Common and distinct neurofunctional representations of core and social disgust in the brain: Coordinate-based and network meta-analyses

Abstract

Disgust represents a multifaceted defensive-avoidance response. On the behavioral level, the response includes withdrawal and a disgust-specific facial expression. While both serve the avoidance of pathogens the latter additionally transmits social-communicative information. Given that common and distinct brain representation of the primary defensive-avoidance response (core disgust) and encoding of the social-communicative signal (social disgust) remain debated we employed neuroimaging meta-analyses to (1) determine brain systems generally engaged in disgust processing, and (2) segregate common and distinct brain systems for core and social disgust. Disgust processing, in general, engaged a bilateral network encompassing the insula, amygdala, occipital and prefrontal regions. Core disgust evoked stronger reactivity in left-lateralized threat detection and defensive response network including amygdala, occipital and frontal regions while social disgust engaged a right-lateralized superior temporal-frontal network engaged in social cognition. Anterior insula, inferior frontal and fusiform regions were commonly engaged during core and social disgust suggesting a common neural basis. We demonstrate a common and separable neural basis of primary disgust responses and encoding of associated social-communicative signals.

Signatures of social pain empathy: general and process-specific brain-wide representations of social exclusion and separation

“…Spatial similarity was quantified using the cosine similarity metric, comparing the activation patterns within the core regions identified through both the PSSE and PSSS models with each individual regional interest (ROI) or network. By employing river plots, we were able to assess the degree of overlap and consistency between the activation patterns associated with social pain empathy and the anatomical parcellations defined by the Brainnetome Atlas 39,50 . All voxels were maintained as positive values for interpretation.…”

“…To develop comprehensive and process-specific neurofunctional models that can describe cognitive and affective experiences with large effect sizes 37 and at a finer spatial scale 38 , machine learning-based multivariate pattern classification approaches have been established during recent years and have been successfully applied to pain, pain empathy, and general and specific affective domains [39][40][41][42][43][44][45][46][47][48] . To establish neural representation models of the psychological processes underlying social pain empathy, taking into account contextual and individual variations on the experience level 49 , we here combined functional magnetic resonance imaging (fMRI) with machine learningbased multivariate pattern analysis (MVPA) 39,[41][42][43]50 .…”

Behavioural, Cognitive and Neural Mechanisms of Human Social Interaction

“…Noxious and disease-related stimuli induce a visceral disgust reaction together with a nose-wrinkled/eyes closed facial expression that may be an attempt to reduced exposure to the disgusting item (Rozin et al, 2008). When participants experience disgust in an fMRI scanner (induced by smells), there is robust engagement of anterior insula and the same region is also activated when participants see a disgust expression on another person's face (Gan et al, 2022;Wicker et al, 2003). Thus, this particular emotion seems to fit the pattern predicted by Ekman.…”

Neural network of metaphor comprehension: an ALE meta-analysis and MACM analysis

“…Independent contrast and conjunction analyses were conducted to identify the convergent and divergent regions in neural activation between (i) novel and conventional metaphors; (ii) the contexts of words, sentences, and discourses; (iii) Germanic and Chinese languages. In accordance with recent studies that have used contrast and conjunction analyses (Gan et al, 2022), the ALE images were thresholded at p < 0.05 (uncorrected), with a minimum cluster size of 100 mm 3 and 10,000 permutations.…”