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Network Analysis Indicates That Avolition Is the Most Central Domain for the Successful Treatment of Negative Symptoms: Evidence From the Roluperidone Randomized Clinical Trial.网络分析表明,动力缺乏是成功治疗阴性症状的最核心领域:来自罗匹尼罗随机临床试验的证据。
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The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research.网络方法在精神病理学中的应用:2008-2018 年文献回顾与未来研究议程。
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精神分裂症谱系障碍中的心理病理、认知和运动症状网络。

A Network of Psychopathological, Cognitive, and Motor Symptoms in Schizophrenia Spectrum Disorders.

机构信息

Department of Psychiatry, Faculty of Medicine, University of Lisbon, Lisbon, Portugal.

Department of Psychiatry and Mental Health, North Lisbon University Hospital Centre, Avenida Professor Egas Moniz, Lisbon, Portugal.

出版信息

Schizophr Bull. 2021 Jul 8;47(4):915-926. doi: 10.1093/schbul/sbab002.

DOI:10.1093/schbul/sbab002
PMID:33533401
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8266645/
Abstract

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.

摘要

精神分裂症谱系障碍(SSDs)是一种复杂的综合征,涉及精神病理学、认知和运动症状等核心特征。更好地了解这些症状如何相互影响,可以转化为诊断、预后,最终转化为治疗上的进步。本研究旨在:(1)估计 SSD 中精神病理学、认知和运动症状的网络模型;(2)检测社区并探索网络内变量的连接性和相对重要性;(3)根据缓解状态探索亚样本网络的差异。分析中纳入了来自多中心队列研究的 1007 名患者的样本。我们估计了一个包含 43 个节点的网络,包括阳性和阴性综合征量表、认知评估电池和锥体外系症状临床评分的所有项目。网络分析的特定方法被用来解决研究的目的。总样本的估计网络是密集互联的,并组织成 7 个社区。与洞察力、抽象能力、注意力和怀疑有关的节点是网络社区之间的主要桥梁。与未缓解患者的网络相比,缓解患者亚组的估计网络密度较小,结构也不同。总之,本研究详细描述了一组 SSD 核心临床要素之间的相互关系。这些结果为临床评估和干预提供了潜在的新线索。