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Article Dans Une Revue Neural Networks Année : 2022

Computational epidemiology study of homeostatic compensation during sensorimotor aging

Denis Sheynikhovich
Eduardo Ros
Angelo Arleo

Résumé

The vestibulo-ocular reflex (VOR) stabilizes vision during head motion. Age-related changes of vestibular neuroanatomical properties predict a linear decay of VOR function. Nonetheless, human epidemiological data show a stable VOR function across the life span. In this study, we model cerebellum-dependent VOR adaptation to relate structural and functional changes throughout aging. We consider three neurosynaptic factors that may codetermine VOR adaptation during aging: the electrical coupling of inferior olive neurons, the long-term spike timing-dependent plasticity at parallel fiber-Purkinje cell synapses and mossy fiber-medial vestibular nuclei synapses, and the intrinsic plasticity of Purkinje cell synapses Our cross-sectional aging analyses suggest that long-term plasticity acts as a global homeostatic mechanism that underpins the stable temporal profile of VOR function. The results also suggest that the intrinsic plasticity of Purkinje cell synapses operates as a local homeostatic mechanism that further sustains the VOR at older ages. Importantly, the computational epidemiology approach presented in this study allows discrepancies among human cross-sectional studies to be understood in terms of interindividual variability in older individuals. Finally, our longitudinal aging simulations show that the amount of residual fibers coding for the peak and trough of the VOR cycle constitutes a predictive hallmark of VOR trajectories over a lifetime.
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Origine : Publication financée par une institution

Dates et versions

hal-03500873 , version 1 (22-12-2021)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Niceto R Luque, Francisco Naveros, Denis Sheynikhovich, Eduardo Ros, Angelo Arleo. Computational epidemiology study of homeostatic compensation during sensorimotor aging. Neural Networks, 2022, 146, pp.316-333. ⟨10.1016/j.neunet.2021.11.024⟩. ⟨hal-03500873⟩
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