Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: A non-replication from the ICON-DB consortium |
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| Affiliation: | 1. Epworth Centre for Innovation in Mental Health, Epworth Healthcare, The Epworth Clinic, Camberwell, Victoria 3004, Australia;2. Monash University, Department of Psychiatry, Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia;3. Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands;4. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands;5. TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Dept. of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA;6. Non-Invasive Neurostimulation Therapies Laboratory, Dpt. Psychiatry, The University of British Columbia, Vancouver, BC, Canada;7. Dept. of Psychiatry, University of Toronto, Toronto, ON, Canada;8. Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada;9. Butler Hospital Mood Disorders Research Program and Neuromodulation Research Facility, Dept. of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA;10. Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Location AMC, Amsterdam Neuroscience, the Netherlands |
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| Abstract: | ObjectiveOur 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.MethodsWe 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).ResultsOur 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).ConclusionsThese results suggest that baseline resting EEG theta connectivity or alpha power are unlikely to be generalisable predictors of response to rTMS treatment for depression.SignificanceThese results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research. |
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| Keywords: | Replication Alpha power Theta connectivity rTMS Depression ICON-DB EEG |
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