Neurophysiological predictors of non-response to rTMS in depression

Background: The application of rTMS in Depression has been very well investigated over the last few years. However, little is known about predictors of non-response associated with rTMS treatment. Objective: This study examined neurophysiological parameters (EEG and ERP) in 90 depressed patients tre...

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Bibliographic Details
Main Authors: Martijn Arns, Wilhelmus H. Drinkenburg, Paul B. Fitzgerald, J. Leon Kenemans
Format: Article
Language:English
Published: Elsevier 2012-10-01
Series:Brain Stimulation
Subjects:
EEG
ERP
Online Access:http://www.sciencedirect.com/science/article/pii/S1935861X11001720
Description
Summary:Background: The application of rTMS in Depression has been very well investigated over the last few years. However, little is known about predictors of non-response associated with rTMS treatment. Objective: This study examined neurophysiological parameters (EEG and ERP) in 90 depressed patients treated with rTMS and psychotherapy and sought to identify predictors of non-response. Methods: This study is a multi-site open-label study assessing pre-treatment EEG and ERP measures associated with non-response to rTMS treatment. Results: Non-responders were characterized by 1) Increased fronto-central theta EEG power, 2) a slower anterior individual alpha peak frequency, 3) a larger P300 amplitude, and 4) decreased pre-frontal delta and beta cordance. A discriminant analysis yielded a significant model, and subsequent ROC curve demonstrated an area under the curve of 0.814. Conclusions: Several EEG variables demonstrated clear differences between R and NR such as the anterior iAPF, fronto-central Theta and pre-frontal cordance in the Delta and Beta band (representative of increased relative pre-frontal perfusion). The increased P300 amplitude as a predictor for non-response requires further study, since this was the opposite as hypothesized and there were no correlations of this measure with clinical improvement for the whole sample. Combining these biomarkers in a discriminant analysis resulted in a reliable identification of non-responders with low false positive rates. Future studies should prospectively replicate these findings and also further investigate appropriate treatments for the sub-groups of non-responders identified in this study, given that most of these biomarkers have also been found in antidepressant medication studies.
ISSN:1935-861X