Schizophrenia Phenomenology Revisited: Positive and Negative Symptoms Are Strongly Related Reflective Manifestations of an Underlying Single Trait Indicating Overall Severity of Schizophrenia.

Schizophrenia comprises various symptom domains the two most important being positive and negative symptoms. Nevertheless, using (un)supervised machine learning techniques it was shown that a) negative symptoms are significantly interrelated with PHEM (psychosis, hostility, excitation, and mannerism) symptoms, formal thought disorders (FTD) and psychomotor retardation (PMR); and b) stable phase schizophrenia comprises two distinct classes, namely Major Neuro-Cognitive Psychosis (MNP, largely overlapping with deficit schizophrenia) and Simple NP (SNP). In this study, we recruited 120 MNP patients and 54 healthy subjects and measured the above-mentioned symptom domains. In MNP, there were significant associations between negative and PHEM symptoms, FTD and PMR. A single latent trait, which is essentially unidimensional, underlies these key domains of schizophrenia and additionally shows excellent internal consistency reliability, convergent validity, and predictive relevance. Confirmatory Tedrad Analysis indicates that this latent vector fits a reflective model. Soft Independent Modeling of Class Analogy (SIMCA) shows that MNP (diagnosis based on negative symptoms) is better modeled with PHEM symptoms, FTD and PMR than with negative symptoms. In conclusion, in MNP, a restricted sample of the schizophrenia population, negative and PHEM symptoms, FTD and PMR belong to one underlying latent vector reflecting general psychopathology and, therefore, may be used as an overall severity of schizophrenia (OSOS) index. The bi-dimensional concept of positive and negative symptoms and type I and II schizophrenia is revised.