双相情感障碍的脑振荡:定量脑电图研究的见解。

IF 1.7
Amir Reza Bahadori, Erfan Naghavi, Pantea Allami, Saba Dahaghin, Afshan Davari, Sahar Ansari, Sara Ranji, Mehrdad Sheikhvatan, Sajad Shafiee, Abbas Tafakhori
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引用次数: 0

摘要

定量脑电图(QEEG)是一种神经生理学工具,可以分析不同频段的大脑振荡,为双相情感障碍(BD)等精神疾病提供见解。这种以情绪波动为特征的疾病给诊断和治疗带来了挑战,突出了对可靠生物标志物的需求。目的评价双相障碍患者的QEEG变化,探讨其诊断和治疗潜力,并将其与重度抑郁障碍(MDD)和精神分裂症区分开来。方法按照PRISMA 2020指南,对PubMed、Scopus、Web of Science和Embase进行全面检索,截止到2024年10月30日,没有时间限制。纳入使用QEEG评估BD患者的研究。主要结果集中在频带改变、治疗反应和诊断分化。结果纳入20项研究,475例BD患者。在双相障碍中,伽马和β活动持续增加。然而,α和θ波段变化的方向性不同,根据大脑区域和情绪状态观察到差异。δ波段的改变在BD i中更为突出。治疗反应显示γ、θ和α波段的减弱。QEEG还根据频带特征将双相障碍与重度抑郁症和精神分裂症区分开来。结论qeeg作为双相障碍的诊断和治疗工具具有重要的前景,尽管方法上存在差异,但与机器学习的结合可以提高诊断精度并指导个性化治疗。需要进一步的研究来标准化方法和验证结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Oscillations in Bipolar Disorder: Insights from Quantitative EEG Studies.

IntroductionQuantitative electroencephalography (QEEG) is a neurophysiological tool that analyzes brain oscillations across frequency bands, providing insights into psychiatric conditions like bipolar disorder (BD). This disorder, marked by mood fluctuations, poses diagnostic and treatment challenges, highlighting the need for reliable biomarkers.ObjectiveThis systematic review aims to evaluate QEEG changes in BD patients, investigate its diagnostic and therapeutic potential, and differentiate BD from major depressive disorder (MDD) and schizophrenia.MethodsFollowing PRISMA 2020 guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and Embase was conducted till 30th of October 2024 without timeline restrictions. Studies involving BD patients assessed using QEEG were included. Key outcomes focused on frequency band alterations, treatment responses, and diagnostic differentiation.ResultsThe review included 20 studies with 475 BD patients. Increased gamma and beta activity were consistently observed in BD. However, the directionality of alpha and theta band changes varied, with differences observed depending on brain region and mood state. Delta band alterations were more prominent in BD I. Treatment responses showed reduced power in gamma, theta, and alpha bands. QEEG also distinguished BD from MDD and schizophrenia based on frequency band characteristics.ConclusionQEEG demonstrates significant promise as a diagnostic and therapeutic tool for BD. Despite methodological variability, its integration with machine learning could enhance diagnostic precision and guide personalized treatments. Further research is needed to standardize methodologies and validate findings.

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