Study Of Data Process Method And Application Based On Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) is mainly based on blood oxygenation level dependent (BOLD). It is the most efficient instrument that can be used to precisely locate brain function 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.Focused on the application of fMRI in brain science, systematic improvements are conducted from MRI image segmentation and registration, brain functional quantitative analysis, brain function activation analysis based on independent component analysis and support vector machine. Meanwhile, combined with hot question of cognitive science, brain functional activation application study is implemented. The details are shown as follow:1)A new image registration algorithm based on the mutual information was proposed. As the measured function of scaling the similarity of two images, the mutual information reach to maximization when the images registration of two pictures is well done. Powell direct searching method is adopted to accelerate the image registration and avoid calculating gradient. An experiment result demonstrates that our method can achieve the registration of multimodal medical image precisely, and could reach sub-pixel precision.2) Technique of data processing on functional magnetic resonance imaging (fMRI) by using spatial independent component analysis (sICA) method in the resting state was proposed. Firstly, the low-frequency oscillations theory is applied to the choice of components of interest (COI) by sICA method. The activation voxels and noise voxels are specified by Z value of separated sICA component. Then COI whose energy concentrates between 0.01Hz and 0.1Hz were chosen through spectrum analysis. And then, the functional connectivity networks were obtained using hierarchical clustering. Finally, by analyzing and evaluating the results, It has proved that our method can be feasible and our results can be related to the frequency method of the time courses.3) SPM(Statistical parameter mapping) is adopted to process fMRI data .Then principal components analysis is proposed to make time series data compression. These comprehensive methods are combined to train fMRI data. Finally, the weight vector that manifests the cerebrum activity difference was acquired by selecting most superior nuclear function of support vector mapping. The region of cerebrum activity was detected in the state of right and left hands movement activity.4) The norm of fMRI blood oxygenation level dependent (BOLD) signal percent change was introduced to quantitatively measure BOLD signal intensity change difference between left and right motor areas. The results of the data collected from six subjects show that the norm of BOLD signal percent change in right motor area is higher than that in left motor area both for two hand movement and single hand movement with right handedness. These results from fMRI show the asymmetry of motor areas and may suggest that the left brain motor area comes into being as an adaptation system, which only need few neuron cell to finish movement task for right handedness. The left motor area activation intensity is reduced with normal right finger movement. The right motor activation intensity is higher than the left motor activation intensity.