Leveraging Stacked Classifiers for Multi-task Executive Function in Schizophrenia Yields Diagnostic and Prognostic Insights

BACKGROUND: Executive function (EF) impairments are often seen in mental disorders, particularly schizophrenia (SZ), where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of SZ remains opaque. STUDY DESIGN: We used classification models with a stacking approach on systematically measured EFs using 6 tasks to discriminate 195 patients with SZ from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with 4 symptom dimensions. STUDY RESULTS: The models highlighted the importance of inhibitory control (interference and response inhibitions) or working memory (WM) in accurately identifying individuals with SZ (area under the curve [AUC] = 0.87) or those in remission (AUC = 0.81). Patients who are correctly classified, in the association with the contribution of interference inhibition function to our diagnostic classifier, present more severe baseline negative symptoms compared to those who are more likely to be misclassified. Also, linked to the function of WM updating, patients who are successfully classified as remitted display milder cognitive symptoms at follow-up. Remitted patients do not differ significantly from non-remitted cases in baseline EF assessments or overall symptom severity. CONCLUSIONS: Our work indicates that impairments in specific EF dimensions in SZ are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be promising in the clinical evaluation of this disorder.