Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden.
Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden.
复制标题
检查认知状态和淀粉样蛋白β负担的助记符歧视任务的诊断价值。
DOI：
10.1016/j.neuropsychologia.2023.108727
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发表时间：
2023-12-15
期刊：
影响因子：
2.6
通讯作者：
Yassa, Michael A.
中科院分区：
文献类型：
--
作者：
Kim, Soyun;Adams, Jenna N.;Chappel-Farley, Miranda G.;Keator, David;Janecek, John;Taylor, Lisa;Mikhail, Abanoub;Hollearn, Martina;Mcmillan, Liv;Rapp, Paul;Yassa, Michael A.
关键词：
阿尔茨海默病（AD）是最常见的痴呆类型，其特征在于早期记忆障碍和日常功能逐渐恶化。AD相关的病理学，如β淀粉样蛋白（Aβ）斑块，在临床症状发作前多年开始积累。通过相关病理学预测AD的风险至关重要，因为临床前阶段可以作为治疗时间窗口，允许早期管理疾病并降低健康和经济成本。然而，目前用于检测AD病理学的方法通常是昂贵的和侵入性的，限制了对临床环境的广泛和容易的访问。一个非侵入性的，具有成本效益的平台，如计算机化的认知测试，可能有助于尽早识别高危人群。在这项研究中，我们检查了情景记忆任务，记忆辨别任务（MDT）的诊断价值，用于预测认知障碍或Aβ负荷的风险。我们构建了一个随机森林分类算法，利用MDT性能指标和各种神经心理学测试分数作为输入特征，并使用曲线下面积（AUC）评估模型性能。基于MDT性能指标的模型获得了分类结果，认知状态的AUC为0.83，Aβ状态的AUC为0.64。我们的研究结果表明，记忆辨别功能可能是进展为前驱AD或Aβ负荷风险增加的有用预测因子，这可能是一种具有成本效益的非侵入性认知测试解决方案，可用于广泛评估AD病理和认知风险。
Alzheimer’s disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.
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