Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China.
School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
Hum Brain Mapp. 2021 Jan;42(1):175-191. doi: 10.1002/hbm.25215. Epub 2020 Oct 1.
Trust forms the basis of virtually all interpersonal relationships. Although significant individual differences characterize trust, the driving neuropsychological signatures behind its heterogeneity remain obscure. Here, we applied a prediction framework in two independent samples of healthy participants to examine the relationship between trust propensity and multimodal brain measures. Our multivariate prediction analyses revealed that trust propensity was predicted by gray matter volume and node strength across multiple regions. The gray matter volume of identified regions further enabled the classification of individuals from an independent sample with the propensity to trust or distrust. Our modular and functional decoding analyses showed that the contributing regions were part of three large-scale networks implicated in calculus-based trust strategy, cost-benefit calculation, and trustworthiness inference. These findings do not only deepen our neuropsychological understanding of individual differences in trust propensity, but also provide potential biomarkers in predicting trust impairment in neuropsychiatric disorders.
信任是几乎所有人际关系的基础。尽管信任存在显著的个体差异，但驱动其异质性的神经心理学特征仍不清楚。在这里，我们在两个独立的健康参与者样本中应用了一个预测框架，以检验信任倾向与多模态脑测量之间的关系。我们的多元预测分析表明，信任倾向可以由多个区域的灰质体积和节点强度来预测。确定区域的灰质体积进一步使我们能够从具有信任或不信任倾向的独立样本中对个体进行分类。我们的模块化和功能解码分析表明，这些贡献区域是三个大规模网络的一部分，这些网络与基于微积分的信任策略、成本效益计算和可信度推断有关。这些发现不仅加深了我们对信任倾向个体差异的神经心理学理解，而且为预测神经精神障碍中的信任障碍提供了潜在的生物标志物。
