The ability to experience, use and eventually control anger, is crucial to maintain well-being and build healthy relationships. Despite its relevance, the neural mechanisms behind individual differences in experiencing and controlling anger are poorly understood. To elucidate these points, we employed an unsupervised machine learning approach based on Independent Component Analysis, to test the hypothesis that specific functional and structural networks are associated with individual differences in trait anger and anger control. Structural and functional resting state images of 71 subjects as well as their scores from the State-Trait Anger Expression Inventory entered the analyses. At a structural level, the concentration of gray matter in a network including ventromedial temporal areas, posterior cingulate, fusiform gyrus and cerebellum was associated with trait anger. The higher the concentration, the higher the proneness to experience anger in daily life due to the greater tendency to orient attention toward aversive events and interpret them with higher hostility. At a functional level, the activity of the Default Mode Network was associated with anger control. The higher the DMN temporal frequency, the stronger the exerted control over anger, thus extending previous evidence on the role of the DMN in regulating cognitive and emotional functions in the domain of anger. Taken together, these results show, for the first time, two specialized brain networks for encoding individual differences in trait anger and anger control.

Structural and functional brain networks of individual differences in trait anger and anger control: an unsupervised machine learning study / Sorella, Sara; Vellani, Valentina; Siugzdaite, Roma; Feraco, Paola; Grecucci, Alessandro. - In: EJN. EUROPEAN JOURNAL OF NEUROSCIENCE. - ISSN 1460-9568. - ELETTRONICO. - 2021:1(2021), pp. 1-18. [10.1111/ejn.15537]

Structural and functional brain networks of individual differences in trait anger and anger control: an unsupervised machine learning study

Sorella, Sara;Siugzdaite, Roma;Grecucci, Alessandro
2021

Abstract

The ability to experience, use and eventually control anger, is crucial to maintain well-being and build healthy relationships. Despite its relevance, the neural mechanisms behind individual differences in experiencing and controlling anger are poorly understood. To elucidate these points, we employed an unsupervised machine learning approach based on Independent Component Analysis, to test the hypothesis that specific functional and structural networks are associated with individual differences in trait anger and anger control. Structural and functional resting state images of 71 subjects as well as their scores from the State-Trait Anger Expression Inventory entered the analyses. At a structural level, the concentration of gray matter in a network including ventromedial temporal areas, posterior cingulate, fusiform gyrus and cerebellum was associated with trait anger. The higher the concentration, the higher the proneness to experience anger in daily life due to the greater tendency to orient attention toward aversive events and interpret them with higher hostility. At a functional level, the activity of the Default Mode Network was associated with anger control. The higher the DMN temporal frequency, the stronger the exerted control over anger, thus extending previous evidence on the role of the DMN in regulating cognitive and emotional functions in the domain of anger. Taken together, these results show, for the first time, two specialized brain networks for encoding individual differences in trait anger and anger control.
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Sorella, Sara; Vellani, Valentina; Siugzdaite, Roma; Feraco, Paola; Grecucci, Alessandro
Structural and functional brain networks of individual differences in trait anger and anger control: an unsupervised machine learning study / Sorella, Sara; Vellani, Valentina; Siugzdaite, Roma; Feraco, Paola; Grecucci, Alessandro. - In: EJN. EUROPEAN JOURNAL OF NEUROSCIENCE. - ISSN 1460-9568. - ELETTRONICO. - 2021:1(2021), pp. 1-18. [10.1111/ejn.15537]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/322198
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