Abstract
AbstractBackgroundEntorhinal cortex is vulnerable to neurodegenerative changes in the earliest stages of AD. However, this early AD stage cannot be detected by currently available neuropsychological tests. The entorhinal cortex contains spatial modulated cells, known as grid cells, that play a key role in supporting path integration (PI), a crucial component of spatial navigation that supports continuous integration of self‐motion information and in combination with environmental cues provides accurate internal representation of space. Previously, PI deficits were associaed with impaired grid cell function in healthy older adults and in young APOE‐E4 carriers. Importantly, PI performance has successfully discriminated between biomarker positive and negative mild cognitive impairment patients and between healthy older adults at higher and lower risk of AD. In this study, we tested whether performance on an immersive virtual reality PI task can differentiate between patients with and without subjective cognitive decline (SCD).MethodA group of healthy older adults and SCD patients will perform an immersive virtual reality PI task. In this task, participants are immersed in an open‐field virtual environment and are guided along short, curved paths. At certain points they are asked to stop and indicate their initial heading orientation and the location of an object, providing information about distance and orientation coding. During the task participants have access to multisensory self‐motion cues (visual, vestibular and proprioceptive), all of which are pivotal for supporting grid cell function. Additionally, participants will be screened for presence of objective cognitive impairments and will undergo an assessment of their visuospatial working memory and balance.ResultWe will compare if SCD patients perform worse than healthy older controls in the general PI performance measures (e.g., PI error, response time). Furthermore, we will apply a recently developed computational model to identify multiple sources of PI error. This approach allows for a very nuanced characterization of PI deficits, and, as a result, provides additional, potentially even more sensitive markers of very early AD compared to typical PI performance measures.ConclusionThe results will be discussed with reference to the utility of immersive virtual PI as a sensitive biomarker for early, preclinical, AD manifestation.
Published Version
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