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

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cam.issuedOnline2021-12-27
dc.contributor.authorSorella, Sara
dc.contributor.authorVellani, Valentina
dc.contributor.authorSiugzdaite, Roma
dc.contributor.authorFeraco, Paola
dc.contributor.authorGrecucci, Alessandro
dc.contributor.orcidSorella, Sara [0000-0001-6080-9467]
dc.date.accessioned2022-01-07T16:48:06Z
dc.date.available2022-01-07T16:48:06Z
dc.date.issued2022-01
dc.date.submitted2021-05-03
dc.date.updated2022-01-07T16:48:06Z
dc.description.abstractThe 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 grey 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 towards aversive events and interpret them with higher hostility. At a functional level, the activity of the default mode network (DMN) 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.
dc.identifier.doi10.17863/CAM.79792
dc.identifier.eissn1460-9568
dc.identifier.issn0953-816X
dc.identifier.otherejn15537
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332346
dc.languageen
dc.language.isoeng
dc.publisherWiley
dc.publisher.urlhttp://dx.doi.org/10.1111/ejn.15537
dc.subjectanger control
dc.subjectbrain networks
dc.subjectmachine learning
dc.subjectresting state
dc.subjectsource-based morphometry
dc.subjecttrait anger
dc.subjectAnger
dc.subjectBrain
dc.subjectBrain Mapping
dc.subjectHumans
dc.subjectIndividuality
dc.subjectMagnetic Resonance Imaging
dc.subjectNeural Pathways
dc.subjectUnsupervised Machine Learning
dc.titleStructural and functional brain networks of individual differences in trait anger and anger control: An unsupervised machine learning study.
dc.typeArticle
dcterms.dateAccepted2021-11-08
prism.publicationNameEur J Neurosci
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1111/ejn.15537

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