Resting-state functional magnetic resonance imaging analysis with seed definition constrained by regional homogeneity

Researchers have recently focused their attention on the intrinsic functional connectivity (FC) in the brain using resting-state functional magnetic resonance imaging. Seed-based correlation analysis (SCA_C), which correlates a predefined seed region with other voxels in the brain, is a common index for FC. However, definition of seed sizes and locations was ambiguous in previous studies and this may lead to spurious results for people with a unique functional anatomy. To address this issue, this study proposes a novel method (SCA_ReHo) that provides a data-driven seed selection (including sizes and locations) method by incorporating regional homogeneity (ReHo) in the SCA_C method. The disparities between SCA_C and SCA_ReHo methods among 12 healthy participants were evaluated in the FC of default mode network (DMN), task-positive network (TPN), and amygdala network. The SCA_ReHo method bypasses the seed-selection ambiguity and enhances the sensitivity in detecting FC of the DMN, TPN, and amygdala network. This study suggests that the SCA_ReHo method improves the sensitivity of FC analysis and reduces the uncertainty of seed selection. Thus, this method may be particularly useful for psychiatric and neurological investigations.