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@INPROCEEDINGS{Amunts:887932,
      author       = {Amunts, Julia and Camilleri, Julia and Heim, Stefan and
                      Eickhoff, Simon and Weis, Susanne},
      title        = {{M}achine learning predicts executive functions from verbal
                      fluency data: {A} study in healthy participants},
      school       = {HHU Düsseldorf},
      reportid     = {FZJ-2020-04524},
      year         = {2020},
      abstract     = {Introduction: Executive functions (EFs) have been shown to
                      be crucial in performing the semantic verbal fluency (sVF)
                      task which is part of several neuropsychological test
                      batteries. While inhibitory control and working memory are
                      needed to monitor already produced words and prevent
                      repetitions, cognitive flexibility contributes to creative
                      thinking. By studying the role of clustering and switching
                      between words using manual approaches previous research
                      aimed to better understand participants’ search strategies
                      and the link between VF performance and EFs. The present
                      study makes use of a computerized analysis of both semantic
                      information and latency patterns to investigate whether
                      these variables can predict EF performance in healthy
                      individuals.Methods:Healthy monolingual German speaking
                      participants (n=234) completed 13 EF tests covering all
                      subdomains of EFs. Additionally, participants performed two
                      simple and one switching sVF task. The semantic information
                      was extracted using GermaNet [1], an ontological approach of
                      measuring semantic distances. Speech breaks and latency
                      patterns were determined with PRAAT [2]. A Relevance Vector
                      Machine (RVM) [3] approach with 100 replications and a
                      10-fold cross-validation was then employed to predict EF
                      scores from 30 sVF features adjusted for age and sex.
                      Prediction performance was assessed by the correlation
                      between true and predicted values. Features which had a
                      non-zero weight in at least $80\%$ of the models were
                      identified as the most influencing sVF variables.Results:The
                      prediction analysis revealed that sVF features significantly
                      predicted 7 EF tests tapping into all EF subdomains.
                      Specifically, latency patterns, especially those coming from
                      the first quarter of the sVF task, were identified as
                      predictive features. Moreover, the mean semantic relatedness
                      within each sVF task was identified as a meaningful feature
                      to predict EF scores. In particular, in the sVF switching
                      task the mean semantic relatedness was shown to reflect
                      inhibition performance. Results additionally show that
                      category errors could be linked to cognitive flexibility and
                      working memory performance.Discussion:The use of a machine
                      learning approach in the current study enabled an insight
                      into the complex and non-linear relationship between EFs and
                      the sVF task. In line with previous literature identifying
                      semantic distances as a sensitive measurement in
                      Alzheimer´s patients and latencies as a sensitive
                      measurement in ageing, our results reveal a specific link
                      between the switching sVF task and EF performance. They
                      highlight the potential of predicting EF scores from sVF
                      data by considering the combination of both semantic
                      information and latency patterns. Thus, combining
                      computerized speech parameter extraction and prediction
                      analyses carries the potential for an objective and
                      time-efficient alternative to manual evaluations in
                      assessing EFs from VF tasks in scientific and clinical
                      contexts.},
      month         = {Jan},
      date          = {2020-01-26},
      organization  = {European Workshop on Cognitive
                       Neuropsychology, Brixen (Italy), 26 Jan
                       2020 - 31 Jan 2020},
      subtyp        = {After Call},
      cin          = {INM-7 / INM-1},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/887932},
}