Do anger perception and the experience of anger share common neural mechanisms? Coordinate-based meta-analytic evidence of similar and different mechanisms from functional neuroimaging studies

Sorella, Sara; Grecucci, Alessandro; Piretti, Luca; Job, Remo

doi:10.1016/j.neuroimage.2021.117777

Cited by 33 publications

(40 citation statements)

References 131 publications

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“…Other studies reported a negative relationship in the local gyrification of this area and impulsivity in BPD (17, 75). In addition, the OFC and the right inferior frontal gyrus seem to play a key role in anger and its regulation (76), which is known to be dysregulated in BPD. Finally, in addition, the cerebellum, among other functions, is also involved in affective evaluation, in interaction with the prefrontal cortex and basal ganglia (77).…”

Section: Discussionmentioning

confidence: 99%

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Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach

et al. 2022

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Previous morphometric studies of Borderline Personality Disorder (BPD) reported inconsistent alterations in cortical and subcortical areas. However, these studies have investigated the brain at the voxel level using mass univariate methods or region of interest approaches, which are subject to several artifacts and do not enable detection of more complex patterns of structural alterations that may separate BPD from other clinical populations and healthy controls (HC). Multiple Kernel Learning (MKL) is a whole-brain multivariate supervised machine learning method able to classify individuals and predict an objective diagnosis based on structural features. As such, this method can help identifying objective biomarkers related to BPD pathophysiology and predict new cases. To this aim, we applied MKL to structural images of patients with BPD and matched HCs. Moreover, to ensure that results are specific for BPD and not for general psychological disorders, we also applied MKL to BPD against a group of patients with bipolar disorder, for their similarities in affective instability. Results showed that a circuit, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC (80%). Notably, this circuit positively correlates with the affective sector of the Zanarini questionnaire, thus indicating an involvement of this circuit with affective disturbances. Moreover, by contrasting BPD with BD, the spurious regions were excluded, and a specific circuit for BPD was outlined. These results support that BPD is characterized by anomalies in a cortico-subcortical circuit related to affective instability and that this circuit discriminates BPD from controls and from other clinical populations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach

et al. 2022

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“…Finally, the search for experiments on retaliatory behaviors was done by screening the reference lists of recent meta-analyses on aggressivity/anger (Puiu et al, 2020; Sorella et al, 2021; Wong et al, 2019). Experiments on retaliatory behaviors were defined by an explicit decision to punish another player that had provoked the participant (e.g., removing points, delivering a shock/noise).…”

Section: Methodsmentioning

confidence: 99%

“…The authors carried out an additional meta-analysis on “ elicited aggression ”, which was more precisely defined (e.g., Taylor aggression paradigm and Violent first-person shooter games), and the meta-analysis yielded activations in the postcentral gyrus (Wong et al, 2019). Finally, in a recent meta-analysis on “ anger experience ”, the authors have included heterogeneous fMRI task domains including retaliatory behaviors, social exclusion and anger imagery (Sorella, Grecucci, Piretti, & Job, 2021), revealing activations in bilateral insula/vlPFC. Despite that these major differences in definition may partially explain the discrepancies in brain regions, it nonetheless illustrates the importance of dissecting homogeneous constructs using theory-driven terminology.…”

Section: Introductionmentioning

confidence: 99%

Neural bases of Frustration-Aggression Theory: A multi-domain meta-analysis of functional neuroimaging studies

Dugré

Potvin

2021

Preprint

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Early evidence suggests that unexpected non-reward may increase the risk for aggressive behaviors. Despite the growing interest in understanding brain functions that may be implicated in aggressive behaviors, the neural processes underlying such frustrative events remain largely unknown. Furthermore, meta-analytic results have produced discrepant results, potentially due to substantial differences in the definition of anger/aggression constructs. Therefore, coordinate-based meta-analyses on unexpected non-reward and retaliatory behaviors in healthy subjects were conducted. Conjunction analyses were further examined to discover overlapping brain activations across these meta-analytical maps. Frustrative non-reward deactivated the orbitofrontal cortex, ventral striatum and posterior cingulate cortex, whereas increased activations were observed in midcingulo-insular regions, as well as dorsomedial prefrontal cortex, amygdala, thalamus and periaqueductal gray, when using liberal threshold. Retaliation activated of midcingulo-insular regions, the dorsal caudate and the primary somatosensory cortex. Conjunction analyses revealed that both strongly activated midcingulo-insular regions. Our results underscore the role of anterior midcingulate/pre-supplementary motor area and fronto-insular cortex in both frustration and retaliatory behaviors. A neurobiological framework for understanding frustration-based impulsive aggression is provided.

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“…Anger is a fundamental emotion that helps us to respond to perceived provocations, hurts or threats (Sorella et al, 2021). In this paper, we explored important aspects of anger by relying on self-report measures of trait anger, anger control, anger-out and anger-in of the STAXI questionnaire.…”

Section: Discussionmentioning

confidence: 99%

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

Sorella

Vellani

Šiugždaitė

et al. 2021

Eur J of Neuroscience

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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.

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Do anger perception and the experience of anger share common neural mechanisms? Coordinate-based meta-analytic evidence of similar and different mechanisms from functional neuroimaging studies

Cited by 33 publications

References 131 publications

Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach

Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach

Neural bases of Frustration-Aggression Theory: A multi-domain meta-analysis of functional neuroimaging studies

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

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