Group-wise connection activation detection based on DICCCOL

Abstract

Task-based fMRI is widely used to locate activated cortical regions during task performance. In the community of fMRI analysis, the general linear model (GLM) is the most popular method to detect activated brain regions, based on the assumption that fMRI BOLD signals follow well the shape of external stimulus. In this paper, instead of analyzing the voxel-based BOLD signal, we examine the functional connection curves between pairs of brain regions. Specifically, we calculate the dynamic functional connection (DFC) between a pair of our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL), and use the GLM to estimate if DFC time series follow the shape of external stimulus. Since the DICCCOL landmarks possess structural and functional correspondence across subjects and these correspondences also apply to their connections, the mixed-effects model is thus performed to effect sizes estimated from GLM of each corresponding connection across subjects to detect group-wise activation. In other words, we assess the activation of cortical landmarks' dynamic interactions at the group-level. Our experimental results demonstrate that the proposed approach is able to detect reasonable activated connection patterns.