Senior Researcher; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia; Department Senior Researcher; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia.
Head of the Laboratory of Molecular Immunology and Virology; National Research Center "Kurchatov Institute", 1 Akademika Kurchatova Square, Moscow, 123182, Russia; Senior Researcher, Laboratory of Clinical Immunology; Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency of Russia, 1A Malaya Pirogovskaya St., Moscow, 119435, Russia.
Sovrem Tekhnologii Med. 2022;14(5):53-75. doi: 10.17691/stm2022.14.5.06. Epub 2022 Sep 29.
Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.
精神分裂症是一种具有重要社会意义的精神障碍，常导致严重的残疾形式。临床精神病学中的诊断、治疗策略选择和康复主要基于行为模式、社会人口数据和其他调查的评估，例如临床观察和神经心理学测试，包括精神科医生对患者的检查、自我报告和问卷调查。在许多方面，这些数据是主观的，因此近年来出现了大量致力于寻找反映患者行为和心理神经模式的人体内部过程的客观特征（指标、生物标志物）的工作。这些生物标志物基于仪器和实验室研究（神经影像学、电生理学、生化学、免疫学、遗传学等）的结果，并成功应用于神经科学领域，以了解神经系统疾病发生和发展的机制。目前，随着新的有效神经影像学、实验室和其他研究方法的出现，以及现代数据分析、机器学习和人工智能方法的发展，大量的科学和临床研究正在进行，旨在寻找具有诊断和预后价值的标志物，这些标志物可用于临床实践，使建立和明确诊断、选择和优化治疗和康复策略、预测疾病过程和结局的过程客观化。
本综述分析了描述精神分裂症诊断与健康专业人员建立的各种精神障碍表现（其亚型、病程变异、严重程度、观察到的症状等）之间的相关性的研究工作，以及在对患者进行仪器和实验室检查时获得的客观测量特征/定量指标（解剖学、功能、免疫学、遗传学等）。这些工作的相当一部分致力于基于结构和功能（静息和认知负荷下）MRI、EEG、示踪和免疫学数据的精神分裂症相关物/生物标志物。发现的相关物/生物标志物反映了特定脑区的解剖障碍、脑区功能活动的损伤、脑白质的特定微观结构以及不同结构束之间的连接水平、大脑不同部位脑电活动的变化在不同 EEG 谱范围，以及先天和适应性免疫的变化。本综述还考虑了当前用于搜索精神分裂症生物标志物的数据分析和机器学习方法，以及它们在构建和解释精神分裂症预测性诊断模型中的应用。
