Big data in spatial navigation – towards personalised cognitive diagnostics of ‘at-genetic-risk’ Alzheimer’s disease

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Spatial navigation is emerging as a critical factor in identifying preclinical Alzheimer’s disease (AD). However, the impact of inter-individual navigation ability and demographic risk factors (eg APOE, age, sex) on spatial navigation make it difficult to identify ‘at-high-risk’ of AD people in the preclinical stages. In the current study we use spatial navigation Big Data (n=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 ageing lab cohort into high vs. low risk people based on their genetic (APOE) and demographic (sex, age, 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 the first 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 stepping stone for individualised diagnostics and outcome measures of cognitive symptoms in preclinical AD.


Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America (PNAS)
Publication statusAccepted/In press - 25 Mar 2019

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