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.