Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis

Xiao Pan Ding; Si Jia Wu; Jiangang Liu; Genyue Fu; Kang Lee

doi:10.1038/s41598-017-11754-4

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis

Xiao Pan Ding^*
, Si Jia Wu
, Jiangang Liu
, Genyue Fu
, Kang Lee

^*Corresponding author for this work

Department of Psychology
National University of Singapore
Eric Jackman Institute of Child Study
University of Toronto
Beijing Jiaotong University
Department of Psychology
Hangzhou Normal University
Department of Psychology
Zhejiang Normal University

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

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.

Original language	English
Article number	12085
Journal	Scientific Reports
Volume	7
Issue number	1
DOIs	https://doi.org/10.1038/s41598-017-11754-4
State	Published - 1 Dec 2017
Externally published	Yes

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title = "Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis",

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.",

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AU - Wu, Si Jia

AU - Liu, Jiangang

AU - Fu, Genyue

AU - Lee, Kang

PY - 2017/12/1

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N2 - 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.

AB - 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.

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Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis

Abstract

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