Newson Jennifer J, Thiagarajan Tara C
Sapien Labs, Arlington, VA, United States.
Front Hum Neurosci. 2019 Jan 9;12:521. doi: 10.3389/fnhum.2018.00521. eCollection 2018.
A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.
相当一部分脑电图（EEG）文献聚焦于功率谱中历史上预先定义的频段差异，这些频段通常被称为阿尔法、贝塔、伽马、西塔和德尔塔波。在此，我们回顾了184项EEG研究，这些研究报告了包括抑郁症、注意力缺陷多动障碍（ADHD）、自闭症、成瘾、双相情感障碍、焦虑症、恐慌症、创伤后应激障碍（PTSD）、强迫症（OCD）和精神分裂症在内的一系列精神疾病在静息状态（睁眼和闭眼）下的频段差异，以确定不同疾病之间的模式。汇总所有报告结果后，我们证明特定频段内功率变化的特征模式并非任何一种疾病所特有，而是在不同疾病之间存在大量重叠，并且在同一疾病内部也存在变异性。特别是，我们表明，在包括ADHD、精神分裂症和OCD在内的几种疾病类型中，最主要的变化模式是低频（德尔塔和西塔）功率增加，高频（阿尔法、贝塔和伽马）功率降低。然而，相当多的疾病，如PTSD、成瘾和自闭症，在频谱变化的任何方向上都没有主导趋势。我们报告了不同疾病和条件下的一致性和验证分数，表明所有疾病的主要结果在文献中出现的可能性通常仅为替代结果的2.2倍，并且在汇总所有报告该结果的研究时，参与者通常少于250人。此外，结果的大小很少被报告，通常较小，在20%至30%之间，并且与症状严重程度评分的相关性较弱。最后，我们讨论了整个文献中与这种频段分析相关的许多方法学挑战和局限性。这些结果提醒人们谨慎解读仅孤立考虑一种疾病的研究结果，以及该方法在心理健康领域提供有价值见解的整体潜力。
