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Updated: Dec 23, 2025

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
Published on: August 26, 2011
Meta-analytic evidence for a joint neural mechanism underlying response inhibition and state anger
Andrei A Puiu1,2,3, Olga Wudarczyk3,4, Gregor Kohls1
1Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
Abstract:
Although anger may weaken response inhibition (RI) by allowing outbursts to bypass deliberate processing, it is equally likely that RI deficits precipitate a state of anger (SA). In adolescents, for instance, anger occurs more frequently and often leads to escalating aggressive behaviors. Even though RI is considered a key component in explaining individual differences in SA expression, the neural overlap between SA and RI remains elusive. Here, we aimed to meta-analytically revisit and update the neural correlates of motor RI, to determine a consistent neural architecture of SA, and to identify their joint neural network. Considering that inhibitory abilities follow a protracted maturation until early adulthood, we additionally computed RI meta-analyses in youths and adults. Using activation likelihood estimation, we calculated twelve meta-analyses across 157 RI and 39 SA experiments on healthy individuals. Consistent with previous findings, RI was associated with a broad frontoparietal network including the anterior insula/inferior frontal gyrus (aI/IFG), premotor and midcingulate cortices, extending into right temporoparietal areas. Youths showed convergent activity in right midcingulate and medial prefrontal areas, left aI/IFG, and the temporal poles. SA, on the other hand, reliably recruited the right aI/IFG and anterior cingulate cortex. Conjunction analyses between RI and SA yielded a single convergence cluster in the right aI/IFG. While frontoparietal networks and bilateral aI are ubiquitously recruited during RI, the right aI/IFG cluster likely represents a node in a dynamically-adjusting monitoring network that integrates salient information thereby facilitating the execution of goal-directed behaviors under highly unpredictable scenarios.
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