A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI
复值功能磁共振成像脑激活检测的贝叶斯变量选择方法
Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small…