Comprehensive verbal fluency features predict executive function performance

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 be...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Julia Amunts, Julia A. Camilleri, Simon B. Eickhoff, Kaustubh R. Patil, Stefan Heim, Georg G. von Polier, Susanne Weis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f2f23f1f061940c7b4a9da597fe7ecb1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f2f23f1f061940c7b4a9da597fe7ecb1
record_format	dspace
spelling	oai:doaj.org-article:f2f23f1f061940c7b4a9da597fe7ecb12021-12-02T13:24:15ZComprehensive verbal fluency features predict executive function performance10.1038/s41598-021-85981-12045-2322https://doaj.org/article/f2f23f1f061940c7b4a9da597fe7ecb12021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85981-1https://doaj.org/toc/2045-2322Abstract 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.Julia AmuntsJulia A. CamilleriSimon B. EickhoffKaustubh R. PatilStefan HeimGeorg G. von PolierSusanne WeisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Julia Amunts Julia A. Camilleri Simon B. Eickhoff Kaustubh R. Patil Stefan Heim Georg G. von Polier Susanne Weis Comprehensive verbal fluency features predict executive function performance
description	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.
format	article
author	Julia Amunts Julia A. Camilleri Simon B. Eickhoff Kaustubh R. Patil Stefan Heim Georg G. von Polier Susanne Weis
author_facet	Julia Amunts Julia A. Camilleri Simon B. Eickhoff Kaustubh R. Patil Stefan Heim Georg G. von Polier Susanne Weis
author_sort	Julia Amunts
title	Comprehensive verbal fluency features predict executive function performance
title_short	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_sort	comprehensive verbal fluency features predict executive function performance
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f2f23f1f061940c7b4a9da597fe7ecb1
work_keys_str_mv	AT juliaamunts comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance AT juliaacamilleri comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance AT simonbeickhoff comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance AT kaustubhrpatil comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance AT stefanheim comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance AT georggvonpolier comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance AT susanneweis comprehensiveverbalfluencyfeaturespredictexecutivefunctionperformance
_version_	1718393127621885952

Comprehensive verbal fluency features predict executive function performance

Ejemplares similares