Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning. (15th December 2018)

• Record Type: Journal Article
• Authors: Tang, Honghong; Lu, Xiaping; Cui, Zaixu; Feng, Chunliang; Lin, Qixiang; Cui, Xuegang; Su, Song; Liu, Chao
• Is Part Of: Neuroscience. Volume 395(2018)
• Page Start: 101
• Page End: 112
• Publication Date: 2018-12-15
• Subjects: DLPFC dorsolateral prefrontal cortex -- IPL inferior parietal lobule -- LOOCV leave-one-out cross-validation -- MFC middle frontal cortex -- NB nodal betweenness centrality -- ND nodal degree centrality -- NE nodal efficiency -- OFC orbitofrontal cortex -- ROIs regions of interests -- RSFC resting-state functional connectivity -- RVR relevance vector regression -- TPJ temporo-parietal junction; deception -- individual difference -- neural trait -- machine learning -- cross validation -- resting-state fMRI; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2018.10.036
• Languages: English
• ISSNs: 0306-4522

Abstract

Graphical abstract: Highlights: The neural substrates of individualized deception could be captured by RSFC. A RVR machine-learning approach was used to predict individualized deception by RSFC. The executive controlling, mentalizing, reward networks contribute to the prediction. These networks form a signaling cognitive framework of deception in previous studies. Abstract: Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in deception remain unclear. In this study, we sought to address this issue by employing a machine-learning approach to predict individuals' deceptive propensity based on the topological properties of whole-brain resting-state functional connectivity (RSFC). Participants finished the resting-state functional MRI (fMRI) data acquisition, and then, one week later, participated as proposers in a modified ultimatum game in which they spontaneously chose to be honest or deceptive. A linear relevance vector regression (RVR) model was trained and validated to examine the relationship between topological properties of networks of RSFC and actual deceptive behaviors. The machine-learning model sufficiently decoded individual differences in deception using three brain networks based on RSFC, including the executive controlling network (dorsolateral prefrontal cortex, middle frontal cortex, and orbitofrontal cortex), the social and mentalizing network (the temporal lobe, temporo-parietal junction, and inferior parietal lobule), and the reward network (putamen and thalamus). These networks have been found to form a signaling cognitive framework of deception by coding the mental states of others and the reward or values of deception or honesty, and integrating this information to make a final decision about being deceptive or honest. These findings suggest the potential of using RSFC as a task-independent neural trait for predicting deceptive propensity, and shed light on using machine-learning approaches in deception detection.