Hierarchical clustering analysis for visual - movement response functional MRI data processing

Cognitive experiments involving visual and movement tasks have been intensively studied using functional magnetic resonance imaging (fMRI). A common paradigm of such experiments is that subjects are required to complete different tasks such as visual task or movement task simultaneously. A key problem with these studies is how to identify various cerebral networks, even then all of them were activated and responded to execution of same task. We hypothesized that the differences in spatio-temporal property of various cerebral network and in asymmetry of cerebral networks in bilateral hemispheres allowed us to identify and classify these task-related cerebral networks. In this paper, six datasets from the experiment involving simultaneous bilateral hand movement and classic visual stimulation (i.e. checkerboard stimulation) were analyzed by using hierarchical clustering analysis (HCA) with neighborhood local correlation (NC). The hierarchical result represented that our method can identify and classify various task-related networks. The result of asymmetry on hand moving networks between left hemisphere and right hemisphere also clarified the validity of our method.