Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy.
Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK.
Eur J Neurosci. 2022 Jan;55(2):510-527. doi: 10.1111/ejn.15537. Epub 2021 Dec 27.
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 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.
体验、使用和最终控制愤怒的能力对于维持健康和建立健康的人际关系至关重要。尽管这一点很重要，但个体在体验和控制愤怒方面的差异背后的神经机制仍知之甚少。为了阐明这些观点，我们采用了一种基于独立成分分析的无监督机器学习方法来检验以下假设：特定的功能和结构网络与特质愤怒和愤怒控制的个体差异有关。分析中使用了 71 名被试的结构和功能静息态图像以及他们在状态-特质愤怒表达量表中的得分。在结构水平上，包括腹内侧颞叶、后扣带回、梭状回和小脑在内的网络中灰质的浓度与特质愤怒有关。浓度越高，由于人们更倾向于将注意力集中在令人不快的事件上，并以更高的敌意来解释这些事件，因此在日常生活中体验愤怒的倾向就越高。在功能水平上，默认模式网络（DMN）的活动与愤怒控制有关。DMN 的时间频率越高，对愤怒的控制就越强，从而扩展了 DMN 在愤怒领域调节认知和情绪功能方面的作用的先前证据。总之，这些结果首次表明，有两个专门的大脑网络用于编码特质愤怒和愤怒控制的个体差异。
