How human aging disrupts the head direction network: evidence from VR experiments and mechanistic models
Bernard, M.; Shine, J.; Bicanski, A.; Wolbers, T.
Show abstract
Navigational deficits during aging can severely limit mobility and reduce quality of life. While research on the underlying neural mechanisms has primarily focused on medial temporal lobe dysfunction, the head-direction (HD) system--a core component of the mammalian navigation circuit--remains largely unexplored in the context of aging. We established an immersive virtual reality paradigm that provides direct behavioral read-outs of HD signals. In addition, we developed a biologically inspired HD model, which accommodates noise sources that simulate age-related neural changes. Compared to younger adults, older participants exhibited larger angular errors, and a brief delay increased their heading uncertainty. In addition, our novel ring-attractor architecture shows that synaptic noise and small-scale neuronal loss replicate the magnitude and dynamics of the age-related deficits observed behaviorally. Together, these behavioral and computational findings provide the first evidence that aging compromises the fidelity and stability of the HD system. By pinpointing noise accumulation and neuron attrition as mechanistic contributors, our study significantly advances the understanding of spatial navigation deficits in old age, and it highlights novel targets for interventions aimed at preserving navigational abilities and quality of life.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Modulation of spatial cue processing across the lifespan: a geometric polarization of space restores allocentric navigation strategies in children and older adults 96%
- A circuit mechanism for irrationalities in decision-making and NMDA receptor hypofunction: behaviour, computational modelling, and pharmacology 95%
- Eye movements reveal spatiotemporal dynamics of visually-informed planning in navigation 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.