Evidence for lateralized functional connectivity patterns at rest related to the tendency of externalizing or internalizing anger

Non-angry aggressive arousal and angriffsberietschaft: A narrative review of the phenomenology and physiology of proactive/offensive aggression motivation and escalation in people and other animals

Human aggression typologies largely correspond with those for other animals. While there may be no non-human equivalent of angry reactive aggression, we propose that human proactive aggression is similar to offense in other animals' dominance contests for territory or social status. Like predation/hunting, but unlike defense, offense and proactive aggression are positively reinforcing, involving dopamine release in accumbens. The drive these motivational states provide must suffice to overcome fear associated with initiating risky fights. We term the neural activity motivating proactive aggression "non-angry aggressive arousal", but use "angriffsberietschaft" for offense motivation in other animals to acknowledge possible differences. Temporal variation in angriffsberietschaft partitions fights into bouts; engendering reduced anti-predator vigilance, redirected aggression and motivational over-ride. Increased aggressive arousal drives threat-to-attack transitions, as in verbal-to-physical escalation and beyond that, into hyper-aggression. Proactive aggression and offense involve related neural activity states. Cingulate, insular and prefrontal cortices energize/modulate aggression through a subcortical core containing subnuclei for each aggression type. These proposals will deepen understanding of aggression across taxa, guiding prevention/intervention for human violence.

Decoding individual differences in expressing and suppressing anger from structural brain networks: A supervised machine learning approach

Anger can be broken down into different elements: a transitory state (state anger), a stable personality feature (trait anger), a tendency to express it (anger-out), or to suppress it (anger-in), and the ability to regulate it (anger control). These elements are characterized by individual differences that vary across a continuum. Among them, the abilities to express and suppress anger are of particular relevance as they determine outcomes and enable successful anger management in daily situations. The aim of this study was to demonstrate that anger suppression and expression can be decoded by patterns of grey matter of specific well-known brain networks. To this aim, a supervised machine learning technique, known as Kernel Ridge Regression, was used to predict anger expression and suppression scores of 212 healthy subjects from the grey matter concentration. Results show that individual differences in anger suppression were predicted by two grey matter patterns associated with the Default-Mode Network and the Salience Network. Additionally, individual differences in anger expression were predicted by a circuit mainly involving subcortical and fronto-temporal regions when considering whole brain grey matter features. These results expand previous findings regarding the neural bases of anger by showing that individual differences in specific anger-related components can be predicted by the grey matter features of specific networks.