Michal Hajdúk1, David L Penn2, Philip D Harvey3, Amy E Pinkham4. 1. Department of Psychology, Faculty of Arts, Comenius University in Bratislava, Bratislava, Slovakia; Department of Psychiatry, Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia; Science Park UK, Center for Psychiatric Disorders Research, Bratislava, Slovakia. Electronic address: michal.hajduk@uniba.sk. 2. Department of Psychology, University of North Carolina, Chapel Hill, NC, USA; School of Psychology, Australian Catholic University, Melbourne, Victoria, Australia. 3. Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Florida, USA; Research Service, Miami VA Healthcare System, Florida, USA. 4. Department of Psychology, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, USA.
Abstract
BACKGROUND: Extensive difficulties in reaching functional milestones characterize schizophrenia and related psychotic disorders. These impairments are in part explained by lower social cognitive abilities, cognitive impairment, and current psychopathology. The present study aims to model dynamic associations among social cognition, neurocognition, psychopathology, social skills, functional capacity, and functional outcomes in schizophrenia using network analysis in order to identify those factors that are most central to functioning. METHODS: The sample consisted of 408 patients with schizophrenia spectrum disorders who were drawn from the SCOPE project. Participants completed a complex battery of state-of-the-art measures of social cognition, neurocognition, and functional outcomes. Gaussian Graphical Modeling was used for estimation of the network structure. Accuracy of the network was evaluated using the Bootstrap method. RESULTS: Data supported the importance of functional capacity and social skills, which are prerequisites to real - world outcomes. These variables were among the most central in the network. Social cognition was related to functional capacity, social skills, and real - world functioning. Negative symptoms were connected to functional capacity, social skills, and real - world functioning. CONCLUSIONS: Predictors of functional outcomes are complexly associated with each other. Functional capacity, social - skills, working memory, negative symptoms, mentalizing, and emotion recognition were central nodes that support their importance as potential targets of personalized intervention.
BACKGROUND: Extensive difficulties in reaching functional milestones characterize schizophrenia and related psychotic disorders. These impairments are in part explained by lower social cognitive abilities, cognitive impairment, and current psychopathology. The present study aims to model dynamic associations among social cognition, neurocognition, psychopathology, social skills, functional capacity, and functional outcomes in schizophrenia using network analysis in order to identify those factors that are most central to functioning. METHODS: The sample consisted of 408 patients with schizophrenia spectrum disorders who were drawn from the SCOPE project. Participants completed a complex battery of state-of-the-art measures of social cognition, neurocognition, and functional outcomes. Gaussian Graphical Modeling was used for estimation of the network structure. Accuracy of the network was evaluated using the Bootstrap method. RESULTS: Data supported the importance of functional capacity and social skills, which are prerequisites to real - world outcomes. These variables were among the most central in the network. Social cognition was related to functional capacity, social skills, and real - world functioning. Negative symptoms were connected to functional capacity, social skills, and real - world functioning. CONCLUSIONS: Predictors of functional outcomes are complexly associated with each other. Functional capacity, social - skills, working memory, negative symptoms, mentalizing, and emotion recognition were central nodes that support their importance as potential targets of personalized intervention.
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