基于可复制性的独立成分分析——应用于功能磁共振成像数据

This dissertation systematically depicted and improved the application of Independent Component Analysis (ICA) to Functional Magnetic Resonance Imaging (fMRI), following the logic of verification, improvement, extension, and application. The concept of “reproducibility” was the philosophy throughout its four concluded studies. In the “verification” study, ICA was applied to the resting-state fMRI data, verified the resultant components with reproducibility, and examined the consistency of the results from ICA and traditional “seed voxel” method. At the meantime, the limitation of ICA application on fMRI data analysis was presented. In the “improvement” study, an improved ICA algorithm based on reproducibility, RAICAR, was developed to aid some of the limitations of ICA application. RAICAR was able to rank ICA components by reproducibility, determine the number of reliable components, and obtain more stable results. RAICAR provided useful tools for validation and interpretation of ICA results. In the “extension” study, RAICAR as well as the concept of “reproducibility” was extended to multi-subject ICA analysis, and gRAICAR algorithm was developed. gRAICAR allows some variation across subjects, examining common components among subjects. gRAICAR is also capable to detect potential subject grouping on some components. It is a new way for exploratory group analysis on fMRI. In the “application” study, two newly developed methods, RAICAR and gRAICAR, were used to investigate the effect of early music training on the brain mechanism of memory and learning. The results showed brain mechanism difference in memory retrieval and learning process between two groups of subjects. This study also verified the usefulness and importance of the new methods.