Network analysis of single-subject fMRI during a finger opposition task.

The analysis of functional magnetic resonance imaging (fMRI) data has typically relied on univariate methods to identify areas of brain activity related to cognitive and behavioral task performance. We investigated the ability of multivariate network analysis using a modified form of principal component analysis, the Scaled Subprofile Model (SSM), applied to single-subject fMRI data to identify patterns of interactions among brain regions over time during an anatomically well-characterized simple motor task. We hypothesized that each subject would exhibit correlated patterns of brain activation in several regions known to participate in the regulation of movement including the contralateral motor cortex and the ipsilateral cerebellum. EPI BOLD images were acquired in six healthy participants as they performed a visually and auditorally paced finger opposition task. SSM analysis was applied to the fMR time series on a single-subject basis. Linear combinations of the major principal components that predicted the expected hemodynamic response to the order of experimental conditions were identified for each participant. These combinations of SSM patterns were highly associated with the expected hemodynamic response, an indicator of local neuronal activity, in each participant (0.84