Toward personalized cognitive diagnostics of at-genetic-risk Alzheimer's disease

Coughlan, Gillian, Coutrot, Antoine, Khondoker, Mizanur, Minihane, Anne-Marie, Spiers, Hugo and Hornberger, Michael (2019) Toward personalized cognitive diagnostics of at-genetic-risk Alzheimer's disease. Proceedings of the National Academy of Sciences of the United States of America, 116 (19). pp. 9285-9292. ISSN 1091-6490

[img]
Preview
PDF (Accepted_Manuscript) - Submitted Version
Download (1MB) | Preview
[img]
Preview
PDF (Accepted_Manuscript) - Submitted Version
Download (777kB) | Preview

Abstract

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 (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 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.

Item Type: Article
Additional Information: Early title: Big data in spatial navigation – towards personalised cognitive diagnostics of ‘at-genetic-risk’ Alzheimer’s disease
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
Depositing User: LivePure Connector
Date Deposited: 26 Mar 2019 14:30
Last Modified: 12 May 2020 23:53
URI: https://ueaeprints.uea.ac.uk/id/eprint/70348
DOI: 10.1073/pnas.1901600116

Actions (login required)

View Item View Item