Prediction of response to repetitive transcranial magnetic stimulation in phantom sounds based on individual brain anatomy. Issue 3 (27th May 2021)

• Record Type: Journal Article
• Authors: Poeppl, Timm B; Schecklmann, Martin; Sakreida, Katrin; Landgrebe, Michael; Langguth, Berthold; Eickhoff, Simon B
• Is Part Of: Brain communications. Volume 3:Issue 3(2021)
• Publication Date: 2021-05-27
• Subjects: phantom sounds -- prediction -- machine learning -- magnetic resonance imaging -- tinnitus; 616
• DOI: 10.1093/braincomms/fcab115
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Non-invasive brain stimulation can reduce the severity of tinnitus phantom sounds beyond the 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 sub-sampling 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 the auditory cortex but also revealed a network of non-auditory 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 the precision treatment of tinnitus, involving automatic identification of the appropriate treatment method for the individual patient. Abstract : Poeppl et al. applied machine-learning algorithms to structural MRI for prediction of response to non-invasive brain stimulation in patients with tinnitus phantom sounds. The identified classifier predicted therapy success with an accuracy of 83.5%. The findings may hence pave the way for future investigations into precision treatment of tinnitus. Graphical Abstract: