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.