ObjectivesAcceptance is an adaptive emotion regulation strategy characterized by an open and non-judgmental attitude toward mental and sensory experiences. While a few studies have investigated the neural correlates of acceptance in task-based fMRI studies, a gap remains in the scientific literature regarding dispositional use of acceptance, and how this is reflected at a structural level. Therefore, the aim of the present study was to investigate the neural and psychological differences between infrequent acceptance users (i.e., low accepters) and frequent users (i.e., high accepters). Another question was whether high and low accepters differ in personality traits and emotional intelligence.MethodFor the first time, we applied a data fusion unsupervised machine learning approach (mCCA-jICA) to the gray matter (GM) and white matter (WM) of high accepters (n = 50), and low accepters (n = 78) to possibly detect joint GM-WM differences in both modalities.ResultsOur results show that two covarying GM-WM networks separate high from low accepters. The first network showed decreased GM-WM concentration in a fronto-temporal-parietal circuit largely overlapping with the default mode network in high accepters compared to low accepters. The second network showed increased GM-WM concentration in portions of the orbito-frontal, temporal, and parietal areas, which may correspond to a central executive network, also in high accepters compared to low accepters. At the psychological level, the high accepters displayed higher openness to experience compared to low accepters.ConclusionsOverall, our findings suggest that high accepters compared to low accepters differ in neural and psychological mechanisms. These findings confirm and extend previous studies on the relevance of acceptance as a strategy associated with well-being.
