A new post-processing method of applying independent component analysis to fMRI data

Independent component analysis (ICA) method can be used to separate fMRI data into some task-related independent components, including one consistently task-related (CTR) and several transiently task-related (TTR) components. However, the weights, with which the CTR and TTRs contribute to the final task component, are often unknown, but are important for finding its relevant spatial activation area. Here we propose a new ICA post-processing method alternative to combine not only these CTR and TTRs which sometimes are judged in a subjective manner, but also others in an effort to identify a comprehended and summed spatial pattern that is responsible for the behavior under investigation. This proposed procedure has been successfully used in principal component analysis (PCA) based scaled subprofile modeling (SSM). Adopting this newly proposed approach, we essentially refer the ICA exploratory findings to a hypothesized temporal brain response pattern (reference function). Basically, we will use linear regression method to seek the relationship between the reference function and time courses of multi components generated from the ICA procedure. The linear regression coefficients are then used as relative weights in generating the final summed spatial pattern. Moreover, this approach allows a researcher to use T-test to statistically infer the importance of each independent component in its contribution to the final pattern and consequently the contribution to the cognitive process. Experiment result also shows that the spatial activation of the final task component becomes more accurate.