Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, Macao; Center for Brain Disorders and Cognitive Sciences, Shenzhen University, China; College of Psychology and Sociology, Shenzhen University, China.
Department of psychology, McGill University, Canada.
Neuroimage. 2022 Aug 1;256:119253. doi: 10.1016/j.neuroimage.2022.119253. Epub 2022 Apr 28.
Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.
动机性不诚实是一种典型的因人而异的社会行为。静息态功能磁共振成像(rsfMRI)能够从大脑区域之间的功能连接(FC)中识别出独特的模式。最近的研究已经在静息状态下的大脑网络与西方参与者的不诚实之间建立了联系。为了确定和再现相关的神经模式，并构建一个可解释的模型来预测不诚实，我们分析了两个概念上相似的数据集，其中包含了不同不诚实任务的 rsfMRI 数据。这两个任务都实施了信息传递范式，其中使用金钱奖励来诱导不诚实。我们应用连接组学预测建模(CPM)来构建四个社会大脑网络(奖励、自我参照、道德和认知控制)内和之间的 FC 模型。CPM 分析表明，社会大脑网络的 FC 可以预测不诚实率，特别是奖励网络内的 FC 和自我参照与认知控制网络之间的 FC。我们的研究提供了一个概念上的复制，并通过 rsfMRI 整合模型来预测不诚实，结果表明，频繁的动机性不诚实决策可能需要社会大脑区域的更高参与。
