Background: The utility of an endophenotype depends on its ability to reduce complex disorders into stable, genetically linked phenotypes. P50 and P300 event-related potential (ERP) measures are endophenotype candidates for schizophrenia; however, their abnormalities are broadly observed across neuropsychiatric disorders. This study examined the diagnostic efficiency of P50 and P300 in schizophrenia as compared with healthy and bipolar disorder samples. Supplemental ERP measures and a multivariate classification approach were evaluated as methods to improve specificity.
Methods: Diagnostic classification was first modeled in schizophrenia (SZ = 50) and healthy normal (HN = 50) samples using hierarchical logistic regression with predictors blocked by 4 levels of analysis: (1) P50 suppression, P300 amplitude, and P300 latency; (2) N100 amplitude; (3) evoked spectral power; and (4) P50 and P300 hemispheric asymmetry. The optimal model was cross-validated in a holdout sample (SZ = 34, HN = 31) and tested against a bipolar (BP = 50) sample.
Results: P50 and P300 endophenotypes classified SZ from HN with 71% accuracy (sensitivity = .70, specificity = .72) but did not differentiate SZ from BP above chance level. N100 and spectral power measures improved classification accuracy of SZ vs HN to 79% (sensitivity = .78, specificity = .80) and SZ vs BP to 72% (sensitivity = .74, specificity = .70). Cross validation analyses supported the stability of these models.
Conclusions: Although traditional P50 and P300 measures failed to differentiate schizophrenia from bipolar participants, N100 and evoked spectral power measures added unique variance to classification models and improved accuracy to nearly the same level achieved in comparison of schizophrenia to healthy individuals.
Keywords: N100; P300; P50; event-related potential; gamma frequency.