Unraveling the Influence of Age, IQ, Education, and Negative Symptoms on Neurocognitive Performance in Schizophrenia: A Conditional Inference Tree Analysis

Introduction The complex nature of neurocognitive impairment in schizophrenia has been discussed in light of the mixed effects of antipsychotic drugs, psychotic symptoms, dopamine D ₂ receptor blockade, and intelligence quotient (IQ). These factors have not been thoroughly examined before. Methods This study conducted a comprehensive re-analysis of the CATIE data using machine learning techniques, in particular Conditional Inference Tree (CTREE) analysis, to investigate associations between neurocognitive functions and moderating factors such as estimated trough dopamine D ₂ receptor blockade with risperidone, olanzapine, or ziprasidone, Positive and Negative Syndrome Scale (PANSS), and baseline IQ in 573 patients with schizophrenia. Results The study reveals that IQ, age, and education consistently emerge as significant predictors across all neurocognitive domains. Furthermore, higher severity of PANSS-negative symptoms was associated with lower cognitive performance scores in several domains. CTREE analysis, in combination with a genetic algorithm approach, has been identified as particularly insightful for illustrating complex interactions between variables. Lower neurocognitive function was associated with factors such as age>52 years, IQ<94/95,<12/13 education years, and more pronounced negative symptoms (score<26). Conclusions These findings emphasize the multifaceted nature of neurocognitive functioning in patients with schizophrenia, with the PANSS-negative score being an important predictor. This gives rise to a role in addressing negative symptoms as a therapeutic objective for enhancing cognitive impairments in these patients. Further research must examine nonlinear relationships among various moderating factors identified in this work, especially the role of D ₂ occupancy.