Brain-Network Feature Recognition of Deception Based on Multivariate Pattern Analysis

Considerable functional MRI (fMRI) studies have shown differences of brain activity between lie-telling and truth-telling. However there are few studies aiming at brain network feature of lie-telling. In this study, we obtained fMRI data of 32 subjects while responding to questions in a truthful, inverse and deceitful manner, then constructed whole-brain functional connectivity networks for the lie-telling and truth-telling conditions based on a canonical template of 116 brain regions, and used a multivariate pattern analysis approach based on machine learning to classify the lie-telling from truth-telling. The results showed that the classifier achieved high classification accuracy (82.03%, 84.38% for lie-telling, 79.69% for truth-telling) and could extract informational functional connectivities that could be used to discriminate lie-telling from truth-telling. These informational functional connectivities were mainly located among networks. These results not only demonstrated good performance when classifying with functional connectivities, but also elucidated the neural mechanism of lie-telling from a functional integration viewpoint.