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Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance.

ABSTRACT: In chronic psychotic patients, word graph analysis shows potential as complementary psychiatric assessment. This analysis relies mostly on connectedness, a structural feature of speech that is anti-correlated with negative symptoms. Here we aimed to verify whether speech disorganization during the first clinical contact, as measured by graph connectedness, can correctly classify negative symptoms and the schizophrenia diagnosis 6 months in advance. Positive and negative syndrome scale scores and memory reports were collected from 21 patients undergoing first clinical contact for recent-onset psychosis, followed for 6 months to establish diagnosis, and compared to 21 well-matched healthy subjects. Each report was represented as a word-trajectory graph. Connectedness was measured by number of edges, number of nodes in the largest connected component and number of nodes in the largest strongly connected component. Similarities to random graphs were estimated. All connectedness attributes were combined into a single Disorganization Index weighted by the correlation with the positive and negative syndrome scale negative subscale, and used for classifications. Random-like connectedness was more prevalent among schizophrenia patients (64?×?5% in Control group, p?=?0.0002). Connectedness from two kinds of memory reports (dream and negative image) explained 88% of negative symptoms variance (p?N?=?60) (85% accuracy). Thus, speech disorganization during the first clinical contact correlates tightly with negative symptoms, and is quite discriminative of the schizophrenia diagnosis.

SUBMITTER: Mota NB

PROVIDER: S-EPMC5441540 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance.

Mota Natália B NB Copelli Mauro M Ribeiro Sidarta S

NPJ schizophrenia 20170413

In chronic psychotic patients, word graph analysis shows potential as complementary psychiatric assessment. This analysis relies mostly on connectedness, a structural feature of speech that is anti-correlated with negative symptoms. Here we aimed to verify whether speech disorganization during the first clinical contact, as measured by graph connectedness, can correctly classify negative symptoms and the schizophrenia diagnosis 6 months in advance. Positive and negative syndrome scale scores and ...[more]

PMID: 28560264

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Project description:ImportanceNegative symptoms are associated with a range of poor clinical outcomes, and currently available treatments generally do not produce a clinically meaningful response. Limited treatment progress may be owing in part to poor clarity regarding latent structure. Prior studies have inferred latent structure using exploratory factor analysis, which has led to the conclusion that there are 2 dimensions reflecting motivation and pleasure (MAP) and diminished expressivity (EXP) factors. However, whether these conclusions are statistically justified remains unclear because exploratory factor analysis does not test latent structure. Confirmatory factor analysis (CFA) is needed to test competing models regarding the latent structure of a construct.ObjectiveTo evaluate the fit of 4 models of the latent structure of negative symptoms in schizophrenia using CFA.Design, setting, and participantsThree cross-sectional studies were conducted on outpatients with schizophrenia who were rated on the 3 most conceptually contemporary measures: Scale for the Assessment of Negative Symptoms (SANS), Brief Negative Symptom Scale (BNSS), and Clinical Assessment Interview for Negative Symptoms (CAINS). Confirmatory factor analysis evaluated the following 4 models: (1) a 1-factor model; (2) a 2-factor model with EXP and MAP factors; (3) a 5-factor model with separate factors for the 5 domains of the National Institute of Mental Health consensus development conference (blunted affect, alogia, anhedonia, avolition, and asociality); and (4) a hierarchical model with 2 second-order factors reflecting EXP and MAP and 5 first-order factors reflecting the 5 consensus domains.Main outcomes and measuresOutcomes included CFA model fit statistics derived from symptom severity scores on the SANS, BNSS, and CAINS.ResultsThe study population included 860 outpatients with schizophrenia (68.0% male; mean [SD] age, 43.0 [11.4] years). Confirmatory factor analysis was conducted on each scale, including 268 patients for the SANS, 192 for the BNSS, and 400 for the CAINS. The 1- and 2-factor models provided poor fit for the SANS, BNSS, and CAINS as indicated by comparative fit indexes (CFIs) and Tucker Lewis indexes (TLIs) less than 0.950, RMSEAs that exceeded the 0.080 threshold, and WRMRs greater than 1.00. The 5-factor and hierarchical models provided excellent fit, with the 5-factor model being more parsimonious. The CFIs and TLIs met the 0.95 threshold and the 1.00 threshold for both factor models with all 3 measures. Interestingly, the RMSEAs for the 5-factor model and the hierarchical model fell under the 0.08 threshold for the BNSS and the CAINS but not the SANS.Conclusions and relevanceThese findings suggest that the recent trend toward conceptualizing the latent structure of negative symptoms as 2 distinct dimensions does not adequately capture the complexity of the construct. The latent structure of negative symptoms is best conceptualized in relation to the 5 consensus domains. Implications for identifying pathophysiological mechanisms and targeted treatments are discussed.

Dataset Information

Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance.

Publications

Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance.

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