Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges.
Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges.
复制标题
DOI：
10.1002/dad2.12135
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yong KXX
中科院分区：
文献类型：
--
作者：
Bellio M;Oxtoby NP;Walker Z;Henley S;Ribbens A;Blandford A;Alexander DC;Yong KXX
最近临床和临床前阿尔茨海默病（AD）的数据共享倡议导致越来越多的非临床研究人员使用现代数据驱动的计算方法分析这些数据集。认知测试是这些数据集的关键组成部分，代表了建立表型和监测症状进展的主要临床工具。尽管计算分析在补充对阿尔茨海默病的临床理解方面具有潜力，但计算研究人员和其他非专业受众往往不熟悉认知测试的特征和多因素性质。本文概述了认知测试数据的核心特征、特质和应用。我们报告了数据共享计划中常见的测试，强调了选择和分析中的关键考虑因素，并提供了避免误解风险的建议。最终，认知测量的更大透明度将最大限度地提供对阿尔茨海默病的见解，特别是关于了解阿尔茨海默病表型异质性的程度和基础。
Recent data‐sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non‐clinical researchers analyzing these datasets using modern data‐driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non‐specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data‐sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.