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

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.

Keywords: APOE genotype; Alzheimer’s disease; personalized health care; preclinical diagnosis; spatial navigation.

Fig. 1.

SHQ goal-oriented wayfinding levels 6 (A), 8 (B), and 11 (C). Players initially see a map featuring a start location and several checkpoints (in red) to find in a set order. Checkpoints are buoys with flags marking the checkpoint number. Participants study a map of the level for a recorded number of seconds. When participants exit the map view, they are asked to immediately find the checkpoints (or goals) in the order indicated on the map under timed conditions. As participants navigate the boat through the level, they must keep track of their location using self-motion and environmental landscape cues such as water–land separation. The initiation time is zero as the boat accelerates immediately after the map disappears. If the participant takes more than a set time, an arrow appears pointing in the direction along the Euclidean line to the goal to aid navigation. (D) In flare accuracy levels (here, levels 9 and 14), participants are not provided with an allocentric map. Instead, they immediately navigated along a river to find a flare gun. Once they find the flare gun at the end of the river, the boat rotates by 180°, and participants are asked to choose one of three possible directions (right, front, and left) that they believe points to the starting point. This level requires participants to (i) form an accurate representation of the starting point relative to their position and (ii) integrate this representation with a representation of the direction they are facing after the rotation. Depending on their accuracy, players receive one, two, or three stars.

Fig. 2.

Mixed-effects models, with subject-level random effects, adjusted for age, sex, and baseline cognitive ability show the following: (A) main effect of genotype (b = 0.22; P = 0.004) on wayfinding distance; ε3ε4 carriers participants deviate from the more Euclidean trajectory leading to an overall greater distance traveled to complete the wayfinding levels relative to the ε3ε3 carriers. (B) No main effect of genotype on wayfinding duration (i.e., time taken to complete wayfinding levels); both groups used the same boat acceleration during wayfinding. (C) No main effect of genotype on flare accuracy, which required participants to integrate newly acquired allocentric information with egocentric-viewpoint–based cues presented at the end of the level. The spatial trajectory of each participant (colors red and green were used to differentiate the trajectories by the genetic groups) on wayfinding level 6 (D), level 8 (E), and level 11 (F), using x and y coordinates generated during game play. The maps generated illustrated a drift-like navigation tendency in the ε3ε4 group that can be characterized as navigational preference to deviate from the most Euclidean path and travel toward the border of the environment compared with the ε3ε3 who demonstrated a preference to navigate more along the direct path to the checkpoint goal. A by-level analysis on wayfinding distance in the three levels showed that the e4 allele increased wayfinding distance on level 6 (F = 5.48, P = 0.02) and level 8 (F = 4.08, P = 0.04).

Fig. 3.

ROC curves for SHQ distance [pink line (laboratory cohort); dark pink line (laboratory–benchmark combined)] and nonverbal episodic memory [gold line (laboratory cohort)] predicting APOE genotype. SHQ (laboratory cohort): AUC, 0.714; SE, 0.068; 95% CI, 0.555–0.822 | SHQ distance (laboratory–benchmark combined): AUC, 0.701; SE, 0.031; 95% CI, 0.639–0.759 | nonverbal episodic memory (laboratory cohort): AUC, 0.541; SE, 0.074; 95% CI, 0.286–0.578.

Fig. 4.

Each ε3ε4 carrier score (red line) on SHQ distance plotted against the normal distribution of scores from an age/sex/education-matched subpopulation of the benchmark dataset (green histogram). Wayfinding distance scores are on the x axis and frequency of the benchmark population on the y axis. Sex is represented as male (M) and female (F). Age is illustrated under each distribution right of sex.