Department of Theoretical & Applied Linguistics, Faculty of Philosophy, School of English, Aristotle University of Thessaloniki, Thessaloniki, Greece.
School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
J Alzheimers Dis. 2024;100(s1):S25-S43. doi: 10.3233/JAD-240496.
The assessment of language deficits can be valuable in the early clinical diagnosis of neurodegenerative disorders, including Alzheimer's disease (AD).
The present study aims to explore whether language markers at the macrostructural level could assist with the placement of an individual across the dementia continuum employing production data from structured narratives.
We administered a Picture Sequence Narrative Discourse Task to 170 speakers of Greek: young healthy controls (yHC), cognitively intact healthy elders (eHC), elder participants with subjective cognitive impairment (SCI), with mild cognitive impairment (MCI), and with AD dementia at the mild/moderate stages. Structural MRIs, medical history, neurological examination, and neuropsychological/cognitive screening determined the status of each speaker to appropriately groupthem.
The data analysis revealed that the Macrostructure Index, Irrelevant Info, and Narration Density markers can track cognitive decline and AD (p < 0.001; Macrostructural Index: eHC versus AD Sensitivity 93.8%, Specificity 74.4%, MCI versus AD Sensitivity 93.8%, Specificity 66.7%; Narration Density: eHC versus AD Sensitivity 90.6%, Specificity 71.8%, MCI versus AD Sensitivity 93.8%, Specificity 66.7%). Moreover, Narrative Complexity was significantly affected for subjects with AD, Irrelevant Info increased in the narrations of speakers with MCI and AD, while Narration Length did not appear to indubitably differentiate between the cognitively intact groups and the clinical ones.
Narrative Macrostructure Indices provide valuable information on the language profile of speakers with(out) intact cognition revealing subtle early signs of cognitive decline and AD suggesting that the inclusion of language-based assessment tools would facilitate the clinical process.
语言缺陷的评估在神经退行性疾病(包括阿尔茨海默病)的早期临床诊断中可能具有重要价值。
本研究旨在探讨宏观结构水平的语言标志物是否可以通过使用结构叙述的生成数据来帮助将个体放置在痴呆症连续体中。
我们对 170 名希腊语使用者进行了图片序列叙事话语任务测试:年轻健康对照组(yHC)、认知健全的健康老年人(eHC)、有主观认知障碍(SCI)的老年人、有轻度认知障碍(MCI)和有轻度/中度阶段 AD 痴呆症的老年人。结构磁共振成像、病史、神经系统检查和神经心理学/认知筛查确定了每位演讲者的状态,以便将他们适当地分组。
数据分析表明,宏观结构指数、不相关信息和叙述密度标志物可以跟踪认知下降和 AD(p<0.001;宏观结构指数:eHC 与 AD 的敏感性为 93.8%,特异性为 74.4%,MCI 与 AD 的敏感性为 93.8%,特异性为 66.7%;叙述密度:eHC 与 AD 的敏感性为 90.6%,特异性为 71.8%,MCI 与 AD 的敏感性为 93.8%,特异性为 66.7%)。此外,叙述复杂性受到 AD 患者的显著影响,不相关信息在 MCI 和 AD 患者的叙述中增加,而叙述长度似乎不能明确地区分认知健全组和临床组。
叙述宏观结构指数为具有(不具有)认知健全的演讲者的语言特征提供了有价值的信息,揭示了认知能力下降和 AD 的早期细微迹象,这表明纳入基于语言的评估工具将有助于临床过程。