What is an Independent Component (IC) and how do I know what each one means?
Independent Component Analysis (ICA) attempts to split the 4D functional data into a set of spatial maps, each with an associated time course. This is a way of breaking up the original data set in a way which does not require the experimental paradigm to be specified and hopefully separates out signals of interest from other signals or artefacts. It is particularly useful when examining data where the timecourse of the response is uncertain. Ideally the result of running ICA will be a set of Independent Components (ICs), some of which are clearly related to activation while some are related to other physiological processes (e.g. respiration, resting-state signals, etc) or to imaging artefacts (e.g. motion, ghosting, slice dropout, noise, etc). An example of several different artefacts can be found in The Little FMRI Shop of Horror. An example of some simple activation-related signals can be found in the FSL Course Examples. There is no automatic way of determining which ICs are artefac
