Andrea Perrottelli, Francesco Flavio Marzocchi, Giorgio Di Lorenzo, Chiara D'Amelio, Noemi Sansone, Luigi Giuliani, Pasquale Pezzella, Edoardo Caporusso, Antonio Melillo, Giulia Maria Giordano, Paola Bucci, Armida Mucci, Silvana Galderisi
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引用次数: 0
Abstract
Introduction: Event-related potentials (ERPs), recorded through electroencephalography (EEG) during sensory and cognitive tasks, have been consistently employed to investigate electrophysiological correlates of psychiatric disorders. However, traditional peak component analysis of ERPs is limited by the a priori selection of time windows and electrodes. Microstate analysis, a data-driven approach based on identifying periods of quasi-stable scalp topographies, has been applied to ERP data, offering a valuable tool for understanding the temporal dynamics of large-scale neural networks. This review aims to provide a comprehensive summary of studies examining event-related microstates in individuals with psychiatric disorders.
Methods: A systematic review of English-language articles indexed in PubMed, Scopus, and Web of Science (WoS) was conducted on May 1, 2024. Studies were included only if they applied microstate analysis to ERP data and analyzed data from at least one group of patients with psychiatric disorders in comparison to healthy controls.
Results: Of the 1,115 records screened, 17 studies were included in the final qualitative synthesis. The majority of these studies (n=8) included patients with schizophrenia, using various tasks focusing mainly on visuospatial processing (n=6) and face processing (n=6). Regarding the microstate methodology, the primary clustering approach employed was the k-means clustering algorithm (n=8), while the cross-validation criterion (n=10) was the most commonly used measure of fit. Sixteen of the 17 studies reported at least one significant difference in microstate features between patients and healthy controls, mainly in the temporal and topographic characteristics of microstates and the sequence of their occurrence.
Conclusions: This review highlights the value of event-related potential microstates analysis in identifying spatiotemporal alterations in brain dynamics associated with psychiatric disorders. However, the limited number of studies and the heterogeneity of experimental paradigms constrain the generalizability of the findings.
在感觉和认知任务中通过脑电图（EEG）记录的事件相关电位（ERPs）一直被用于研究精神疾病的电生理相关性。然而，传统的erp峰成分分析受到时间窗和电极的先验选择的限制。微状态分析是一种基于识别准稳定头皮地形周期的数据驱动方法，已被应用于ERP数据，为理解大规模神经网络的时间动态提供了有价值的工具。这篇综述的目的是提供一个全面的研究总结，检查事件相关的微观状态的个体精神障碍。方法：对2024年5月1日在PubMed、Scopus和Web of Science （WoS）中检索的英文论文进行系统综述。只有将微观状态分析应用于ERP数据，并将至少一组精神障碍患者的数据与健康对照进行分析的研究才被纳入。结果：在筛选的1115份记录中，有17份研究被纳入最终的定性综合。这些研究中的大多数（n=8）包括精神分裂症患者，使用主要集中在视觉空间加工（n=6）和面部加工（n=6）的各种任务。在微观状态方法方面，采用的主要聚类方法是k-means聚类算法（n=8），而交叉验证标准（n=10）是最常用的拟合度量。17项研究中有16项报告了患者与健康对照者在微状态特征上至少有一项显著差异，主要是微状态的时间和地形特征及其发生的顺序。结论：本综述强调了事件相关电位微观状态分析在识别与精神疾病相关的脑动力学时空变化方面的价值。然而，有限的研究数量和实验范式的异质性限制了研究结果的普遍性。系统综述注册：https://www.crd.york.ac.uk/PROSPERO，标识符CRD42024529185。
期刊介绍：
Frontiers in Psychiatry publishes rigorously peer-reviewed research across a wide spectrum of translational, basic and clinical research. Field Chief Editor Stefan Borgwardt at the University of Basel is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
The journal''s mission is to use translational approaches to improve therapeutic options for mental illness and consequently to improve patient treatment outcomes.
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