Functional and effective connectivity of visuomotor control systems demonstrated using generalized partial least squares and structural equation modeling

Tasks employing parametric variation in movement rate are associated with predictable modulations in neural activity and provide a convenient context for developing new techniques for system identification. Using a multistage approach, we explored the functional and effective connectivity of a visuomotor control system by combining generalized partial least squares (gPLS) with subsequent structural equation modeling (SEM) to reveal the relationships between neural activity and finger movement rate in an experiment involving visually paced left or right thumb flexion. The gPLS in the first analysis stage automatically identified spatially distributed sets of BOLD‐contrast signal changes using linear combinations of sigmoidal basis functions parameterized by kinematic variables. The gPLS provided superior sensitivity in detecting task‐related functional activity patterns via a step‐wise comparison with both classical linear modeling and behavior correlation analysis. These activity patterns were used in the second analysis stage, which employed SEM to characterize the areal regional interactions. The hybrid gPLS/SEM procedure allowed modeling of complex regional interactions in a network including primary motor cortex, premotor areas, cerebellum, thalamus, and basal ganglia, with differential activity modulations with respect to rate observed in the corticocerebellar and corticostriate subsystems. This effective connectivity analysis of visuomotor control circuits showed that both the left and right corticocerebellar and corticostriate circuits exhibited movement rate‐related modulation. The identification of the functional connectivity among regions participating particular classes of behavior using gPLS, followed by the estimation of the effective connectivity using SEM is an efficient means to characterize the neural interactions underlying variations in sensorimotor behavior. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.