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

Neil W Bailey*, Noralie Krepel, Hanneke van Dijk, Andrew F Leuchter, Fidel Vila-Rodriguez, Daniel M Blumberger, Jonathan Downar, Andrew Wilson, Zafiris J Daskalakis, Linda L Carpenter, Juliana Corlier, Martijn Arns, Paul B Fitzgerald

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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, 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.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalClinical Neurophysiology
DOIs
Publication statusE-pub ahead of print - 10 Nov 2020

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