Kaiser Anna, Aggensteiner Pascal-M, Holtmann Martin, Fallgatter Andreas, Romanos Marcel, Abenova Karina, Alm Barbara, Becker Katja, Döpfner Manfred, Ethofer Thomas, Freitag Christine M, Geissler Julia, Hebebrand Johannes, Huss Michael, Jans Thomas, Jendreizik Lea Teresa, Ketter Johanna, Legenbauer Tanja, Philipsen Alexandra, Poustka Luise, Renner Tobias, Retz Wolfgang, Rösler Michael, Thome Johannes, Uebel-von Sandersleben Henrik, von Wirth Elena, Zinnow Toivo, Hohmann Sarah, Millenet Sabina, Holz Nathalie E, Banaschewski Tobias, Brandeis Daniel
Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159 Mannheim, Germany.
LWL-University Hospital for Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics, Ruhr University Bochum, 59071 Hamm, Germany.
Brain Sci. 2021 Feb 10;11(2):214. doi: 10.3390/brainsci11020214.
Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (n = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.
脑电图（EEG）是一种广泛应用的评估大脑功能改变及正常发育的方法。然而，关于EEG数据质量、其相关因素及后果的系统性研究却很匮乏。为填补这一研究空白，本研究聚焦于标准EEG预处理后无伪迹片段的百分比作为数据质量指标。我们分析了与参与者相关的因素和方法学影响，并通过复制具有里程碑意义的EEG效应来验证其有效性。此外，还探讨了数据质量对频谱功率分析的影响，且这种影响超出了与参与者相关的特征。对来自一个多中心注意力缺陷多动障碍（ADHD）队列（年龄范围6至45岁）以及一个非ADHD学龄对照组的EEG数据进行了分析（n = 305）。收集了睁眼和闭眼静息状态数据，以及在提示连续性能任务（CPT）期间的任务相关数据。预处理后，对数据拟合了一般线性模型和逐步回归模型。我们发现EEG数据质量与人口统计学特征密切相关，但与方法学因素无关。我们能够复制EEG文献中报道的成熟、任务和ADHD效应，从而建立与EEG标志性效应的联系。此外，我们还表明，除了成熟和症状严重程度的影响外，较差的数据质量会显著增加频谱功率。综上所述，当前结果表明，通过精心设计和系统的质量控制，开展贯穿整个生命周期、表征神经发育障碍神经生理机制的大规模多中心试验是可行的。然而，结果受限于所报道的局限性。未来的工作将阐明其预测价值。
