SOURCE: Clinical Neurophysiology. 132(2):650-659, 2021 02.
AUTHORS: Bailey NW; Krepel N; van Dijk H; Leuchter AF; Vila-Rodriguez F; Blumberger DM; Downar J; Wilson A; Daskalakis ZJ; Carpenter LL; Corlier J; Arns M; Fitzgerald PB
Abstract
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, etap2 = 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.
