Gast ::
| Hauptseite > Publikationsdatenbank > Machine learning predicts executive functions from verbal fluency data > print |
| Hauptseite > Publikationsdatenbank > Machine learning predicts executive functions from verbal fluency data > print |
| 001 | 887931 | ||
| 005 | 20210130010709.0 | ||
| 037 | _ | _ | |a FZJ-2020-04523 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Amunts, Julia |0 P:(DE-Juel1)172863 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a European Workshop on Cognitive Neuropsychology |g EWCN |c Brixen |d 2020-01-26 - 2020-01-31 |w Italy |
| 245 | _ | _ | |a Machine learning predicts executive functions from verbal fluency data |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1605626897_4611 |2 PUB:(DE-HGF) |x After Call |
| 502 | _ | _ | |c HHU Düsseldorf |
| 520 | _ | _ | |a Introduction:Executive functions (EFs) have been shown to be crucial in performing the semantic verbal fluency (sVF) task which is part of several neuropsychological test batteries. While inhibitory control and working memory are needed to monitor already produced words and prevent repetitions, cognitive flexibility contributes to creative thinking. By studying the role of clustering and switching between words using manual approaches previous research aimed to better understand participants’ search strategies and the link between VF performance and EFs. The present study makes use of a computerized analysis of both semantic information and latency patterns to investigate whether these variables can predict EF performance in healthy individuals.Methods:Healthy monolingual German speaking participants (n=234) completed 13 EF tests covering all subdomains of EFs. Additionally, participants performed two simple and one switching sVF task. The semantic information was extracted using GermaNet [1], an ontological approach of measuring semantic distances. Speech breaks and latency patterns were determined with PRAAT [2]. A Relevance Vector Machine (RVM) [3] approach with 100 replications and a 10-fold cross-validation was then employed to predict EF scores from 30 sVF features adjusted for age and sex. Prediction performance was assessed by the correlation between true and predicted values. Features which had a non-zero weight in at least 80% of the models were identified as the most influencing sVF variables.Results:The prediction analysis revealed that sVF features significantly predicted 7 EF tests tapping into all EF subdomains. Specifically, latency patterns, especially those coming from the first quarter of the sVF task, were identified as predictive features. Moreover, the mean semantic relatedness within each sVF task was identified as a meaningful feature to predict EF scores. In particular, in the sVF switching task the mean semantic relatedness was shown to reflect inhibition performance. Results additionally show that category errors could be linked to cognitive flexibility and working memory performance.Discussion:The use of a machine learning approach in the current study enabled an insight into the complex and non-linear relationship between EFs and the sVF task. In line with previous literature identifying semantic distances as a sensitive measurement in Alzheimer´s patients and latencies as a sensitive measurement in ageing, our results reveal a specific link between the switching sVF task and EF performance. They highlight the potential of predicting EF scores from sVF data by considering the combination of both semantic information and latency patterns. Thus, combining computerized speech parameter extraction and prediction analyses carries the potential for an objective and time-efficient alternative to manual evaluations in assessing EFs from VF tasks in scientific and clinical contexts. |
| 536 | _ | _ | |a 572 - (Dys-)function and Plasticity (POF3-572) |0 G:(DE-HGF)POF3-572 |c POF3-572 |f POF III |x 0 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/887931/files/JAmunts_Brixen20.pdf |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:887931 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)172863 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-572 |2 G:(DE-HGF)POF3-500 |v (Dys-)function and Plasticity |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2020 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 1 |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|