% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Poeppl:893856,
      author       = {Poeppl, Timm B and Schecklmann, Martin and Sakreida, Katrin
                      and Landgrebe, Michael and Langguth, Berthold and Eickhoff,
                      Simon B},
      title        = {{P}rediction of response to repetitive transcranial
                      magnetic stimulation in phantom sounds based on individual
                      brain anatomy},
      journal      = {Brain communications},
      volume       = {3},
      number       = {3},
      issn         = {2632-1297},
      address      = {[Großbritannien]},
      publisher    = {Guarantors of Brain},
      reportid     = {FZJ-2021-02880},
      pages        = {fcab115},
      year         = {2021},
      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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34396100},
      UT           = {WOS:000734327400004},
      doi          = {10.1093/braincomms/fcab115},
      url          = {https://juser.fz-juelich.de/record/893856},
}