TY  - JOUR
AU  - Amunts, Julia
AU  - Camilleri, Julia
AU  - Eickhoff, Simon
AU  - Patil, Kaustubh
AU  - Heim, Stefan
AU  - von Polier, Georg
AU  - Weis, Susanne
TI  - Comprehensive verbal fluency features predict executive function performance
JO  - Scientific reports
VL  - 11
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Macmillan Publishers Limited, part of Springer Nature
M1  - FZJ-2021-02020
SP  - 6929
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 33767208
UR  - <Go to ISI:>//WOS:000635702100012
DO  - DOI:10.1038/s41598-021-85981-1
UR  - https://juser.fz-juelich.de/record/892359
ER  -