Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders

Publication date: Available online 27 October 2017
Source:Brain Stimulation
Author(s): N.W. Bailey, K.E. Hoy, N.C. Rogasch, R.H. Thomson, S. McQueen, D. Elliot, C.M. Sullivan, B.D. Fulcher, Z.J. Daskalakis, P.B. Fitzgerald
BackgroundRepetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, but only some individuals respond. Predicting response could reduce patient and clinical burden. Neural activity related to working memory (WM) has been related to mood improvements, so may represent a biomarker for response prediction.Primary hypothesesWe expected higher theta and alpha activity in responders compared to non-responders to rTMS.MethodsFifty patients with treatment resistant depression and twenty controls performed a WM task while electroencephalography (EEG) was recorded. Patients underwent 5–8 weeks of rTMS treatment, repeating the EEG at week 1 (W1). Of the 39 participants with valid WM-related EEG data from baseline and W1, 10 were responders. Comparisons between responders and non-responders were made at baseline and W1 for measures of theta (4–8 Hz), upper alpha (10–12.5 Hz), and gamma (30–45 Hz) power, connectivity, and theta-gamma coupling. The control group's measures were compared to the depression group's baseline measures separately.ResultsResponders showed higher levels of WM-related fronto-midline theta power and theta connectivity compared to non-responders at baseline and W1. Responder's fronto-midline theta power and connectivity was similar to controls. Responders also showed an increase in gamma connectivity from baseline to W1, with a concurrent improvement in mood and WM reaction times. An unbiased combination of all measures provided mean sensitivity of 0.90 at predicting responders and specificity of 0.92 in a predictive machine learning algorithm.ConclusionBaseline and W1 fronto-midline theta power and theta connectivity show good potential for predicting response to rTMS treatment for depression.



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