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Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: A non-replication from the ICON-DB consortium
Clinical Neurophysiology ( IF 3.7 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.clinph.2020.10.018
Neil W Bailey 1 , Noralie Krepel 2 , Hanneke van Dijk 3 , Andrew F Leuchter 4 , Fidel Vila-Rodriguez 5 , Daniel M Blumberger 6 , Jonathan Downar 7 , Andrew Wilson 4 , Zafiris J Daskalakis 6 , Linda L Carpenter 8 , Juliana Corlier 4 , Martijn Arns 9 , Paul B Fitzgerald 1
Affiliation  

OBJECTIVE Our previous research showed high predictive accuracy at differentiating responders from non-responders to repetitive transcranial magnetic stimulation (rTMS) for depression using resting electroencephalography (EEG) and clinical data from baseline and one-week following treatment onset using a machine learning algorithm. In particular, theta (4-8 Hz) connectivity and alpha power (8-13 Hz) significantly differed between responders and non-responders. Independent replication is a necessary step before the application of potential predictors in clinical practice. This study attempted to replicate the results in an independent dataset. METHODS We submitted baseline resting EEG data from an independent sample of participants who underwent rTMS treatment for depression (N = 193, 128 responders) (Krepel et al., 2018) to the same between group comparisons as our previous research (Bailey et al., 2019). RESULTS Our previous results were not replicated, with no difference between responders and non-responders in theta connectivity (p = 0.250, Cohen's d = 0.1786) nor alpha power (p = 0.357, ηp2 = 0.005). CONCLUSIONS These results suggest that baseline resting EEG theta connectivity or alpha power are unlikely to be generalisable predictors of response to rTMS treatment for depression. SIGNIFICANCE These results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research.

中文翻译:

静息 EEG theta 连接和 alpha 功率预测抑郁症中重复的经颅磁刺激反应:来自 ICON-DB 联盟的非复制

目标我们之前的研究表明,使用机器学习算法使用静息脑电图 (EEG) 和基线和治疗开始后一周的临床数据,在区分重复经颅磁刺激 (rTMS) 抑郁症的反应者和无反应者方面具有很高的预测准确性。特别是,响应者和非响应者之间的 theta (4-8 Hz) 连接性和 alpha 功率 (8-13 Hz) 显着不同。独立复制是在临床实践中应用潜在预测因子之前的必要步骤。本研究试图在独立数据集中复制结果。方法我们提交了来自接受 rTMS 治疗抑郁症的参与者的独立样本的基线静息 EEG 数据(N = 193,128 名响应者)(Krepel 等人,2018 年)与我们之前的研究(Bailey 等人,2019 年)的组间比较相同。结果 我们之前的结果没有被复制,响应者和非响应者在 theta 连接性(p = 0.250,Cohen's d = 0.1786)和 alpha 功率(p = 0.357,ηp2 = 0.005)方面没有差异。结论 这些结果表明,基线静息 EEG theta 连通性或 alpha 功率不太可能是对 rTMS 治疗抑郁症反应的普遍预测因子。意义这些结果突出了独立复制、数据共享和使用大型数据集在响应研究预测中的重要性。1786)也不是阿尔法幂(p = 0.357,ηp2 = 0.005)。结论 这些结果表明,基线静息 EEG theta 连通性或 alpha 功率不太可能是对 rTMS 治疗抑郁症反应的普遍预测因子。意义这些结果突出了独立复制、数据共享和使用大型数据集在响应研究预测中的重要性。1786)也不是阿尔法幂(p = 0.357,ηp2 = 0.005)。结论 这些结果表明,基线静息 EEG theta 连通性或 alpha 功率不太可能是对 rTMS 治疗抑郁症反应的普遍预测因子。意义这些结果突出了独立复制、数据共享和使用大型数据集在响应研究预测中的重要性。
更新日期:2021-02-01
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