The Human Centromedial Amygdala Contributes to Negative Prediction Error Signaling during Appetitive and Aversive Pavlovian Gustatory Learning.

Prediction error (PE) is the mismatch between a prior expectation and reality, and it lies at the core of associative learning about aversive and appetitive stimuli. Human studies on fear learning have linked the amygdala to aversive PEs. In contrast, the relationship between the amygdala and PE in appetitive settings and stimuli, unlike those that induce fear, has received less research attention. Animal studies show that the amygdala is a functionally heterogeneous structure. Nevertheless, the role of the amygdala nuclei in PE signaling remains unknown in humans. To clarify the role of two subdivisions of the human amygdala, the centromedial amygdala (CMA) and basolateral amygdala (BLA), in appetitive and aversive PE signaling, we used gustatory pavlovian learning involving eating-related naturalistic outcomes. Thirty-eight right-handed individuals (19 females) participated in the study. We found that surprise with neutral feedback when a reward is expected triggers activity within the left and right CMA. When an aversive outcome is expected, surprise with neutral feedback triggers activity only within the left CMA. Notably, the BLA was not activated by those conditions. Thus, the CMA engages in negative PE signaling during appetitive and aversive gustatory pavlovian learning, whereas the BLA is not critical for this process. In addition, PE-related activity within the left CMA during aversive learning is negatively correlated with neuroticism and positively correlated with extraversion. The findings indicate the importance of the CMA in gustatory learning when the value of outcomes changes and have implications for understanding psychological conditions that manifest perturbed processing of negative PEs.SIGNIFICANCE STATEMENT A discrepancy between a prediction and an actual outcome (PE) plays a crucial role in learning. Learning improves when an outcome is more significant than expected (positive PE) and worsens when it is smaller than expected (negative PE). We found that the negative PE during appetitive and aversive taste learning is associated with increased activity of the CMA, which suggests that the CMA controls taste learning. Our findings may have implications for understanding psychological states associated with deficient learning from negative PEs, such as obesity and addictive behaviors.

Parcellation of the human amygdala using recurrence quantification analysis.

Several previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has rarely been considered in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon's entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.

Functional organization of the human amygdala in appetitive learning.

The amygdala is a small subcortical structure located bilaterally in medial temporal lobes. It is a key region for emotional processes and some forms of associative learning. In particular, the role of the amygdala in processing of negative emotions and aversive learning has been shown in numerous studies. However, involvement of this structure in processing of positive affect and appetitive learning is not fully understood. Previous experiments in animals are not consistent. While some authors implicate only the centromedial part of the amygdala in appetitive learning, the others suggest contribution of both centromedial and basolateral subregions. Although from the evolutionary perspective appetitive learning is equally important as aversive learning, research on the role of the human amygdala and its subregions in appetitive learning is undertaken relatively rarely and the results are not conclusive. Therefore, the aim of this review is twofold: to summarize the current knowledge in this field and to indicate and discuss the factors, which might affect the observed level of the amygdala activity during appetitive learning in humans.