Detecting overlapped functional clusters in resting state fMRI with Connected Iterative Scan: A graph theory based clustering algorithm

Abstract

The brain is a complex neural network with interleaving functional connectivity among anatomical regions. However, current functional parcellation algorithms usually emphasize independence or orthogonality between the spatial components, with the interleaving nature underrepresented. This study investigates a method, Connected Iterative Scan (CIS), for identifying functionally overlapped anatomical groups with resting state fMRI. CIS iteratively optimizes a grouping of vertexes in a weighted graph, using a density metric computed based on the input and output weights of a candidate cluster. In this study, CIS is able to detect the overlapped clusters in a simulated dataset. CIS also detects that the default mode network and the task positive network, which were known as two anti-correlated networks, are overlapped at the posterior cingulate cortex and the lateral parietal cortex. CIS also detects the conventional functional clusters in the whole brain neural network (e.g., the visual cluster, the motor cluster, the frontal cluster, etc.), as well as meaningful overlaps, and also revealed the possible existence of an emotional memory functional cluster. CIS was able to identify several hub regions actively participating in many clusters. With the ability to reveal overlapping functional clusters, CIS is potentially useful in revealing the delicate architecture of the brain neural network.