Neurostructural correlation of dispositional self-compassion

Fang Guan, Guanmin Liu* (Corresponding Author), Walker Pedersen, Outong Chen, Sasa Zhao, Jie Sui, Kaiping Peng* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
1 Downloads (Pure)

Abstract

Self-compassion is an important emotion regulation strategy predicting positive psychological health and fewer psychopathological problems, but little is known about its structural neural basis. In the current study, we investigated the neurostructural correlates of dispositional self-compassion and its components using voxel-based morphometry (VBM). We found that self-compassion was inversely correlated with gray matter volume (GMV) in the left dorsolateral prefrontal cortex (DLPFC), which was primarily driven by the reduced self-judgment component. We also found that the mindfulness component was associated with greater GMV in the dorsomedial prefrontal cortex/anterior cingulate cortex and the left supplementary motor area, while the isolation and the over-identification components were both correlated with greater GMV in the right inferior temporal gyrus, and over-identification additionally related to less GMV in visual areas. Our findings suggest that dispositional self-compassion and its components are associated with brain structures in regions involved in emotion regulation, self-referential and emotion processing, with implications for the cognitive and neural mechanisms of self-compassion as well as those underlying the effects of self-compassion on its health outcomes.
Original languageEnglish
Article number107978
Number of pages6
JournalNeuropsychologia
Volume160
Early online date12 Aug 2021
DOIs
Publication statusPublished - 17 Sep 2021

Keywords

  • self-compassion
  • self-judgement
  • mindfulness
  • emotion regulation
  • voxel-based morphometry

Fingerprint

Dive into the research topics of 'Neurostructural correlation of dispositional self-compassion'. Together they form a unique fingerprint.

Cite this