Thursday, May 19, 2022
5/19/2022
PROJECT SUMMARY/ABSTRACT Early diagnosis and identification of predictors for Alzheimer’s Disease (AD) is crucial as it allows intervention with prevention and treatment strategies when neuronal loss is at its minimum. Spatial navigation is a complex and multi-component skill that gets impaired early in the course of brain diseases and may be considered a relevant, sensitive and specific marker for future clinical progress of AD, even in its preclinical stage. Active spatial navigation assessments allow to identify modifications of gait associated with cognitive decline, and to amplify the difficulty of the test through a dual task effect created by the increased postural demands of walking. This study will validate the first motor-cognitive screening instrument able to extract digital markers in the form of navigational and gait performances in people at risk of developing AD. We propose that a fully immersive Virtual Reality (VR) navigational test has numerous advantages with respect to classical tests because it allows the manipulation of environmental features based on specific needs. We will use a VR version of the Floor Maze Test (VR-FMT) to create virtual mazes with preferred complexity and display them within a commercial VR headset. Cognitively normal adults at low risk and higher risk of developing AD and subjects with amnesic mild cognitive impairment will complete two visits. In the first visit, a battery of neuropsychological tests will be taken. In the second, participants will perform multiple navigations inside the VR-FMT. Two visuals representations (vista and environmental), and two explorations types (real walking and with a joystick) will be tested. Gait will be recorded using VR trackers and a motion capture system. Functional near-infrared spectroscopy (fNIRS) will be used to measure resting-state brain connectivity. In Aim 1 we will validate the gait measures and the manipulations operated by the VR-FMT. We hypothesize that the portable trackers will show comparable validity in measuring gait compared to the optical motion capture system and that participants will show different navigation performance in the walking and the in-place versions and in the environmental and vista spaces versions of the VR-FMT. In Aim 2 we will investigate the ability of the VR-FMT as a test to differentiate the various levels of cognitive impairment. We hypothesize that navigation performance while performing the active version of the VR-FMT in the environmental space would show superior ability to distinguish the groups. Then, a machine learning algorithm will be used to extract the most significant features and classify participants. We hypothesize that both gait and navigation performances would increase the sensitivity of the classifier. In Aim 3 we will explore associations between performance in the VR-FMT, neuropsychological tests, and brain connectivity. We hypothesize that lower navigation performance will be associated with altered brain connectivity and lower psychomotor speed, memory and executive function scores. Findings from this research will set the stage for further longitudinal studies which will be aimed at predicting accumulating AD biomarkers. The long-term goal is to develop an accurate, low-cost, user-friendly, and portable system that can be used to predict cognitive decline.
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