Prediction of trust propensity from intrinsic brain morphology and functional connectome. Issue 1 (1st October 2020)

• Record Type: Journal Article
• Authors: Feng, Chunliang; Zhu, Zhiyuan; Cui, Zaixu; Ushakov, Vadim; Dreher, Jean‐Claude; Luo, Wenbo; Gu, Ruolei; Wu, Xia; Krueger, Frank
• Is Part Of: Human brain mapping. Volume 42:Issue 1(2021)
• Page Start: 175
• Page End: 191
• Publication Date: 2020-10-01
• Subjects: functional decoding -- gray matter -- individualized prediction -- large‐scale brain networks -- trust propensity; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25215
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: 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. Abstract : 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.