Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis

Background: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicabl...

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Main Authors: Alfonso Delgado-Álvarez, Jordi A. Matias-Guiu, Cristina Delgado-Alonso, Laura Hernández-Lorenzo, Ana Cortés-Martínez, Lucía Vidorreta, Paloma Montero-Escribano, Vanesa Pytel, Jorge Matias-Guiu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Neurology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2020.629183/full
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spelling doaj-00e5be4d5def4964b138aff439a666ac2021-01-21T10:06:30ZengFrontiers Media S.A.Frontiers in Neurology1664-22952021-01-011110.3389/fneur.2020.629183629183Cognitive Processes Underlying Verbal Fluency in Multiple SclerosisAlfonso Delgado-ÁlvarezJordi A. Matias-GuiuCristina Delgado-AlonsoLaura Hernández-LorenzoAna Cortés-MartínezLucía VidorretaPaloma Montero-EscribanoVanesa PytelJorge Matias-GuiuBackground: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicable, and reliable in MS patients. In this study, we aimed to evaluate the cognitive processes related to VF and to develop machine-learning algorithms to predict those patients with cognitive deficits using only VF-derived scores.Methods: Two hundred participants with MS were enrolled and examined using a comprehensive neuropsychological battery, including semantic and phonemic fluencies. Automatic linear modeling was used to identify the neuropsychological test predictors of VF scores. Furthermore, machine-learning algorithms (support vector machines, random forest) were developed to predict those patients with cognitive deficits using only VF-derived scores.Results: Neuropsychological tests associated with attention-executive functioning, memory, and language were the main predictors of the different fluency scores. However, the importance of memory was greater in semantic fluency and clustering scores, and executive functioning in phonemic fluency and switching. Machine learning algorithms predicted general cognitive impairment and executive dysfunction, with F1-scores over 67–71%.Conclusions: VF was influenced by many other cognitive processes, mainly including attention-executive functioning, episodic memory, and language. Semantic fluency and clustering were more explained by memory function, while phonemic fluency and switching were more related to executive functioning. Our study supports that the multiple cognitive components underlying VF tasks in MS could serve for screening purposes and the detection of executive dysfunction.https://www.frontiersin.org/articles/10.3389/fneur.2020.629183/fullmultiple sclerosiscognitiveneuropsychologyfluencyprocessing speedmachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Alfonso Delgado-Álvarez
Jordi A. Matias-Guiu
Cristina Delgado-Alonso
Laura Hernández-Lorenzo
Ana Cortés-Martínez
Lucía Vidorreta
Paloma Montero-Escribano
Vanesa Pytel
Jorge Matias-Guiu
spellingShingle Alfonso Delgado-Álvarez
Jordi A. Matias-Guiu
Cristina Delgado-Alonso
Laura Hernández-Lorenzo
Ana Cortés-Martínez
Lucía Vidorreta
Paloma Montero-Escribano
Vanesa Pytel
Jorge Matias-Guiu
Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis
Frontiers in Neurology
multiple sclerosis
cognitive
neuropsychology
fluency
processing speed
machine learning
author_facet Alfonso Delgado-Álvarez
Jordi A. Matias-Guiu
Cristina Delgado-Alonso
Laura Hernández-Lorenzo
Ana Cortés-Martínez
Lucía Vidorreta
Paloma Montero-Escribano
Vanesa Pytel
Jorge Matias-Guiu
author_sort Alfonso Delgado-Álvarez
title Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis
title_short Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis
title_full Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis
title_fullStr Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis
title_full_unstemmed Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis
title_sort cognitive processes underlying verbal fluency in multiple sclerosis
publisher Frontiers Media S.A.
series Frontiers in Neurology
issn 1664-2295
publishDate 2021-01-01
description Background: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicable, and reliable in MS patients. In this study, we aimed to evaluate the cognitive processes related to VF and to develop machine-learning algorithms to predict those patients with cognitive deficits using only VF-derived scores.Methods: Two hundred participants with MS were enrolled and examined using a comprehensive neuropsychological battery, including semantic and phonemic fluencies. Automatic linear modeling was used to identify the neuropsychological test predictors of VF scores. Furthermore, machine-learning algorithms (support vector machines, random forest) were developed to predict those patients with cognitive deficits using only VF-derived scores.Results: Neuropsychological tests associated with attention-executive functioning, memory, and language were the main predictors of the different fluency scores. However, the importance of memory was greater in semantic fluency and clustering scores, and executive functioning in phonemic fluency and switching. Machine learning algorithms predicted general cognitive impairment and executive dysfunction, with F1-scores over 67–71%.Conclusions: VF was influenced by many other cognitive processes, mainly including attention-executive functioning, episodic memory, and language. Semantic fluency and clustering were more explained by memory function, while phonemic fluency and switching were more related to executive functioning. Our study supports that the multiple cognitive components underlying VF tasks in MS could serve for screening purposes and the detection of executive dysfunction.
topic multiple sclerosis
cognitive
neuropsychology
fluency
processing speed
machine learning
url https://www.frontiersin.org/articles/10.3389/fneur.2020.629183/full
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