A Network of Psychopathological, Cognitive, and Motor Symptoms in Schizophrenia Spectrum Disorders
Author(s) -
Bernardo Melo Moura,
Geeske van Rooijen,
Frederike Schirmbeck,
Johanna T. W. Wigman,
Thérèse van Amelsvoort,
Agna A. BartelsVelthuis,
Richard Bruggeman,
Wiepke Cahn,
Lieuwe de Haan,
René S. Kahn,
Claudia J.P. Simons,
Luís Madeira,
Peter van Harten,
Jim van Os,
P. Roberto Bakker,
Machteld Marcelis
Publication year - 2021
Publication title -
schizophrenia bulletin
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.823
H-Index - 190
eISSN - 1745-1707
pISSN - 0586-7614
DOI - 10.1093/schbul/sbab002
Subject(s) - psychopathology , cognition , positive and negative syndrome scale , schizophrenia (object oriented programming) , psychology , extrapyramidal symptoms , cohort , clinical psychology , psychiatry , medicine , psychosis , antipsychotic
Schizophrenia spectrum disorders (SSDs) are complex syndromes involving psychopathological, cognitive, and also motor symptoms as core features. A better understanding of how these symptoms mutually impact each other could translate into diagnostic, prognostic, and, eventually, treatment advancements. The present study aimed to: (1) estimate a network model of psychopathological, cognitive, and motor symptoms in SSD; (2) detect communities and explore the connectivity and relative importance of variables within the network; and (3) explore differences in subsample networks according to remission status. A sample of 1007 patients from a multisite cohort study was included in the analysis. We estimated a network of 43 nodes, including all the items from the Positive and Negative Syndrome Scale, a cognitive assessment battery and clinical ratings of extrapyramidal symptoms. Methodologies specific to network analysis were employed to address the study’s aims. The estimated network for the total sample was densely interconnected and organized into 7 communities. Nodes related to insight, abstraction capacity, attention, and suspiciousness were the main bridges between network communities. The estimated network for the subgroup of patients in remission showed a sparser density and a different structure compared to the network of nonremitted patients. In conclusion, the present study conveys a detailed characterization of the interrelations between a set of core clinical elements of SSD. These results provide potential novel clues for clinical assessment and intervention.
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