  Background: The precise quantification of negative symptoms is necessary to
improve differential diagnosis and prognosis prediction in Schizophrenia. In
chronic psychotic patients, the representation of verbal reports as word graphs
provides automated sorting of schizophrenia, bipolar disorder and control
groups based on the degree of speech connectedness. Here we aim to use machine
learning to verify whether speech connectedness during first clinical contact
can predict negative symptoms and Schizophrenia diagnosis six months later.
Methods: PANSS scores and memory reports were collected from 21 patients
undergoing first clinical contact for recent-onset psychosis and followed for 6
months to establish DSM-IV diagnosis, and 21 healthy controls. Each report was
represented as a graph in which words corresponded to nodes, and node temporal
succession corresponded to edges. Three connectedness attributes were extracted
from each graph, z-scores to random graph distributions were measured,
correlated with the PANSS negative subscale, combined into a single
Fragmentation Index, and used for predictions. Findings: Random-like speech was
prevalent among Schizophrenia patients (64% x 5% in Control group, p=0.0002).
Connectedness explained 92% of the PANSS negative subscale variance (p=0.0001).
The Fragmentation Index classified low versus high scores of PANSS negative
subscale with 93% accuracy (AUC=1), predicted Schizophrenia diagnosis with 89%
accuracy (AUC=0.89), and was validated in an independent cohort of chronic
psychotic patients. Interpretation: The structural randomness of speech graph
connectedness is increased in Schizophrenia. It provides a quantitative
measurement of word salad as a Fragmentation Index that tightly correlates with
negative symptoms and predicts Schizophrenia diagnosis during first clinical
contact of recent-onset psychosis.
