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Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in
Neil W Bailey1, Noralie Krepel2, Hanneke van Dijk3
1Epworth 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.
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.