Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity

Chunliang Feng, Jie Yuan, Haiyang Geng, Ruolei Gu, Hui Zhou, Xia Wu*, Yuejia Luo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

Abstract

Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole-brain resting-state functional connectivity (RSFC). Resting-state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole-brain RSFC and trait narcissism. We demonstrated that the machine-learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level.

Original languageEnglish
Pages (from-to)3701-3712
Number of pages12
JournalHuman Brain Mapping
Volume39
Issue number9
DOIs
Publication statusPublished - Sept 2018
Externally publishedYes

Keywords

  • connectome-based predictive modeling
  • cross validation
  • narcissism
  • resting-state functional connectivity

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