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| Hauptseite > Publikationsdatenbank > Predicting verbal fluency performance from executive functions tests: A study in healthy subjects > print |
| Hauptseite > Publikationsdatenbank > Predicting verbal fluency performance from executive functions tests: A study in healthy subjects > print |
| 001 | 863674 | ||
| 005 | 20210130002251.0 | ||
| 024 | 7 | _ | |a 2128/22471 |2 Handle |
| 037 | _ | _ | |a FZJ-2019-03680 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Amunts, Julia |0 P:(DE-Juel1)172863 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a INM/ICS retreat 2019 |c Jülich |d 2019-06-25 - 2019-06-26 |w Germany |
| 245 | _ | _ | |a Predicting verbal fluency performance from executive functions tests: A study in healthy subjects |
| 260 | _ | _ | |c 2019 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Verbal fluency (VF) tasks are a common diagnostic tool in neuropsychological assessment for testing executive functions (EFs) and are part of several diagnostic batteries in the clinical context. Interindividual differences like age or sex impact performance of EFs leading to ambiguous results concerning the relationship between EFs and VF. Thus, the question of which subdomains of EFs impact on VF tasks remains inconclusive. The present study examines whether VF can be predicted on the basis of EF test performance and aims to find out which variables have most impact on prediction analysis.We tested 235 monolingual German speaking healthy subjects (94 males, aged 20-55) with 13 EF tests and 3 VF tasks. These VF tests included two simple and one switching task. Each task was performed for two minutes and the total sum of corrects words was calculated. Additionally, saliva samples were collected to analyze for sex hormone levels and stress. Data were adjusted for sex and age by linear regression and analyses were computed on the residuals. For prediction analysis a Relevance Vector Machine (RVM) approach with 500 replications and a 10-fold cross-validation was computed. Prediction performance was assessed by computing the correlation between real and predicted values. Variables that had a non-zero weight in at least 80% of all models are considered to have the most influenced prediction performance.A significant correlation was observed between true and predicted values (r = 0.25; p < 0.001) and 9 EF tests exhibited non-zero weights in more than 80% of the models. Out of these, the Stroop test, WAF-G (divided attention), Raven´s Matrices test, Corsi and Wisconsin Card Sorting test had at least 95% non-zero weights. Stroop contributed 3 features and WAF-G 2 out of the 9 relevant EFs features. With regards to non-EFs features, Cortisol and Estradiol had a high impact on prediction performance (100%).Results of this study indicate that scores obtained from tasks testing inhibition and updating are representative to reflect VF performance. These findings concur with previous studies which infer that updating ability might be involved in VF when keeping track of already spoken words to avoid errors and repetitions, while inhibition might be needed to suppress repeated or irrelevant responses. Additionally, together with other studies, this study elucidates the impact of hormones on EFs but also on VF performance. Previous studies have shown that the menstrual cycle phase impacts accuracy and processing speed in EFs test while the effects of cortisol are still inconclusive. |
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| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/863674/files/AmuntsJ_Poster_INMICS_Retreat_2019.pdf |
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