Cáceda Ricardo, James G Andrew, Gutman David A, Kilts Clinton D
Brain Imaging Research Center, Psychiatric Research Institute, University of Arkansas for Medical Sciences, 4301 West Markham St., Slot #554, Little Rock, AR 72205, USA.
Biomedical Informatics, Emory University, 36 Eagle Row, 5th Floor South Atlanta, GA 3032, USA.
Behav Brain Res. 2015 Oct 1;292:478-83. doi: 10.1016/j.bbr.2015.07.008. Epub 2015 Jul 9.
Reciprocation of trust exchanges is central to the development of interpersonal relationships and societal well-being. Understanding how humans make pro-social and self-centered decisions in dyadic interactions and how to predict these choices has been an area of great interest in social neuroscience. A functional magnetic resonance imaging (fMRI) based technology with potential clinical application is the study of resting state brain connectivity. We tested if resting state connectivity may predict choice behavior in a social context. Twenty-nine healthy adults underwent resting state fMRI before performing the Trust Game, a two person monetary exchange game. We assessed the ability of patterns of resting-state functional brain organization, demographic characteristics and a measure of moral development, the Defining Issues Test (DIT-2), to predict individuals' decisions to reciprocate money during the Trust Game. Subjects reciprocated in 74.9% of the trials. Independent component analysis identified canonical resting-state networks. Increased functional connectivity between the salience (bilateral insula/anterior cingulate) and central executive (dorsolateral prefrontal cortex/ posterior parietal cortex) networks significantly predicted the choice to reciprocate pro-social behavior (R(2) = 0.20, p = 0.015). Stepwise linear regression analysis showed that functional connectivity between these two networks (p = 0.002), age (p = 0.007) and DIT-2 personal interest schema score (p = 0.032) significantly predicted reciprocity behavior (R(2) = 0.498, p = 0.001). Intrinsic functional connectivity between neural networks in conjunction with other individual characteristics may be a valuable tool for predicting performance during social interactions. Future replication and temporal extension of these findings may bolster the understanding of decision making in clinical, financial and marketing settings.
信任交换的 reciprocation 是人际关系发展和社会福祉的核心。了解人类在二元互动中如何做出亲社会和自我中心的决策以及如何预测这些选择,一直是社会神经科学中一个备受关注的领域。一种具有潜在临床应用价值的基于功能磁共振成像(fMRI)的技术是静息态脑连接性研究。我们测试了静息态连接性是否可以预测社会情境中的选择行为。29名健康成年人在进行信任游戏(一种两人货币交换游戏)之前接受了静息态fMRI检查。我们评估了静息态功能性脑组织结构模式、人口统计学特征以及道德发展测量指标(定义问题测试,DIT-2)预测个体在信任游戏中回报金钱的决策的能力。受试者在74.9%的试验中进行了回报。独立成分分析确定了典型的静息态网络。突显网络(双侧岛叶/前扣带回)和中央执行网络(背外侧前额叶皮层/后顶叶皮层)之间功能连接性的增加显著预测了回报亲社会行为的选择(R(2)=0.20,p=0.015)。逐步线性回归分析表明,这两个网络之间的功能连接性(p=0.002)、年龄(p=0.007)和DIT-2个人兴趣模式得分(p=0.032)显著预测了互惠行为(R(2)=0.498,p=0.001)。神经网络之间的内在功能连接性与其他个体特征相结合,可能是预测社会互动中表现的有价值工具。这些发现的未来重复和时间扩展可能会加强对临床、金融和营销环境中决策的理解。