Bailey Neil W, Krepel Noralie, van Dijk Hanneke, Leuchter Andrew F, Vila-Rodriguez Fidel, Blumberger Daniel M, Downar Jonathan, Wilson Andrew, Daskalakis Zafiris J, Carpenter Linda L, Corlier Juliana, Arns Martijn, Fitzgerald Paul B
Epworth Centre for Innovation in Mental Health, Epworth Healthcare, The Epworth Clinic, Camberwell, Victoria 3004, Australia; Monash University, Department of Psychiatry, Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia.
Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
Clin Neurophysiol. 2021 Feb;132(2):650-659. doi: 10.1016/j.clinph.2020.10.018. Epub 2020 Nov 10.
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.
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).
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, η = 0.005).
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.
These results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research.
我们之前的研究表明，使用静息脑电图（EEG）以及治疗开始时和治疗开始后一周的临床数据，通过机器学习算法区分抑郁症患者对重复经颅磁刺激（rTMS）的反应者和无反应者具有较高的预测准确性。特别是，反应者和无反应者之间的θ波（4 - 8赫兹）连通性和α波功率（8 - 13赫兹）存在显著差异。在临床实践中应用潜在预测指标之前，独立复制是必要步骤。本研究试图在一个独立数据集中复制这些结果。
我们将来自接受rTMS治疗抑郁症的参与者独立样本（N = 193，128名反应者）（Krepel等人，2018年）的基线静息EEG数据进行与我们之前研究（Bailey等人，2019年）相同的组间比较。
我们之前的结果未被复制，反应者和无反应者在θ波连通性（p = 0.250，科恩d值 = 0.1786）和α波功率（p = 0.357，η = 0.005）方面均无差异。
这些结果表明，基线静息EEG的θ波连通性或α波功率不太可能是抑郁症rTMS治疗反应的通用预测指标。
这些结果突出了在反应预测研究中独立复制、数据共享和使用大型数据集的重要性。
