Brain Oscillations in Bipolar Disorder: Insights from Quantitative EEG Studies

双相情感障碍 脑电图 精神分裂症(面向对象编程) 重性抑郁障碍 心理学 神经科学 心情 精神科 临床心理学 听力学 医学
作者
Amir Reza Bahadori,Erfan Naghavi,Pantea Allami,S Dahaghin,Afshan Davari,Sahar Ansari,Sara Ranji,Mehrdad Sheikhvatan,Sajad Shafiee,Abbas Tafakhori
出处
期刊:Clinical Eeg and Neuroscience [SAGE Publishing]
卷期号:57 (1): 5-16 被引量:1
标识
DOI:10.1177/15500594251360059
摘要

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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