Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach

doi:10.1016/j.neuroscience.2018.05.052

	Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach
	Chunliang Feng 1,2,3; Zhiyuan Zhu 1; Ruolei Gu4,5 ; Xia Wu 1; Yue-Jia Luo3,6,7 ; Frank Krueger 8,9
第一作者	Chunliang Feng
通讯作者邮箱	wuxia@bnu.edu.cn (x. wu) ; luoyj@szu.edu.cn (y.-j. luo).
心理所单位排序	4
摘要	A large number of studies have demonstrated costly punishment to unfair events across human societies. However, individuals exhibit a large heterogeneity in costly punishment decisions, whereas the neuropsychological substrates underlying the heterogeneity remain poorly understood. Here, we addressed this issue by applying a multivariate machine-learning approach to compare topological properties of resting-state brain networks as a potential neuromarker between individuals exhibiting different punishment propensities. A linear support vector machine classifier obtained an accuracy of 74.19% employing the features derived from resting-state brain networks to distinguish two groups of individuals with different punishment tendencies. Importantly, the most discriminative features that contributed to the classification were those regions frequently implicated in costly punishment decisions, including dorsal anterior cingulate cortex (dACC) and putamen (salience network), dorsomedial prefrontal cortex (dmPFC) and temporoparietal junction (mentalizing network), and lateral prefrontal cortex (central-executive network). These networks are previously implicated in encoding norm violation and intentions of others and integrating this information for punishment decisions. Our findings thus demonstrated that resting-state functional connectivity (RSFC) provides a promising neuromarker of social preferences, and bolster the assertion that human costly punishment behaviors emerge from interactions among multiple neural systems. (C) 2018 IBRO. Published by Elsevier Ltd. All rights reserved.
关键词	costly punishment fairness ultimatum game machine learning support vector machine cross validation
	2018
DOI	10.1016/j.neuroscience.2018.05.052
发表期刊	NEUROSCIENCE
ISSN	0306-4522
卷号	385 页码:25-37
期刊论文类型	实证研究
收录类别	SCI ; SSCI
Q分类	Q2
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/34153
专题	中国科学院行为科学重点实验室
作者单位	1.College of Information Science and Technology, Beijing Normal University, Beijing, China 2.State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China 3.Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China 4.Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China 5.University of Chinese Academy of Sciences, Beijing, China 6.Medical School, Kunming University of Science and Technology, Kunming, China 7.Center for Emotion and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China 8.School of Systems Biology, George Mason University, Fairfax, VA, USA 9.Department of Psychology, University of Mannheim, Mannheim, Germany
推荐引用方式 GB/T 7714	Chunliang Feng,Zhiyuan Zhu,Ruolei Gu,et al. Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach[J]. NEUROSCIENCE,2018,385:25-37.
APA	Chunliang Feng,Zhiyuan Zhu,Ruolei Gu,Xia Wu,Yue-Jia Luo,&Frank Krueger.(2018).Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach.NEUROSCIENCE,385,25-37.
MLA	Chunliang Feng,et al."Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach".NEUROSCIENCE 385(2018):25-37.

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