Mähler Roman, Reichenbach Alexandra
Center for Machine Learning, Heilbronn University, Heilbronn, Germany.
Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany.
Front Neurosci. 2025 Jun 17;19:1595221. doi: 10.3389/fnins.2025.1595221. eCollection 2025.
Major Depressive Disorder (MDD) is a prevalent, multi-faceted psychiatric disorder influenced by a plethora of physiological and environmental factors. Neuroimaging biomarkers such as diagnosis support systems based on electroencephalography (EEG) recordings have the potential to substantially improve its diagnostic procedure. Research on these biomarkers, however, provides inconsistent findings regarding the robustness of specific markers. One potential source of these contradictions that is frequently neglected may arise from the variability in study populations.
This study systematically reviews 66 original studies from the last 5 years that investigate resting-state EEG-biomarker for MDD detection or diagnosis. The study populations are compared regarding demographic factors, diagnostic procedures and medication, as well as neuropsychological characteristics. Furthermore, we investigate the impact these factors have on the biomarkers, if they were included in the analysis. Finally, we provide further insights into the impact of diagnostic choices and the heterogeneity of a study population based on exploratory analyses in two publicly available data sets.
We find indeed a large variability in the study populations with respect to all factors included in the review. Furthermore, these factors are often neglected in analyses even though the studies that include them tend to find effects.
In light of the variability in diagnostic procedures and heterogeneity in neuropsychological characteristics of the study populations, we advocate for more differentiated target variables in biomarker research then simply MDD and healthy control. Furthermore, the study populations need to be more extensively described and analyses need to include this information in order to provide comparable findings.
重度抑郁症（MDD）是一种普遍存在的、多方面的精神障碍，受大量生理和环境因素影响。神经影像学生物标志物，如基于脑电图（EEG）记录的诊断支持系统，有可能显著改善其诊断程序。然而，关于这些生物标志物的研究，在特定标志物的稳健性方面提供了不一致的结果。这些矛盾的一个经常被忽视的潜在来源可能是研究人群的变异性。
本研究系统回顾了过去5年中66项关于研究静息态脑电图生物标志物用于MDD检测或诊断的原始研究。对研究人群在人口统计学因素、诊断程序和用药情况以及神经心理学特征方面进行了比较。此外，我们研究了这些因素（如果在分析中包括它们）对生物标志物的影响。最后，我们基于对两个公开可用数据集的探索性分析，进一步深入探讨了诊断选择和研究人群异质性的影响。
我们确实发现，在所审查的所有因素方面，研究人群存在很大的变异性。此外，即使包括这些因素的研究往往发现有影响，但这些因素在分析中常常被忽视。
鉴于诊断程序的变异性和研究人群神经心理学特征的异质性，我们主张在生物标志物研究中采用比单纯的MDD和健康对照更具区分性的目标变量。此外，需要更广泛地描述研究人群，并且分析需要纳入这些信息，以便提供可比的研究结果。
