基于脑电图的精神疾病生物标记物的进展、挑战和前景:综述。

IF 1 Q3 MEDICINE, GENERAL & INTERNAL
Journal of Yeungnam medical science Pub Date : 2024-10-01 Epub Date: 2024-09-09 DOI:10.12701/jyms.2024.00668
Seokho Yun
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引用次数: 0

摘要

由于缺乏准确诊断和治疗所需的适当生物标志物,精神疾病造成了严重的痛苦和功能障碍,导致社会和经济损失。生物标志物对于诊断、预测、治疗和监测各种疾病至关重要。然而,由于大脑结构复杂和缺乏直接的监测模式,精神病学中缺乏生物标记物。本综述探讨了脑电图(EEG)作为神经生理学工具在确定精神科生物标记物方面的潜力。脑电图无创测量大脑电生理活动,用于诊断神经系统疾病,如抑郁症、双相情感障碍(BD)和精神分裂症,并确定精神疾病生物标志物。尽管进行了广泛的研究,但由于测量和分析方面的限制,基于脑电图的生物标记尚未应用于临床。脑电图研究揭示了抑郁症的频谱和复杂性测量、BD 的脑电波异常以及精神分裂症的功率谱异常。然而,目前临床上还没有将基于脑电图的生物标记用于治疗精神疾病。脑电图的优点包括实时数据采集、无创性、成本效益和高时间分辨率。但空间分辨率低、易受干扰、数据解读复杂等挑战限制了其临床应用。要克服这些限制,必须将脑电图与其他神经成像技术、先进的信号处理和标准化方案相结合。人工智能可增强脑电图分析和生物标记物的发现,通过提供早期诊断、个性化治疗和改进疾病进展监测,有可能改变精神病治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review.

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

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