Alegret Montserrat, Blazquez-Folch Josep, Pérez Alba, Ortega Gemma, Espinosa Ana, Muñoz Nathalia, Sanabria Angela, García-Gutiérrez Fernando, Alarcon-Martin Emilio, Rosende-Roca Maitee, Vargas Liliana, Tartari Juan Pablo, Rentz Dorene M, Valero Sergi, Ruiz Agustín, Boada Mercè, Marquié Marta
Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, 08028 Barcelona, Spain.
Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, 28029 Madrid, Spain.
J Clin Med. 2024 Nov 29;13(23):7274. doi: 10.3390/jcm13237274.
: Alzheimer's disease (AD) dementia and mild cognitive impairment (MCI) are currently underdiagnosed in the community, and early detection of cognitive deficits is crucial for timely intervention. FACEmemory, the first completely self-administered online memory test with voice recognition, has been launched as an accessible tool to detect such deficits. This study aims to investigate the neuropsychological associations between FACEmemory subscores and cognitive composites derived from traditional paper-and-pencil neuropsychological tests and to develop an optimal algorithm using FACEmemory data and demographics to discriminate cognitively healthy (CH) individuals from those with MCI. : A total of 669 participants (266 CH, 206 non-amnestic MCI [naMCI], and 197 amnestic MCI [aMCI]) were included. Multiple linear regression analyses were conducted using a cognitive composite as the dependent variable and FACEmemory subscores and demographic data (age, sex, and schooling) as independent variables. Machine learning models were compared to identify an optimal algorithm for distinguishing between CH and MCI (whole MCI, aMCI, and naMCI). : Multiple regression analyses showed associations between FACEmemory scores and the domains of memory (ρ = 0.67), executive functions (ρ = 0.63), visuospatial/visuoperceptual abilities (ρ = 0.55), language (ρ = 0.43), praxis (ρ = 0.52), and attention (ρ = 0.31). An optimal algorithm distinguished between CH and aMCI, achieving a FACEmemory cutoff score of 44.5, with sensitivity and specificity values of 0.81 and 0.72, respectively. : FACEmemory is a promising online tool for identifying early cognitive impairment, particularly aMCI. It may contribute to addressing the underdiagnosis of MCI and dementia in the community and in promoting preventive strategies.
阿尔茨海默病（AD）性痴呆和轻度认知障碍（MCI）目前在社区中未得到充分诊断，认知缺陷的早期检测对于及时干预至关重要。FACEmemory是首个完全自主进行的具有语音识别功能的在线记忆测试，已作为一种可获取的工具推出，用于检测此类缺陷。本研究旨在调查FACEmemory子分数与源自传统纸笔神经心理测试的认知综合指标之间的神经心理学关联，并使用FACEmemory数据和人口统计学信息开发一种最佳算法，以区分认知健康（CH）个体与MCI个体。：共纳入669名参与者（266名CH、206名非遗忘型MCI [naMCI]和197名遗忘型MCI [aMCI]）。使用认知综合指标作为因变量，FACEmemory子分数和人口统计学数据（年龄、性别和受教育程度）作为自变量进行多元线性回归分析。比较机器学习模型以确定区分CH和MCI（整体MCI、aMCI和naMCI）的最佳算法。：多元回归分析显示FACEmemory分数与记忆领域（ρ = 0.67）、执行功能（ρ = 0.63）、视觉空间/视觉感知能力（ρ = 0.55）、语言（ρ = 0.43）、运用能力（ρ = 0.52）和注意力（ρ = 0.31）之间存在关联。一种最佳算法区分了CH和aMCI，FACEmemory截止分数为44.5，敏感性和特异性值分别为0.81和0.72。：FACEmemory是一种用于识别早期认知障碍，尤其是aMCI的有前景的在线工具。它可能有助于解决社区中MCI和痴呆的诊断不足问题，并促进预防策略的实施。
