Application Of Independent Component Analysis In Fmri Data Processing

Functional magnetic resonance imaging (fMRI) is a non-invasional tool for the research of brain function, by which the change of the brain in all kinds of behavior can be directly observed.With the development of fMRI technology, a lot of fMRI data processing techniques have been presented and lots of achievements have been reached. At present, fMRI data processing techniques include two types:model-driven and data-driven. This paper mainly studies to extract the brain active regions of the fMRI processing method on the basis of the data-driven。The application of ICA to fMRI data is discussed. Not only the areas of the brain activated by the task separated, but also the areas activated by eye movement and head movement are gained.1. Three popular methods which can process the signals from a group of subjects-Group independent component analysis (Group ICA) are analyzed. This is very useful for processing fMRI data.2. Informax algorithm with an useful Group ICA method are used to deal with the fMRI data from group subjects. And the result shows using Group ICA to process the data from the group subjects not only can reduce the data, raise the speed of operation but also can ensure the accuracy of the result.This dissertation is divided into five chapters. In chapter 1, the paper reviews systematically the present research independent component analysis in the world, followed in Chapter 2 by an introduction to fMRI data processing techniques. Data processing based on SPM is presented in chapter 3. The ICA is involved in chapter 4. Data processing based on ICA is implemented. Finally, a comparison between result from ICA and SPM is given.