Analysis of auditory fMRI recordings via ICA: a study on consistency

We apply a blind source separation approach to the identification of statistically independent spatial patterns of brain activation to auditory stimulation. Stimuli consisted of spoken text. The data was collected via functional magnetic resonance imaging (fMRI). As expected from standard processing of fMRI, we observe that independent component analysis (ICA) reveals spatial patterns with similar temporal activation as the stimulus. In these, ICA further distinguishes between the primary auditory areas and Broca's and Wernicke's, which are associated with speech production and understanding, respectively. Furthermore, we observe the activation of the thalamus, with a time course unrelated to the stimulus, hence hard to detect in a classical manner. We observe as well a temporally evolving artifact, related to inefficient filtering of the fMRI scans. The consistency of the estimated signals is tested by running the algorithm with many different initial conditions. The solutions found are combined according to their similarities. Estimates that differ greatly from run to run are less likely to correspond to true components, whereas those that present small variances are considered reliable ones.