Literature DB >> 30177344

Positive and negative symptoms in schizophrenia: A longitudinal analysis using latent variable structural equation modelling.

Giuseppe Carrà1, Cristina Crocamo2, Matthias Angermeyer3, Traolach Brugha4, Mondher Toumi5, Paul Bebbington6.   

Abstract

BACKGROUND: Recent network models of schizophrenia propose it is the consequence of mutual interaction between its symptoms. While cross-sectional associations between negative and positive symptoms are consistent with this idea, they may merely reflect their involvement in the diagnostic process. Longitudinal analyses however may allow the identification of possible causal relationships. The European Schizophrenia Cohort (EuroSC) provides data suitable for this purpose.
METHODS: EuroSC includes 1208 patients randomly sampled from outpatient services in France, Germany and the UK. Initial measures were repeated after 12 and 24 months. Latent variable structural equation modelling was used to investigate the direction of effect between positive and negative symptoms assessed with the Positive and Negative Syndrome Scale, controlling for the effects of depressed mood and antipsychotic medication.
RESULTS: The structural model provided acceptable overall fit [χ2 (953) = 2444.32, P < 0.001; CFI = 0.909; RMSEA = 0.046 (90% CI: 0.043, 0.048); SRMR = 0.052]. Both positive and negative symptoms were persistent, and strongly auto-correlated. There were also persistent cross-sectional associations between positive and negative symptoms. While the path from latent positive to negative symptoms from T1 to T2 approached conventional levels of statistical significance (P = 0.051), that from T2 to T3 did not (P = 0.546). Pathways in the reverse direction were uniformly non-significant.
CONCLUSIONS: There was no evidence that negative symptoms predict later positive symptoms. The prediction of negative symptoms by positive symptoms was ambiguous. We discuss implications for conceptualization of schizophrenic processes.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Diathesis models; Longitudinal studies; Negative symptoms; Network models; Positive symptoms; Schizophrenia

Mesh:

Year:  2018        PMID: 30177344     DOI: 10.1016/j.schres.2018.08.018

Source DB:  PubMed          Journal:  Schizophr Res        ISSN: 0920-9964            Impact factor:   4.939


  6 in total

Review 1.  Cognition and Reward Circuits in Schizophrenia: Synergistic, Not Separate.

Authors:  A J Robison; Katharine N Thakkar; Vaibhav A Diwadkar
Journal:  Biol Psychiatry       Date:  2019-10-03       Impact factor: 13.382

2.  Prospective, observational, single-centre cohort study with an independent control group matched for age and sex aimed at investigating the significance of cholinergic activity in patients with schizophrenia: study protocol of the CLASH-study.

Authors:  Benedikt Schick; Eberhard Barth; Benjamin Mayer; Claire-Louise Weber; Theresa Hagemeyer; Carlos Schönfeldt
Journal:  BMJ Open       Date:  2021-12-20       Impact factor: 2.692

3.  Effects of a virtual reality serious game training program on the cognitive function of people diagnosed with schizophrenia: A randomized controlled trial.

Authors:  Xu Wang; Xiaomin Kou; Xiandong Meng; Jianying Yu
Journal:  Front Psychiatry       Date:  2022-07-15       Impact factor: 5.435

4.  Brain Stimulation and Group Therapy to Improve Gesture and Social Skills in Schizophrenia-The Study Protocol of a Randomized, Sham-Controlled, Three-Arm, Double-Blind Trial.

Authors:  Victoria Chapellier; Anastasia Pavlidou; Daniel R Mueller; Sebastian Walther
Journal:  Front Psychiatry       Date:  2022-07-07       Impact factor: 5.435

5.  Factorial Structure of the Serbian Version of the Clinical Assessment Interview for Negative Symptoms - Evidence for Three Factors of Negative Symptoms.

Authors:  Ivan Ristić; Stefan Jerotić; Mirjana Zebić; Bojana Savić; Vuk Vuković; Manuela Russo; Tatjana Voskresenski; Nikolina Jovanović; Nađa P Marić
Journal:  Front Psychol       Date:  2020-10-26

6.  Multi-dimensional predictions of psychotic symptoms via machine learning.

Authors:  Jeremy A Taylor; Kit M Larsen; Marta I Garrido
Journal:  Hum Brain Mapp       Date:  2020-09-01       Impact factor: 5.038

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.