A bottom-up model of functional outcome in schizophrenia

Author:

Luo Hongge,Zhao Yanli,Fan Fengmei,Fan Hongzhen,Wang Yunhui,Qu Wei,Wang Zhiren,Tan Yunlong,Zhang Xiujun,Tan Shuping

Abstract

AbstractSchizophrenia results in poor functional outcomes owing to numerous factors. This study provides the first test of a bottom-up causal model of functional outcome in schizophrenia, using neurocognition, vocal emotional cognition, alexithymia, and negative symptoms as predictors of functional outcome. We investigated a cross-sectional sample of 135 individuals with schizophrenia and 78 controls. Using a series of structural equation modelling analyses, a single pathway was generated among scores from the MATRICS Consensus Cognitive Battery (MCCB), vocal emotion recognition test, Toronto Alexithymia Scale (TAS), Brief Negative Symptom Scale, and the Personal and Social Performance Scale. The scores for each dimension of the MCCB in the schizophrenia group were significantly lower than that in the control group. The recognition accuracy for different emotions (anger, disgust, fear, sadness, surprise, and satire, but not calm was significantly lower in the schizophrenia group than in the control group. Moreover, the scores on the three dimensions of TAS were significantly higher in the schizophrenia group than in the control group. On path analysis modelling, the proposed bottom-up causal model showed a strong fit with the data and formed a single pathway, from neurocognition to vocal emotional cognition, to alexithymia, to negative symptoms, and to poor functional outcomes. The study results strongly support the proposed bottom-up causal model of functional outcome in schizophrenia. The model could be used to better understand the causal factors related to the functional outcome, as well as for the development of intervention strategies to improve functional outcomes in schizophrenia.

Funder

the Hebei Provincial Graduate Innovation Funding Project

the Beijing Municipal Administration of Hospitals’ Ascent Plan

the National Natural Science Foundation of China

the Beijing Natural Science Foundation grant

the Beijing Municipal Science

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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