Abstract
Neuroticism is characterized by emotional instability and increased susceptibility to stress-related disorders. While traditionally treated as a unitary construct, growing evidence suggests that neuroticism is heterogeneous in both its genetic basis and its effects on social and health outcomes. To quantify this heterogeneity at the neurofunctional level, we analyzed resting-state functional connectivity (RSFC) in a large sample (n = 33,180) from the UK Biobank dataset. Using machine learning regression analysis, we identified RSFC patterns associated with item-level responses from the neuroticism scale of the Eysenck Personality Questionnaire. The pattern of RSFC associations across questionnaire items reflected genetically defined clusters (Worry and Depressed Affect), showing a significant correlation (r = 0.767, p < 0.001). It also aligned with psychometrically derived factors (Anxiety/Tension and Worry/Vulnerability), reflecting a factor structure consistent with prior psychological studies. These findings were replicated in a separate MRI scan session from the UK biobank dataset. Associated connectivity was primarily observed in cognitive control, sensory integration, and self-referential processing networks. These neurofunctional signatures position RSFC as a robust intermediate phenotype, bridging genetic predisposition for neuroticism and psychological states. This framework could enhance precision in predicting psychiatric vulnerability and inform tailored therapeutic interventions.