Consortium PSYCHIATRICUMConsortium PSYCHIATRICUMConsortium PSYCHIATRICUM2712-76722713-2919Eco-Vector1578210.17816/CP15782REVIEWОБЗОРResearch ArticleNetwork analysis in the study of schizophrenia symptoms: a narrative reviewПрименение сетевого анализа в изучении симптомов шизофрении: нарративный обзорhttps://orcid.org/0000-0001-9307-919XLKJ-6132-20248154-1925FilippovIvan A.ФилипповИван Алексеевич
Russian Federation

Postgraduate Student, Department of Psychiatry and Narcology, Medical Institute

filippov20121@gmail.comhttps://orcid.org/0000-0003-4096-62083341-2372PetrovaNataliia N.ПетроваНаталия Николаевна
Russian Federation

MD, Dr. Sci (Med.), Professor, Head of Department of Psychiatry and Addiction

filippov20121@gmail.comSaint Petersburg State UniversityФГБОУ ВО «Санкт-Петербургский государственный университет»11092026290920267328410711202511092026Copyright ©; 2026, Eco-VectorCopyright ©; 2026, Эко-Вектор2026Eco-VectorЭко-Векторhttps://creativecommons.org/licenses/by-nc-nd/4.0https://consortium-psy.com/jour/article/view/15782

BACKGROUND: The network analysis method based on machine learning enables the identification of symptoms and factors associated with the development of schizophrenia and the course of the disease. According to several systematic reviews, positive, negative, affective, and cognitive symptoms, as well as a history of childhood trauma, are central to the schizophrenia symptoms network. However, the role of disorganization, clinical insight, side effects of treatment, and differences between symptoms networks in patients with schizophrenia and healthy individuals have not been fully elucidated.

AIM: The study aimed to summarize the results of a network analysis of schizophrenia symptoms.

METHODS: Publications were searched in the PubMed (MEDLINE) database using the keywords “Network analysis” and “Schizophrenia”. The selection criteria included original studies published between 2019 and 2025 that presented results of a network analysis of schizophrenia symptoms.

RESULTS: After a two-step screening process (based on titles/abstracts and full-text analysis), 62 out of 969 identified studies were included in the review. The results of a network analysis of schizophrenia symptoms confirmed the centrality of negative, affective, and cognitive symptoms, as well as disorganization within the structure of the disease. This result demonstrates a close relationship between patient functioning and most symptoms in the network. Insight and a history of childhood abuse are pivotal components of the schizophrenia symptom network. As psychotic symptoms improve and during the chronic course of the illness, the connections between nodes become weaker. Affective disorders and cognitive impairment act as bridge symptoms linking schizophrenia with other mental disorders.

CONCLUSION: The study has identified common features of the schizophrenia symptom network and its most central nodes. Network analysis can be considered a promising tool for studying schizophrenia; however, further data collection, development of eligibility criteria, and standardization of study variables and methods of their assessment are required.

ВВЕДЕНИЕ: Метод сетевого анализа, основанный на машинном обучении, позволяет идентифицировать симптомы и факторы, определяющие развитие и течение шизофрении. Согласно данным ряда систематических обзоров, в сети симптомов шизофрении большое значение имеют позитивные, негативные, аффективные и когнитивные симптомы, а также опыт детской травмы. Вместе с тем роль дезорганизации, осознания болезни, побочных эффектов терапии и соотношение сетей симптомов у пациентов с шизофренией и здоровых индивидов остаются недостаточно изученными.

ЦЕЛЬ: Обобщить результаты сетевого анализа симптомов шизофрении.

МЕТОДЫ: Поиск публикаций осуществляли в базе данных PubMed (MEDLINE) по ключевым словам «Network analysis» и «Schizophrenia». Критерии отбора: оригинальные исследования, опубликованные в 2019–2025 гг., содержащие результаты сетевого анализа симптомов шизофрении.

РЕЗУЛЬТАТЫ: После двухэтапного скрининга (по названиям/аннотациям и полнотекстовому анализу) из 969 обнаруженных работ в обзор включили 62 публикации. Результаты сетевого анализа симптомов шизофрении подтвердили существенную значимость негативных, аффективных и когнитивных симптомов, а также явлений дезорганизации в структуре заболевания. Данный метод демонстрирует тесную связь функционирования пациентов с большинством симптомов сети. Уровень осознания болезни и детский абьюз в анамнезе выступают важными элементами сети симптомов шизофрении. При купировании психотической симптоматики и длительном течении заболевания связи между узлами ослабевают. Роль симптомов, связывающих шизофрению с другими психическими расстройствами, играют аффективные и когнитивные нарушения.

ЗАКЛЮЧЕНИЕ: Определены общие признаки сети симптомов шизофрении и элементы, имеющие в ней наибольшее значение. Сетевой анализ можно считать перспективным инструментом изучения шизофрении, однако необходимы дальнейшее накопление данных, разработка критериев включения и исключения пациентов, а также унификация исследуемых переменных и методов их оценки.

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