  Acceptance 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 in dispositional use
of acceptance, and how this is sedimented at a structural level. Therefore, the
aim of the present study is to investigate the neural and psychological
differences between infrequent acceptance users (i.e., low accepters) and
frequent users (i.e., high accepters). Another question is whether high and low
accepters differ in personality traits and emotional intelligence. To this aim,
we applied, for the first time, 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 find joint GM-WM
differences in both modalities. Our 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, while the second network showed increased GM-WM
concentration in portions of the orbito-frontal, temporal, and parietal areas,
related to a Central Executive Network. At the psychological level, the high
accepters display higher openness to experience compared to low accepters.
Overall, 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.
