Functional networks gating the flow of information in cued affective processing

Introduction: Expectancy shapes our perception of impending events (Walter et al., 2009). Although an aberrant interplay between cognitive and affective processes characterizes many mental disorders (Zhang et al., 2017), it is not well understood how top-down expectancy signals modulate future affect. We therefore propose a novel method tracking the information flow in the brain during cognitive and affective processing segregated in time. Methods: Participants in two independent fMRI studies (n = 37 in the exploration study, n = 84 in the replication study) were instructed to expect a positive, negative or neutral stimulus based on a cue which was then followed by an emotional picture congruent with expectancy. Also, resting-state data was acquired from the participants. Within both studies functional and resting-state BOLD MRI time-series were extracted from regions that showed high activations for expectancy phases as well as from ROIs in the pgACC and dmPFC that were associated with the picture viewing phases (expectedness). Cross-correlations between the expectancy time-series and the two expectedness ROI times-series were calculated for the task and resting-state data separately and subsequently used to obtain task-specific (task minus rest) time-lagged connectivity measures. To identify regions showing the highest connectivity at the time shift associated with the duration between expectancy and picture viewing phase, factorial and hierarchical linear modeling was used. Results: The factorial model including the factors modality (task, rest), ROI (pgACC, dmPFC) and lag (4, -4) showed a significant modality x region x lag interaction in the anterior insula (exploration: p = 0.047, replication: p = 0.039, conjunction: p = 0.013, cluster-level corrected). To further analyze this anterior insula based modulation of the ROIs in opposite directions (figure 1), a hierarchical linear model was run based on the time-lagged connectivity between the anterior insula seed and the pgACC expectedness ROI. This revealed a significant effect of the linear lag (exp.: p = 0.008, repl.: p = 0.002) and dmPFC – anterior insula connectivity (exp.: p < 0.001, repl.: p < 0.001). The squared lag component was only significant in the exploration study (exp.: p = 0.002, repl.: p = 0.112). Conclusions: Using cross-correlations of fMRI time-series we find that the magnitude of expectancy signals modulated the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. We further demonstrate that expectancy signals in the anterior insula foreshadow this opposing pattern in the prefrontal cortex. Also, the results are replicated in an independent dataset, showing that the cross-correlation approach reliably uncovers the dynamics of information flow across datasets. Thus, we uncovered that the anterior insula serves as a hub determining the recruitment of distinct prefrontal networks at the affective stage. Taken together, our proposed method provides new insights into neuronal pathways channeling cognition and emotion within well-defined brain networks and could lead to new approaches to track aberrant information processing within mental disorders