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Connectome-based predictive modeling of individual anxiety

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 anxiety in 76 healthy participants. Using a computational “lesion” method in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM could predict individual anxiety from whole-brain rsFC, especially from limbic areas-whole brain and prefrontal cortex-whole brain. The prediction power of the model significantly decreased from (simulated) lesions of limbic areas, lesions of the connectivity within the limbic system, and lesions of the connectivity between limbic regions and the prefrontal cortex. Although the same model also predicted depression, anxiety-specific networks could be identified independently, centered at the prefrontal cortex. These findings highlight the important role of the limbic system and the prefrontal cortex in the prediction of anxiety. Our work provides evidence for the usefulness of connectome-based modeling of rsFC in predicting individual personality differences and indicates its potential for identifying personality structures at risk of developing psychopathology.