Emotional Context Sculpts Action Goal Representations in the Lateral Frontal Pole

Abstract

Emotional states provide an ever-present source of contextual information that should inform behavioral goals. Despite the ubiquity of emotional signals in our environment, the neural mechanisms underlying their influence on goal-directed action remains unclear. Prior work suggests that the lateral frontal pole (FPl) is uniquely positioned to integrate affective information into cognitive control representations. We used pattern similarity analysis to examine the content of representations in FPl and interconnected mid-lateral prefrontal and amygdala circuitry. Healthy participants (n = 37; n = 21 females) were scanned while undergoing an event-related Affective Go/No-Go task, which requires goal-oriented action selection during emotional processing. We found that FPl contained conjunctive emotion-action goal representations that were related to successful cognitive control during emotional processing. These representations differed from conjunctive emotion-action goal representations found in the basolateral amygdala. While robust action goal representations were present in mid-lateral prefrontal cortex, they were not modulated by emotional valence. Finally, converging results from functional connectivity and multivoxel pattern analyses indicated that FPl emotional valence signals likely originated from interconnected subgenual anterior cingulate cortex (ACC) (BA25), which was in turn functionally coupled with the amygdala. Thus, our results identify a key pathway by which internal emotional states influence goal-directed behavior.SIGNIFICANCE STATEMENT Optimal functioning in everyday life requires behavioral regulation that flexibly adapts to dynamically changing emotional states. However, precisely how emotional states influence goal-directed action remains unclear. Unveiling the neural architecture that supports emotion-goal integration is critical for our understanding of disorders such as psychopathy, which is characterized by deficits in incorporating emotional cues into goals, as well as mood and anxiety disorders, which are characterized by impaired goal-based emotion regulation. Our study identifies a key circuit through which emotional states influence goal-directed behavior. This circuitry comprised the lateral frontal pole (FPl), which represented integrated emotion-goal information, as well as interconnected amygdala and subgenual ACC, which conveyed emotional signals to FPl.

Keywords: cognitive control; emotion; emotion–cognition interactions; lateral frontal pole; prefrontal organization; representational similarity analysis.

Copyright © 2022 the authors.

Figures

Figure 1.

Emotional valence biases action. A, The trial structure of the Affective Go/No-Go Task is shown. At the start of each condition miniblock (n = 20 trials), participants were asked to press a button (Go) in response to either happy or fearful faces, and to withhold responses (No-Go) following the presentation of nontarget emotional expressions (happy, fearful, or neutral faces). The experiment comprised a total of six fMRI scans, and each fMRI scanner run contained n = 4 miniblocks presented in counterbalanced order. B, RT data for Go trials. Positive emotional valence (happy facial expressions) facilitates approach responses compared with negative valence (fearful expressions), reducing Go reaction times. The violin plot shows the distribution of the valence difference score in RTs (Negative – Positive). C, Task accuracy data. Negative emotional valence facilitates avoidance responses, increasing accuracy for No-Go trials. The violin plot shows the distribution of the valence difference score in No-Go accuracy (Negative – Positive). *p < 0.05, ****p < 0.0001.

Figure 2.

Pattern similarity analysis strategy. A, Neural representational similarity matrices were obtained for each ROI by computing the correlation of multivoxel activity patterns across all trials of each condition obtained from independent scans (i.e., between-runs pattern similarity analysis). Each box denotes a voxel in the ROI, and each row denotes a trial. B, Next, we used condition-specific template matrices to test whether neural similarity matrices were explained by action goal, emotional valence, and/or their interaction (conjunctive emotion * action goal representation). Template matrices tested differential representational distances by condition (discrimination matrices) as well as equivalent representational distances across different conditions (similarity matrices). Lower circles depict the representational structure captured by each regressor.

Figure 3.

Pattern similarity analysis. A–C, A simultaneous regression analysis of orthogonal template matrices on neural similarity matrices using a mixed-effects model revealed that FPl (A) and BLA (C) show evidence of conjunctive emotion * action goal representations, whereas mid-LPFC shows evidence of action goal representations unaltered by emotional valence (B). The y-axis shows the fixed-effects regression weights from the mixed-model regression for each ROI. While emotion and action goal are integrated into a higher-order representation in both FPl and BLA, their representational geometries are flipped in sign. Specifically, emotion–action congruent conditions (e.g., Go Positive or No-Go Negative) are represented closer in a high-dimensional space in FPl relative to incongruent conditions (e.g., Go Negative or No-Go Positive; A), whereas the opposite was observed in the amygdala, wherein emotion–action incongruent trials were represented more similarly than congruent trials (C). The strength of emotion–action conjunctive coding differed significantly across the three ROIs, being significantly stronger in FPl and amygdala compared with mid-LPFC, p values < 0.0001. *p < 0.05.

Figure 4.

Association between task accuracy and the magnitude of conjunctive representation of emotion and action goal in FPl [as indicated by the β-coefficient of the emotion * action goal interaction in the pattern similarity analysis (PSA) regression model shown in Fig. 3A] across participants.

Figure 5.

Decoding of emotional valence and action goal representations in FPl–amygdala circuitry. A–C, Subject-wise and run-wise classifier AUCs for emotional valence and action goal were tested against zero using a mixed-effects model for each ROI; β-coefficients for each regression model are plotted for FPl (A), mid-LPFC (B), and BLA (C). *p < 0.05.

Figure 6.

Association between behavior in the AGNG task and strength of action goal and emotional valence representations in FPl. A, Stronger classifier evidence for action goal in FPl was associated with greater task performance within individuals, as evidenced by a mixed-effect model. B, A similar trend was observed across subjects, wherein action goal classifier evidence in FPl tended to correlate positively with task performance in the AGNG task. C, Stronger classifier evidence for action goal in FPl was inversely associated with affect-to-motor spillover in Go trials, as indicated by the faster reaction times in Go-positive relative to Go-negative trials. D, Conversely, stronger classifier evidence for emotion in FPl correlated with greater affect-to-motor spillover in No-Go trials, as indicated by higher accuracy in No-Go-negative relative to No-Go-positive trials.

Figure 7.

A pathway for emotional information flow from vmPFC (BA25) to FPl. A, The strength of classifier evidence for emotional valence in BA25 (AUC) was a significant predictor of the strength of classifier evidence for emotional valence in FPl (AUC; run-wise mixed model). B, A PPI analysis revealed that the BLA and BA25 functionally coupled during negative emotional processing trials, confirming well known dense anatomic projections between BLA and BA25 (Ghashghaei et al., 2007). C, Further suggesting that BA25 may provide a key source of emotional valence information to FPl, the strength of BA25–FPl coupling during negative emotional processing (PPI) predicted the strength of classifier evidence for emotional valence in FPl (AUC).

Figure 8.

Summary of current results and extant neuroanatomical literature. Black arrows indicate neuroanatomical projections in the nonhuman primate, which are consistent with our findings from PPI and comultivariate decoding analyses. The significant covariation of classifier evidence for emotional valence found between BA25 and the FPl is highlighted in blue, and PPI results are indicated in red.