Toniolo Sofia, Attaallah Bahaaeddin, Maio Maria Raquel, Tabi Younes Adam, Slavkova Elitsa, Klar Verena Svenja, Saleh Youssuf, Idris Mohamad Imran, Turner Vicky, Preul Christoph, Srowig Annie, Butler Christopher, Thompson Sian, Manohar Sanjay G, Finke Kathrin, Husain Masud
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK.
Cognitive Disorders Clinic, JR Hospital, Oxford OX3 9DU, UK.
Brain Commun. 2025 Jan 17;7(1):fcaf024. doi: 10.1093/braincomms/fcaf024. eCollection 2025.
Digital cognitive testing using online platforms has emerged as a potentially transformative tool in clinical neuroscience. In theory, it could provide a powerful means of screening for and tracking cognitive performance in people at risk of developing conditions such as Alzheimer's disease. Here we investigate whether digital metrics derived from an in-person administered, tablet-based short-term memory task-the 'What was where?' Oxford Memory Task-were able to clinically stratify patients at different points within the Alzheimer's disease continuum and to track disease progression over time. Performance of these metrics compared to traditional neuropsychological pen-and-paper screening tests of cognition was also analysed. A total of 325 people participated in this study: 49 patients with subjective cognitive decline, 57 with mild cognitive impairment, 63 with Alzheimer's disease dementia and 156 elderly healthy controls. Most digital metrics were able to discriminate between healthy controls and patients with mild cognitive impairment and between mild cognitive impairment and Alzheimer's disease patients. Some, including Absolute Localization Error, also differed significantly between patients with subjective cognitive decline and mild cognitive impairment. Identification accuracy was the best predictor of hippocampal atrophy, performing as well as standard screening neuropsychological tests. A linear support vector model combining digital metrics achieved high accuracy and performed at par with standard testing in discriminating between elderly healthy controls and subjective cognitive decline (area under the curve 0.82) and between subjective cognitive decline and mild cognitive impairment (area under the curve 0.92), while performing worse in classifying between mild cognitive impairment and Alzheimer's disease patients (area under the curve 0.75). Memory imprecision was able to predict cognitive decline on standard cognitive tests over one year. Overall, these findings show how it might be possible to use a digital memory test in clinics and clinical trial contexts to stratify and track performance across the Alzheimer's disease continuum.
利用在线平台进行的数字认知测试已成为临床神经科学中一种具有潜在变革性的工具。理论上，它可以为筛查和追踪有患阿尔茨海默病等疾病风险的人的认知表现提供一种有力手段。在此，我们研究从基于平板电脑的面对面短期记忆任务——“什么在哪里？”牛津记忆任务中得出的数字指标，是否能够在阿尔茨海默病连续体的不同阶段对患者进行临床分层，并随时间追踪疾病进展。还分析了这些指标与传统的认知神经心理学纸笔筛查测试相比的表现。共有325人参与了这项研究：49名主观认知下降患者、57名轻度认知障碍患者、63名阿尔茨海默病痴呆患者和156名老年健康对照者。大多数数字指标能够区分健康对照者与轻度认知障碍患者，以及轻度认知障碍患者与阿尔茨海默病患者。一些指标，包括绝对定位误差，在主观认知下降患者和轻度认知障碍患者之间也有显著差异。识别准确率是海马萎缩的最佳预测指标，其表现与标准筛查神经心理学测试相当。结合数字指标的线性支持向量模型在区分老年健康对照者和主观认知下降（曲线下面积为0.82）以及主观认知下降和轻度认知障碍（曲线下面积为0.92）方面达到了高精度，表现与标准测试相当，而在区分轻度认知障碍患者和阿尔茨海默病患者方面表现较差（曲线下面积为0.75）。记忆不精确性能够预测一年来标准认知测试中的认知下降情况。总体而言，这些发现表明了在临床和临床试验环境中使用数字记忆测试对阿尔茨海默病连续体的表现进行分层和追踪的可能性。
