Journal Article

FZJ-2021-02880

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Prediction of response to repetitive transcranial magnetic stimulation in phantom sounds based on individual brain anatomy

Poeppl, T. B. (Corresponding author) ; Schecklmann, M. ; Sakreida, K. ; Landgrebe, M. ; Langguth, B. ; Eickhoff, S. B.FZJ*

2021
Guarantors of Brain [Großbritannien]

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Please use a persistent id in citations: http://hdl.handle.net/2128/28419  doi:10.1093/braincomms/fcab115

Abstract: Noninvasive brain stimulation can reduce severity of tinnitus phantom sounds beyond time of stimulation by inducing regional neuroplastic changes. However, there are no good clinical predictors for treatment outcome. We used machine learning to investigate whether brain anatomy can predict therapeutic outcome. Sixty-one chronic tinnitus patients received repetitive transcranial magnetic stimulation of left dorsolateral prefrontal and temporal cortex. Before repetitive transcranial magnetic stimulation, a structural magnetic resonance image was obtained from all patients. To predict individual treatment response in new subjects, we employed a support-vector machine ensemble for individual out-of-sample prediction. In the cross-validation, the support-vector machine ensemble based on stratified subsampling and feature selection yielded an area under the curve of 0.87 for prediction of therapy success in new, previously unseen subjects. This corresponded to a balanced accuracy of 83.5%, sensitivity of 77.2%, and specificity of 87.2%. Investigating the most selected features showed the involvement of auditory cortex but also revealed a network of nonauditory brain areas. These findings suggest that idiosyncratic brain patterns accurately predict individual responses to repetitive transcranial magnetic stimulation treatment for tinnitus. Our findings may hence pave the way for future investigations into precision treatment of tinnitus, involving automatic identification of the appropriate treatment method for the individual patient.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2021

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