A Generalizable Multivariate Brain Pattern for Interpersonal Guilt

  • Hongbo Yu*
  • , Leonie Koban
  • , Luke J. Chang
  • , Ullrich Wagner
  • , Anjali Krishnan
  • , Patrik Vuilleumier
  • , Xiaolin Zhou
  • , Tor D. Wager
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed guilt-related brain signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social-affective processes, further indicating the specificity of the brain state it represents. These findings provide a step toward developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

Original languageEnglish
Pages (from-to)3558-3572
Number of pages15
JournalCerebral Cortex
Volume30
Issue number6
DOIs
StatePublished - 18 May 2020
Externally publishedYes

Keywords

  • brain signature
  • cross-culture
  • fMRI
  • guilt
  • multivariate pattern analysis

Fingerprint

Dive into the research topics of 'A Generalizable Multivariate Brain Pattern for Interpersonal Guilt'. Together they form a unique fingerprint.

Cite this