Independent Component Analysis And Fmri Brain Image Sequences

ICA, unlike other methods such as SPM and AFNI software for processing functional MR (fMR) imaging data requires no a priori assumptions about the time series. ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance. It predicts that ICA will be most application importance and it will have intrinsic future.Methods: Event-related fMRI was used to investigate the activation of cerebral cortex during movements in 10 normal right-handed volunteers. The movement task consisted of non-delayed (not moving until the end of cue presentation) "simple" (repetitively moving index finger) movements. ICA procedure was used to detect the active brain region. Quantitative analysis was further implemented for comparison of the time series in each ROI between different movement tasks.Results: While the supplementary motor area (SMA) and contralateral sensorimotor cortex (SMC) were activated in all subjects during simple movements, most of subjects showed activation in bilateral premotor cortex (PMC), and bilateral posterior parietal cortex (PPC) during simple movements.Conclusion: SMA and contralateral Ml play a critical role in motor control, involved in motor preparation and exectution respectively. When in tasks with higher spatio-temporal coordination and working memory demanding, PMC and PPC become more active. The present study revealed a distributed cortical network, areas within this network play complemental roles for coordination and execution of movements.In this paper, the author is attempting to apply ICA to the financial datas and natural image denoising, at the same time we give their simulative results.