Identifying biomarkers of deafness-induced cerebral plasticity using MRI

神经科学 可塑性 神经可塑性 医学 磁共振成像 生物标志物 神经影像学 病理 大脑定位 脑血流
作者
Alessandra Sacco,Stephen G Gordon,Stephen G. Lomber
出处
期刊:NeuroImage [Elsevier BV]
卷期号:333: 121943-121943 被引量:1
标识
DOI:10.1016/j.neuroimage.2026.121943
摘要

• Animals have been vital to understanding neuroplasticity following sensory loss • Microstructural effects of plasticity can be appreciated at the macroscale via MRI • Recent animal advancements in MRI deafness literature are largely limited to cats • Changes are highly localized, impacting various ROIs throughout the mammalian brain Scientific knowledge of cerebral plasticity has evolved substantially in recent decades, with findings from animal models complementing and extending human observations. Notably, invasive techniques unique to animal research have been fundamental in the domain of sensory neuroscience, granting a more comprehensive understanding of the dynamic structure and function of the mammalian brain. The purpose of this review is to explore biomarkers of deafness-induced compensatory crossmodal plasticity from a neuroimaging perspective, with a particular focus on the recent presence of animal models in the realm of magnetic resonance imaging (MRI) literature. There is a limited number of MRI-based investigations concerning profound, bilateral, sensorineural hearing loss in animal models – all using the perinatally-deafened cat – with each endeavoring to further the translational possibilities between more invasive experimental approaches and those implemented in human research. These studies collectively allude to the disproportionate impact that compensatory plasticity has on sensory-related regions within and beneath the cortex, and reflect a complex interplay between structure and function that primarily relies on regionally-distinct modifications within cerebral gray matter to adapt to a modified perceptual environment. Overall, although the emerging presence of deaf animals in MR literature has improved the potential of translational inferences in recent years, many intricacies remain to be unraveled.
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