Individual differences in the expression and control of anger are encoded in the same fronto-temporal GM-WM network

Anger can be deconstructed into distinct components: a temporary emotional state (state anger), a stable personality trait (trait anger), a tendency to outwardly express it (anger-out), or to internally suppress it (anger-in), and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger are of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM-WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM-WM network. As expected, this network included fronto-temporal regions as well as the cingulate, the insula and the precuneus. The higher the concentration of GM-WM in this brain network, the higher level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.