No abstract

Appropriately attending to threatening environmental stimuli is evolutionarily adaptive and crucial for survival. This study revealed that nonconscious attentional modulation of disgust has different behavioral and event-related potential (ERP) patterns, as compared to fear and anger. To facilitate its evolutionary purpose of avoidance, disgust first diverts rather than attracts attention. Accordingly, the N1 was smaller in a validly than in an invalidly disgust-cued condition. Furthermore, the frontal P3a for disgust, anger, and fear was found to be larger in the valid than in the invalid condition, which was interpreted as an involuntary switching of attention toward threat-related events to mobilize cognitive resources for action or defense. On the contrary, the parietal P3b only occurred at the conscious level; the enhanced P3b indicated that more cognitive resources were being allocated toward the task-relevant but previously less attended location, to ensure the effective achievement of task goals. In addition, group comparisons between individuals with low and high disgust sensitivity showed that the ERP differences between the disgust and the anger/fear conditions at the unconscious level may be attributed only to individuals with high disgust sensitivity. These findings, together with previous knowledge of the effects of fear and anger on attention, strengthen our confidence in the two-stage scheme of attentional modulation by threats, which consists of an early stage of bottom-up response scaling of sensory processing (reflected by the P1 and N1) and a later stage of top-down integration and regulation of emotion and behavior (reflected by the P3).

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational “lesion” approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.