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Updated: May 10, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
Published on: June 20, 2012
Resting-state functional connectome-based prediction of valence bias
Seohyeon Lee1, Wonyoung Kim2, Nayoung Kim3
1Department of Psychology, Sungkyunkwan University, Seoul, South Korea.
This study shows that brain connectivity patterns can predict an individual's valence bias (VB), which is the tendency to interpret ambiguous situations negatively or positively. Understanding these neural markers may help identify risks for mental health conditions.
Area of Science:
- Neuroscience
- Psychology
- Mental Health Research
Background:
- Valence bias (VB) involves consistent interpretation of emotional stimuli as positive or negative.
- VB is linked to mental health conditions like anxiety and depression, making its neural basis clinically relevant.
Purpose of the Study:
- To investigate the neural mechanisms underlying valence bias.
- To determine if whole-brain resting-state functional connectome data can predict individual VB scores using connectome-based predictive modeling (CPM).
Main Methods:
- Collected VB scores using a performance-based behavioral measure.
- Applied connectome-based predictive modeling (CPM) to resting-state functional connectivity data.
- Examined distributed connectivity patterns in brain regions involved in emotion regulation, cognitive control, and perceptual processing.
Main Results:
- A functional network model significantly predicted individual VB.
- Key predictive nodes included the amygdala, dorsal anterior cingulate cortex, and frontal operculum.
- Findings were generalized to a Korean adult sample.
Conclusions:
- VB can be predicted from large-scale functional brain networks using CPM.
- Specific brain regions play influential roles in predicting VB.
- Identifying neural underpinnings of VB may reveal markers for mental health vulnerability and resilience.
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