000887931 001__ 887931
000887931 005__ 20210130010709.0
000887931 037__ $$aFZJ-2020-04523
000887931 041__ $$aEnglish
000887931 1001_ $$0P:(DE-Juel1)172863$$aAmunts, Julia$$b0$$eCorresponding author
000887931 1112_ $$aEuropean Workshop on Cognitive Neuropsychology$$cBrixen$$d2020-01-26 - 2020-01-31$$gEWCN$$wItaly
000887931 245__ $$aMachine learning predicts executive functions from verbal fluency data
000887931 260__ $$c2020
000887931 3367_ $$033$$2EndNote$$aConference Paper
000887931 3367_ $$2DataCite$$aOther
000887931 3367_ $$2BibTeX$$aINPROCEEDINGS
000887931 3367_ $$2DRIVER$$aconferenceObject
000887931 3367_ $$2ORCID$$aLECTURE_SPEECH
000887931 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1605626897_4611$$xAfter Call
000887931 502__ $$cHHU Düsseldorf
000887931 520__ $$aIntroduction: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.
000887931 536__ $$0G:(DE-HGF)POF3-572$$a572 - (Dys-)function and Plasticity (POF3-572)$$cPOF3-572$$fPOF III$$x0
000887931 8564_ $$uhttps://juser.fz-juelich.de/record/887931/files/JAmunts_Brixen20.pdf$$yRestricted
000887931 909CO $$ooai:juser.fz-juelich.de:887931$$pVDB
000887931 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172863$$aForschungszentrum Jülich$$b0$$kFZJ
000887931 9131_ $$0G:(DE-HGF)POF3-572$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$v(Dys-)function and Plasticity$$x0
000887931 9141_ $$y2020
000887931 920__ $$lyes
000887931 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000887931 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x1
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000887931 980__ $$aI:(DE-Juel1)INM-1-20090406
000887931 980__ $$aUNRESTRICTED