Vestibular contribution to path integration deficits in ‘at-genetic-risk’ for Alzheimer’s disease

Abstract

Path integration changes may precede a clinical presentation of Alzheimer’s disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer’s disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer’s disease risk carriers.

Aging of the vestibular system and its relationship to dementia

“…The studies published over the last year support and extend Previc's [26] hypothesis that the vestibular system makes an important contribution to cognitive function and that vestibular loss may be a risk factor for AD. With respect to the latter, the studies by Coughlin et al [40 ▪▪ ] and Lim et al [41 ▪▪ ] seem particularly interesting, especially since the latter was based on a very large sample size. One important development in the area is that more studies are controlling for potential confounds by hearing loss (e.g., [33 ▪▪ ,36 ▪ ,37 ▪▪ ]; see [34] for a review).…”

“…Coughlin et al [40 ▪▪ ] developed a vestibular rotation task in which subjects had to use an iPad to indicate the direction of their starting point following rotation, in the absence of visual cues. They then performed genotyping for apolipoprotein (APOE) ε3ε4 and ε3ε3 carriers and employed machine learning algorithms (random forest classification, support vector machines and a multilayer perceptron) to determine whether they could distinguish APOE ε4 carriers, who have an approximately 50% risk of developing AD by age 76, from noncarriers, using the performance on the vestibular rotation task.…”

Modeling the impact of genotype, age, sex, and continuous navigation on pathway integration performance

“…Notably, young APOE4 carriers navigating a virtual environment displayed compromised grid-cell-like representations (Kunz et al, 2015). Recent findings indicate that healthy APOE4 carriers performed less accurately in pure PI tasks and angle estimation tasks compared to a control group (Bierbrauer et al, 2020; Coughlan et al, 2023). However, the precise connection between the malfunctioning entorhinal cortex and PI performance remains elusive (for an overview, refer to (Segen et al, 2022)).…”