Network physiology of "fight or flight" response in facial superficial blood vessels.

We introduced a novel framework to identify the dynamic pattern of blood flow changes in the cutaneous superficial blood vessels of the face for "fight or flight" responses through facial thermal imaging. For this purpose, a thermal data set was collected from 32 subjects in a mock crime scenario. Five facial areas including periorbital, forehead, perinasal, cheek and chin were selected on the face. Due to the cause and effect movement of blood in the facial cutaneous vasculature, the effective connectivity approach and graph analysis were used to extract causality features. The effective connectivity was quantified using a modified version of the multivariate Granger causality method among each pair of facial Region of Interests (ROIs). Validation was performed using statistical analysis, and the results demonstrated that the proposed method was statistically significant in detecting the physiological pattern of deceptive anxiety on the face. Moreover, the obtained graph is visualized by different schemes to show these interactions more effectively. We used machine learning techniques to classify our data based on the Granger causality values that result in more than 87% accuracy rate in discriminating between deceptive and truthful subjects.