (fMRI) is mainly based on blood oxygenation level dependent (BOLD). It is the most efficient method that can be used to precisely locating brain activities without invasion. With very high spatial resolution and potential high temporal resolution, fMRI is well fit for the spatial and temporal analysis of neural action and the research of advanced brain function. So it is being concerned by many science branches such as neuroscience, cognition and clinic et al and is a hot point in current brain research.Focused on the application of fMRI in brain science, systematic improvements are conducted from neural anatomic localization, fMRI signal model and image registration preprocessing of fMRI. These methods are evaluated with practical visual and auditory fMRI data. Meanwhile, innovative applications are also conducted in a clinical epileptic localization and a study of the brain asymmetry of the left-right and upper-lower visual field. The details are shown as follow:1) Newly proposed is an efficient gradient algorithm of Independent Component Analysis (ICA) based on minimum mutual information, a Newton iterate fast algorithm and BFGS algorithm of ICA based on information maximum likelihood estimation. And a composite ICA algorithm is proposed by combining the merits of the gradient-based algorithm and the fix-point based algorithm, it is of good stability and efficiency.2) Based on the study of ICA algorithm, signal processing models of fMRI data were analyzed. And a signal model based on the independence of temporal courses between signal and noises within a tiny spatial domain were developed, thus a new fMRI data processing: independence component correlation algorithm in a Tiny spatial domain. The new method was evaluated and confirmed by simulation, and real fMRI data.3) Based on the fact that there are asynchronous activations or different response patterns in fMRI process, Proposed is a brain functional localizationmethod, which is an ICA component selection and combination strategy. Applying the method to epileptic activities, a new method of epileptic activities spatial-temporal localization was developed, and its correctness and validity were confirmed by experimental data.4) Based on fMRI data PCA method, proposed is a new delay subspace analysis method, which can pick put weak useful signal and locating brain function activation, PCA is its special instance. So PCA precess method of fMRI data is effectively expanded.5) Based on fMRI data cluster analysis method, proposed is integrated between neighborhood correlation and hierarchical clustering method. Firstly, the neighborhood correlation is implemented to principium imaging, which remove a lot of no activation points, then the hierarchical clustering method is implemented to get the finally imaging image. This method can solve the huge data problem of hierarchical clustering and separate effectively the different patterns of brain activation.6) Besides the above data-driven methods of fMRI signal processing listed in the 2) and 3) above, proposed is a model-driven method, that is a general linear model consisted of the convolution between a new dynamic function and the design matrix. Its validity was confirmed by real visual fMRI data.7) In fMRI signal, there have not only the spatial information of the brain functional activation, but also some more implicit dynamic information of the brain function. Combining Friston's BOLD microscopic dynamic blood model and Agnes Aubert's coupling model of brain electrical activity and metabolism, an extended dynamic BOLD model was proposed, which connected brain metabolism with the blood flow blood volume, thus extended the Friston's model one step ahead in electrophysiological aspect.8) According to the dynamic characteristic of BOLD -fMRI signal, a new model of the fMRI signal was proposed as a convolutions between a Gaussian function and the perfusion function which characterizes the neural response to a stimulus of a neural mass. And an improved Gamma convolution signal model where the convolution is between a Gamma function and the perfusion function...