% 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{Rehme:203262,
      author       = {Rehme, Anne and Volz, L. J. and Feis, D.-L. and
                      Bomilcar-Focke, I. and Liebig, T. and Eickhoff, Simon and
                      Fink, G. R. and Grefkes, C.},
      title        = {{I}dentifying {N}euroimaging {M}arkers of {M}otor
                      {D}isability in {A}cute {S}troke by {M}achine {L}earning
                      {T}echniques},
      journal      = {Cerebral cortex},
      volume       = {25},
      number       = {9},
      issn         = {1460-2199},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2015-05243},
      pages        = {3046 - 3056},
      year         = {2015},
      abstract     = {Conventional mass-univariate analyses have been previously
                      used to test for group differences in neural signals.
                      However, machine learning algorithms represent a
                      multivariate decoding approach that may help to identify
                      neuroimaging patterns associated with functional impairment
                      in “individual” patients. We investigated whether fMRI
                      allows classification of individual motor impairment after
                      stroke using support vector machines (SVMs). Forty acute
                      stroke patients and 20 control subjects underwent
                      resting-state fMRI. Half of the patients showed significant
                      impairment in hand motor function. Resting-state
                      connectivity was computed by means of whole-brain
                      correlations of seed time-courses in ipsilesional primary
                      motor cortex (M1). Lesion location was identified using
                      diffusion-weighted images. These features were used for
                      linear SVM classification of unseen patients with respect to
                      motor impairment. SVM results were compared with
                      conventional mass-univariate analyses. Resting-state
                      connectivity classified patients with hand motor deficits
                      compared with controls and nonimpaired patients with
                      $82.6–87.6\%$ accuracy. Classification was driven by
                      reduced interhemispheric M1 connectivity and enhanced
                      connectivity between ipsilesional M1 and premotor areas. In
                      contrast, lesion location provided only $50\%$ sensitivity
                      to classify impaired patients. Hence, resting-state fMRI
                      reflects behavioral deficits more accurately than structural
                      MRI. In conclusion, multivariate fMRI analyses offer the
                      potential to serve as markers for endophenotypes of
                      functional impairment.},
      cin          = {INM-3 / INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000361464000063},
      pubmed       = {pmid:24836690},
      doi          = {10.1093/cercor/bhu100},
      url          = {https://juser.fz-juelich.de/record/203262},
}