《Journal of Affective Disorders》2025年4月1日第374卷

Background: The role of cortical networks in health anxiety remain poorly understood. This study aimed to develop a predictive model for health anxiety, using a machine-learning approach based on resting-state functional connectivity (rsFC) with functional near-infrared spectroscopy (fNIRS).

Conclusion: The findings reveal a predictive role of intrinsic cortical organization in health anxiety and suggest that health anxiety involves complex interactions between cognitive control, emotion regulation, and sensory processing. The work provides new insights into potential neural mechanisms underlying health anxiety, and implications for neuromodulation research and practice targeting severe health anxiety.