Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, United Kingdom.
CNRS-Laboratoire des Sciences du Numérique de Nantes, University of Nantes, 44300 Nantes, France.
Proc Natl Acad Sci U S A. 2019 May 7;116(19):9285-9292. doi: 10.1073/pnas.1901600116. Epub 2019 Apr 23.
Spatial navigation is emerging as a critical factor in identifying preclinical Alzheimer's disease (AD). However, the impact of interindividual navigation ability and demographic risk factors (e.g., APOE, age, and sex) on spatial navigation make it difficult to identify persons "at high risk" of AD in the preclinical stages. In the current study, we use spatial navigation big data ( = 27,108) from the Sea Hero Quest (SHQ) game to overcome these challenges by investigating whether big data can be used to benchmark a highly phenotyped healthy aging laboratory cohort into high- vs. low-risk persons based on their genetic (APOE) and demographic (sex, age, and educational attainment) risk factors. Our results replicate previous findings in APOE ε4 carriers, indicative of grid cell coding errors in the entorhinal cortex, the initial brain region affected by AD pathophysiology. We also show that although baseline navigation ability differs between men and women, sex does not interact with the APOE genotype to influence the manifestation of AD-related spatial disturbance. Most importantly, we demonstrate that such high-risk preclinical cases can be reliably distinguished from low-risk participants using big-data spatial navigation benchmarks. By contrast, participants were undistinguishable on neuropsychological episodic memory tests. Taken together, we present evidence to suggest that, in the future, SHQ normative benchmark data can be used to more accurately classify spatial impairments in at-high-risk of AD healthy participants at a more individual level, therefore providing the steppingstone for individualized diagnostics and outcome measures of cognitive symptoms in preclinical AD.
空间导航能力正在成为识别临床前阿尔茨海默病（AD）的关键因素。然而，个体间导航能力和人口统计学风险因素（如 APOE、年龄和性别）对空间导航的影响使得难以在临床前阶段识别出具有 AD“高风险”的个体。在当前研究中，我们使用来自 Sea Hero Quest（SHQ）游戏的空间导航大数据（=27108），通过调查大数据是否可以用于根据其遗传（APOE）和人口统计学（性别、年龄和教育程度）风险因素将高度表型化的健康衰老实验室队列基准为高风险与低风险个体，从而克服这些挑战。我们的结果复制了 APOE ε4 携带者中的先前发现，表明在 AD 病理生理学最初受影响的内侧颞叶结构（entorhinal cortex）中存在网格细胞编码错误。我们还表明，尽管男性和女性之间的基线导航能力存在差异，但性别不会与 APOE 基因型相互作用影响 AD 相关空间障碍的表现。最重要的是，我们证明可以使用大数据空间导航基准可靠地区分这种高风险的临床前病例与低风险参与者。相比之下，参与者在神经心理学情景记忆测试中无法区分。总之，我们提供的证据表明，在未来，SHQ 正常基准数据可用于更准确地在个体层面上对 AD 高危健康参与者的空间障碍进行分类，从而为临床前 AD 的认知症状的个体化诊断和预后提供了基础。
