Rotten to the core – a neurofunctional signature of subjective core disgust generalizes to oral distaste and socio-moral contexts

Abstract

While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to sociomoral contexts. In a series of studies, we combined functional magnetic resonance imaging (fMRI) with machine-learning based predictive modeling to establish a comprehensive neurobiological model of subjective disgust. The neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n=78) and pre-registered validation (n=30) cohorts and generalized across core disgust (n=26) and sociomoral (unfair offers; n=43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems while the signatures most accurately predicted the respective target experience. We provide an accurate fMRI-signature for disgust with a high potential to resolve ongoing evolutionary and clinical debates.

Distinct neural computations scale the violation of expected reward and emotion in social transgressions

“…However, we did not obtain a multivariate decoder that could robustly and sensitively capture variation of emotional PEs. Despite that, our recent study utilizing the data from this modified UG paradigm have determined that unfair offers indeed evoke a strong aversive emotional response within subcortical regions for avoidance responses (amygdala, PAG, thalamus, putamen) and cortical systems involved in emotional appraisal such as the insula, dorsal ACC and lateral frontal regions 30 . This may provide some inspiration for understanding the possible neural pathways underlie the emotional PEs and could further support spatial dissimilarity between emotional and reward PEs, since the reward PE was implicated in a concentrated frontal-insular network encompassing the left ACC, right vlPFC and pINS.…”

“…Compared to the traditional univariate analyses, the machine-learning based multivariate pattern analyses can provide more comprehensive and precise neural representations of cognitive and mental processes 30,31 . Therefore we utilized a linear support vector machine (C = 1, linear kernel) implemented in Canlabcore tool (https://github.com/canlab/CanlabCore) with a leave-one-out cross validation procedure to get differentiate neural patterns of reward and emotional PEs separated by punishment and accept decisions.…”

“…The 20 reward and emotional PEs were sorted with descend sequence and were then divided into 5 levels (i.e., 5,4,3,2,1) in each run for each subject. Next, we employed support vector regression analyses using a linear kernel (C= 1) (in line with our previous works 30,31 ) implemented in the Spider toolbox (http:// people.kyb.tuebingen.mpg.de/spider) with individual beta maps (one per PE level for each subject) as features to predict participants true PE value. To facilitate a robust determination of the predictive accuracy of the neurofunctional signature we employed various metrics including correlation and forced-choice classification accuracy.…”

“…1c). On the neural level, we aimed to investigate: 1) the common and distinct neural systems that support the prediction and experience of reward and emotion during social decisions, 2) whether distinct multivariate neural patterns are sensitive to capture variations of emotional and reward PEs and the associated punishment decision using machine-learning based neural decoding approaches (given the higher precision of this approach to establish process specific neural signatures 30,31 ), 3) whether the reward or emotional neurofunctional PE representation predict the neurofunctional decision to reject an offer and thus to characterize the different roles the PEs play in punishment decisions (Fig. 1c).…”

Capturing dynamic fear experiences in naturalistic contexts: An ecologically valid fMRI signature integrating brain activation and connectivity

“…To overcome these limitations, fMRI has been combined with multivariate pattern-recognition techniques to establish activation-based brain models for complex subjective experiences including pain(18, 19), negative affect(20, 21) and fear(10, 11). While these models have enabled the development of more precise and comprehensive neural models for emotional experiences(10, 11, 20–25), their capacity for ecologically valid prediction of dynamic emotional experiences in real-world settings remains unexplored. Given the significant differences between laboratory settings and natural environments where fear naturally arises(26), it is imperative to evaluate the sensitivity, specificity, and generalizability of fear-related signatures in predicting dynamic emotional experiences in everyday life, and to establish an ecologically valid neural model for subjective fear in natural environments(2–4, 21, 27).…”