Hardwired for suspicion. Network-level markers of paranoid personality traits revealed by multimodal machine-learning

Background Current neuroimaging research on paranoid traits remains limited. Existing studies have largely relied on small samples, focused on categorical diagnoses or transient paranoid states, and examined either structural or functional measures in isolation. Crucially, the joint contribution of brain structure and intrinsic functional activity to paranoid personality traits (PPT) in the general population and their relationship with other psychological traits, remains unknown Objectives The present study aimed to identify network-level neural markers of paranoid personality traits in a large sample by integrating gray matter morphology and resting-state brain activity, to test the hypothesis that networks associated with social and affective dysfunctions, predict PTT Methods We applied an unsupervised multimodal data-fusion approach (parallel ICA) to gray matter concentration and fractional ALFF in 197 healthy individuals. Paranoid personality traits were assessed dimensionally. In complementary analyses, we examined whether multimodal component loadings captured trait-related variance beyond demographic covariates Results Analyses identified a resting-state component encompassing the precuneus and angular gyrus, partially overlapping with the default mode network, significantly associated with paranoid personality traits. This functional component was positively correlated with a gray matter component including orbitofrontal and insular regions indicating a linked structural–functional pattern. Conclusions By jointly modeling gray matter and resting-state activity, this study provides the first multimodal evidence of network-level markers underlying paranoid personality traits in the general population.