Executive functions predict verbal fluency scores in healthy participants

Julia Amunts,Simon B Eickhoff,Julia A Camilleri,Susanne Weis,Stefan Heim

doi:10.1038/s41598-020-65525-9

Abstract

While there is a clear link between impairments of executive functions (EFs), i.e. cognitive control mechanisms that facilitate goal-directed behavior, and speech problems, it is so far unclear exactly which of the complex subdomains of EFs most strongly contribute to speech performance, as measured by verbal fluency (VF) tasks. Furthermore, the impact of intra-individual variability is largely unknown. This study on healthy participants (n = 235) shows that the use of a relevance vector machine approach allows for the prediction of VF performance from EF scores. Based on a comprehensive set of EF scores, results identified cognitive flexibility and inhibition as well as processing speed as strongest predictors for VF performance, but also highlighted a modulatory influence of fluctuating hormone levels. These findings demonstrate that speech production performance is strongly linked to specific EF subdomains, but they also suggest that inter-individual differences should be taken into account.

Highlights

The relationship between verbal fluency (VF) and the various subdomains of Executive functions (EFs) has frequently been investigated in both healthy controls[19,22,23,24] and patients[17,25]
Abstract reasoning assessed with the Raven’s Progressive Matrices test (SPM) reveals a positive correlation (r = 0.19; p = 0.003) to VF performance indicating a demand of cognitive flexibility and planning while generating words from a specific category
Similar results were found for the TMT (r = −0.14; p = 0.029) and the number of perseveration errors in the WCST (r = −0.14; p = 0.032) which reflect the involvement of cognitive flexibility and working memory in the VF task

Summary

Introduction

The relationship between VF and the various subdomains of EFs has frequently been investigated in both healthy controls[19,22,23,24] and patients[17,25]. Considering previous literature investigating the relationship of EFs and VF in more detail it is obvious that each work contributes to a better understanding of this relationship but generalizing this knowledge is still difficult These limitations are e.g. due to the small subject size or reduced EF test batteries which does not represent overall EF performance. To contribute to a deeper understanding of the so far inconclusive relationship between EFs and VF, the present study used a machine learning approach to investigate to what extent VF performance can be explained by subdomain-specific EF tests. We hypothesize that VF performance can be explained by a conglomeration of cognitive flexibility, working memory and inhibition test scores, which is further modulated by individual variations of fluctuating hormone levels

Objectives

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Conclusion