Functional magnetic resonance imaging (fMRI) is an advanced non-invasive data acquisition technique to investigate the
neural activity in human brain. In addition to localize the functional brain regions that is activated by specific cognitive
task, fMRI can also be utilized to measure the task-related functional interactions among the active regions of interest
(ROI) in the brain. Among the variety of analysis tools proposed for modeling the connectivity of brain regions, Granger
causality analysis (GCA) measure the directions of information interactions by looking for the lagged effect among the
brain regions. In this study, we use fMRI and Granger Causality analysis to investigate the effective connectivity of brain
network induced by viewing several kinds of expressional faces. We focus on four kinds of facial expression stimuli:
fearful, angry, happy and neutral faces. Five face selective regions of interest are localized and the effective connectivity
within these regions is measured for the expressional faces. Our result based on 8 subjects showed that there is
significant effective connectivity from STS to amygdala, from amygdala to OFA, aFFA and pFFA, from STS to aFFA
and from pFFA to aFFA. This result suggested that there is an information flow from the STS to the amygdala when
perusing expressional faces. This emotional expressional information flow that is conveyed by STS and amygdala, flow
back to the face selective regions in occipital-temporal lobes, which constructed a emotional face processing network.
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