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Comprehensive verbal fluency features predict executive function performance

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 insi...

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Autores principales: Amunts, Julia, Camilleri, Julia A., Eickhoff, Simon B., Patil, Kaustubh R., Heim, Stefan, von Polier, Georg G., Weis, Susanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994566/
https://www.ncbi.nlm.nih.gov/pubmed/33767208
http://dx.doi.org/10.1038/s41598-021-85981-1
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author Amunts, Julia
Camilleri, Julia A.
Eickhoff, Simon B.
Patil, Kaustubh R.
Heim, Stefan
von Polier, Georg G.
Weis, Susanne
author_facet Amunts, Julia
Camilleri, Julia A.
Eickhoff, Simon B.
Patil, Kaustubh R.
Heim, Stefan
von Polier, Georg G.
Weis, Susanne
author_sort Amunts, Julia
collection PubMed
description 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.
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spelling pubmed-79945662021-03-29 Comprehensive verbal fluency features predict executive function performance Amunts, Julia Camilleri, Julia A. Eickhoff, Simon B. Patil, Kaustubh R. Heim, Stefan von Polier, Georg G. Weis, Susanne Sci Rep Article 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. Nature Publishing Group UK 2021-03-25 /pmc/articles/PMC7994566/ /pubmed/33767208 http://dx.doi.org/10.1038/s41598-021-85981-1 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Amunts, Julia
Camilleri, Julia A.
Eickhoff, Simon B.
Patil, Kaustubh R.
Heim, Stefan
von Polier, Georg G.
Weis, Susanne
Comprehensive verbal fluency features predict executive function performance
title Comprehensive verbal fluency features predict executive function performance
title_full Comprehensive verbal fluency features predict executive function performance
title_fullStr Comprehensive verbal fluency features predict executive function performance
title_full_unstemmed Comprehensive verbal fluency features predict executive function performance
title_short Comprehensive verbal fluency features predict executive function performance
title_sort comprehensive verbal fluency features predict executive function performance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994566/
https://www.ncbi.nlm.nih.gov/pubmed/33767208
http://dx.doi.org/10.1038/s41598-021-85981-1
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