Schizophrenia phenomenology revisited: positive and negative symptoms
are strongly related reflective manifestations of an underlying single
trait indicating overall severity of schizophrenia
Abstract
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.