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Abstract Background Experiencing symptoms of psychosis, such as delusions and hallucinations, are observed in general, nonclinical populations. These experiences are sometimes described as psychosis proneness (PP) and potentially part of the psychosis continuum. The directional relationships among various factors contributing to psychosis proneness and its interactions, encompassing both environmental and neural mechanisms, lack comprehensive description. We aimed to identify targets to prevent psychosis proneness and its interactions by characterizing pathways using causal discovery analysis (CDA). Methods Participants were 194 healthy adolescent and young adult twin and sibling pairs aged between 14-24 years from Türkiye. They completed comprehensive assessments evaluating sociodemographic status, environmental risk, general intelligence, self-schema, PP, and working memory (WM) performance during fMRI (37 variables). CDA was applied, a novel machine learning algorithm, to understand the causal relationships of PP. Results The analysis identified negative self-schema as having the largest causal effect among all assessments in PP Effect size (ES)= 0.55. Secondly, social cohesion and trust (SC&T) had a protective causal effect on PP ES= −0.18. Lastly, PP was identified as a direct cause of greater activation in DLPFC (BA9a-BA46v) during manipulation in the WM (ES= 0.14). Conclusions CDA provides directionality of the 37 variables which were not presented earlier. The findings highlight the significance of negative self-schema and SC&T in the general population with PP, emphasizing the potential for preventive interventions targeting these factors. These findings also suggest a role for DLPFC as a potential target in this regard. To our knowledge, this is the first study using data-driven analysis to model causal mechanisms in PP in the general population.
Sahin-Ilikoglu et al. (Thu,) studied this question.