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@ARTICLE{Amunts:892359,
      author       = {Amunts, Julia and Camilleri, Julia and Eickhoff, Simon and
                      Patil, Kaustubh and Heim, Stefan and von Polier, Georg and
                      Weis, Susanne},
      title        = {{C}omprehensive verbal fluency features predict executive
                      function performance},
      journal      = {Scientific reports},
      volume       = {11},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2021-02020},
      pages        = {6929},
      year         = {2021},
      abstract     = {Semantic verbal fluency (sVF) tasks are commonly used in
                      clinical diagnostic batteries as well as in a research
                      context. When performing sVF tasks to assess executive
                      functions (EFs) the sum of correctly produced words is the
                      main measure. Although previous research indicates
                      potentially better insights into EF performance by the use
                      of finer grained sVF information, this has not yet been
                      objectively evaluated. To investigate the potential of
                      employing a finer grained sVF feature set to predict EF
                      performance, healthy monolingual German speaking
                      participants (n = 230) were tested with a comprehensive
                      EF test battery and sVF tasks, from which features including
                      sum scores, error types, speech breaks and semantic
                      relatedness were extracted. A machine learning method was
                      applied to predict EF scores from sVF features in previously
                      unseen subjects. To investigate the predictive power of the
                      advanced sVF feature set, we compared it to the commonly
                      used sum score analysis. Results revealed that 8 / 14 EF
                      tests were predicted significantly using the comprehensive
                      sVF feature set, which outperformed sum scores particularly
                      in predicting cognitive flexibility and inhibitory
                      processes. These findings highlight the predictive potential
                      of a comprehensive evaluation of sVF tasks which might be
                      used as diagnostic screening of EFs.},
      cin          = {INM-1 / INM-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-7-20090406},
      pnm          = {525 - Decoding Brain Organization and Dysfunction
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-525},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33767208},
      UT           = {WOS:000635702100012},
      doi          = {10.1038/s41598-021-85981-1},
      url          = {https://juser.fz-juelich.de/record/892359},
}