Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: A non-replication from the ICON-DB consortium

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.