Dissociating memory and executive function impairment through temporal features in a word list verbal learning task

被引:1
|
作者
Dorr, Felix [1 ]
Schaefer, Simona [1 ]
Ohman, Fredrik [2 ]
Linz, Nicklas [1 ]
Bodin, Timothy Hadarsson [2 ]
Skoog, Johan [2 ]
Zettergren, Anna [2 ]
Kern, Silke [2 ]
Skoog, Ingmar [2 ]
Troeger, Johannes [1 ]
机构
[1] Ki Elements, Saarbrucken, Germany
[2] Univ Gothenburg, Inst Neurosci & Physiol, Sahlgrenska Acad, Gothenburg, Sweden
关键词
Amnestic mild cognitive impairment; aMCI; exMCI; Temporal analysis; Speech analysis; RAVLT; MILD COGNITIVE IMPAIRMENT; ALZHEIMERS-DISEASE; PROCESSING SPEED; NORMATIVE DATA; REACTION-TIME; CONVERSION; CRITERIA; AGE;
D O I
10.1016/j.neuropsychologia.2023.108679
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
The Rey Auditory Verbal Learning Test (RAVLT) is an established verbal learning test commonly used to quantify memory impairments due to Alzheimer's Disease (AD) both at a clinical dementia stage or prodromal stage of mild cognitive impairment (MCI). Focal memory impairment-as quantified e.g. by the RAVLT-at an MCI stage is referred to as amnestic MCI (aMCI) and is often regarded as the cognitive phenotype of prodromal AD. However, recent findings suggest that not only learning and memory but also other cognitive domains, especially executive functions (EF) and processing speed (PS), influence verbal learning performance. This research investigates whether additional temporal features extracted from audio recordings from a participant's RAVLT response can better dissociate memory and EF in such tasks and eventually help to better describe MCI subtypes. 675 age-matched participants from the H70 Swedish birth cohort were included in this analysis; 68 participants were classified as MCI (33 aMCI and 35 due to executive impairment). RAVLT performances were recorded and temporal features extracted. Novel temporal features were correlated with established neuropsychological tests measuring EF and PS. Lastly, the downstream diagnostic potential of temporal features was estimated using group differences and a machine learning (ML) classification scenario. Temporal features correlated moderately with measures of EF and PS. Performance of an ML classifier could be improved by adding temporal features to traditional counts. We conclude that RAVLT temporal features are in general related to EF and that they might be capable of dissociating memory and EF in a word list learning task.
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