Resting-state functional connectome-based prediction of valence bias

Valence bias (VB) refers to an individual's tendency to consistently interpret emotionally ambiguous stimuli as either positive or negative. As such bias may be closely linked to mental health outcomes such as anxiety and depression, investigating its underlying neural mechanisms hold important clinical relevance. Here, we obtained VB scores through a robust performance-based behavioral measure and sought to examine whether the whole-brain resting-state functional connectome could be leveraged to predict VB using connectome-based predictive modeling (CPM). Results highlighted a functional network model that could significantly predict individual VB. Specifically, our analyses revealed distributed patterns of connectivity in brain regions that support functions related to emotion regulation, cognitive control, and perceptual/emotional processing. These regions contained several key nodes - the amygdala, dorsal anterior cingulate cortex, and frontal operculum - that demonstrated predictive value for VB. Extending prior findings linking VB to functional brain organization, our findings demonstrated that VB can be predicted from large-scale functional brain regions using CPM, with several key nodes emerging as particularly influential, and further generalized these findings to a Korean adult sample. As VB reflects an individual's past experiences and interpretive tendencies, understanding the neural underpinnings of VB could assist in identifying potential neurobiological markers of vulnerability and resilience.