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 D₂ 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 D₂ 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 D₂ occupancy.