Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-017-11754-4
Title: Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis
Authors: Ding, X.P 
Wu, S.J
Liu, J
Fu, G
Lee, K
Keywords: brain cortex
brain region
child
cognition
deception
female
honesty
human
major clinical study
male
nerve potential
nervous system development
school child
theoretical study
analysis of variance
artificial neural network
biological model
brain
deception
nerve cell network
physiology
Analysis of Variance
Brain
Child
Deception
Female
Humans
Male
Models, Neurological
Nerve Net
Neural Networks (Computer)
Issue Date: 2017
Citation: Ding, X.P, Wu, S.J, Liu, J, Fu, G, Lee, K (2017). Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis. Scientific Reports 7 (1) : 12085. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-017-11754-4
Rights: Attribution 4.0 International
Abstract: The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception. © 2017 The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/178301
ISSN: 20452322
DOI: 10.1038/s41598-017-11754-4
Rights: Attribution 4.0 International
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