Cluster-based analysis for characterizing dynamic functional connectivity

Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an issue common to most dynamic FC analysis techniques, which is the assumption that the spatial extent of a given network remains constant over time. We increase the voxel size and reduce the spatial resolution to analyze variable FC of the whole resting brain. We hypothesize that the adjacent voxels in resting state functional magnetic resonance imaging (rsfMRI), just as in task-based fMRI, exhibit similar intensities, so they can be averaged to obtain larger voxels without any significant loss of information. Sliding window correlation is used to compute variable patterns of the rat's whole brain FC with the seed voxel in the sensorimotor cortex. These patterns are grouped based on their spatial similarities using binary transformed feature vectors in k-means clustering, not only revealing the variable and nonvariable portions of FC in the resting brain but also detecting the extent of the variability of these patterns.