Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning
| Autor: | Chao Liu, Chunliang Feng, Honghong Tang, Xuegang Cui, Song Su, Zaixu Cui, Xiaping Lu, Qixiang Lin |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Deception media_common.quotation_subject Individuality 050105 experimental psychology Temporal lobe Machine Learning 03 medical and health sciences Young Adult 0302 clinical medicine medicine Humans 0501 psychology and cognitive sciences media_common Brain Mapping Resting state fMRI Ultimatum game General Neuroscience 05 social sciences Brain Inferior parietal lobule Magnetic Resonance Imaging Dorsolateral prefrontal cortex medicine.anatomical_structure Games Experimental Mentalization Orbitofrontal cortex Female Nerve Net Psychology 030217 neurology & neurosurgery Cognitive psychology |
| Zdroj: | Neuroscience. 395 |
| ISSN: | 1873-7544 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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