Power spectrum ranked independent component analysis of a periodic fMRI complex motor paradigm

Independent component analysis (ICA) has been demonstrated to be an effective data-driven method for analyzing fMRI data. However, a method for objective differentiation of task-related components from those that are artifactually non-relevant is needed. We propose a method of constant-cycle (periodic) fMRI task paradigm combined with ranking of spatial ICA components by the magnitude contribution of their temporal aspects to the fundamental task frequency. Power spectrum ranking shares some similarity to correlation with an a priori hemodynamic response, but without a need to presume an exact timing or duration of the fMRI response. When applied to a complex motor task paradigm with auditory cues, multiple task-related activations are successfully identified and separated from artifactual components. These activations include sensorimotor, auditory, and superior parietal areas. Comparisons of task-related component time courses indicate the temporal relationship of fMRI responses in functionally involved regions. Results indicate the sensitivity of ICA to short-duration hemodynamics, and the efficacy of a power spectrum ranking method for identification of task-related components.