Mapping Transient, Randomly Occurring Neuropsychological Events Using Independent Component Analysis

The feasibility of mapping transient, randomly occurring neuropsychological events using independent component analysis (ICA) was evaluated in an auditory sentence-monitoring fMRI experiment, in which prerecorded short sentences of random content were presented in varying temporal patterns. The efficacy of ICA on fMRI data with such temporal characteristics was assessed by a series of simulation studies, as well as by human activation studies. The effects of contrast-to-noise ratio level, spatially varied hemodynamic response within a brain region, time lags of the responses among brain regions, and different simulated activation locations on the ICA were investigated in the simulations. Component maps obtained from the auditory sentence-monitoring experiments in each subject using ICA showed distinct activation in bilateral auditory and language cortices, as well as in superior sensorimotor cortices, consistent with previous PET studies. The associated time courses in the activated brain regions matched well to the timing of the sentence presentation, as evidenced by the recorded button-press response signals. Methods for ICA component ordering that may rank highly the components of primary interest in such experiments were developed. The simulation results characterized the performance of ICA under various conditions and may provide useful information for experimental design and data interpretation.