Neurophysiological Characteristics Associated with Driving Abilities in Older Adults: A Scoping Review

老年人驾驶能力相关的神经生理特征:一项范围界定综述

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Abstract

With population aging, motor vehicle accidents involving older drivers have increased. Age-related cognitive decline affects driving performance; however, the underlying neural mechanisms remain unclear. This scoping review explored neurophysiological characteristics associated with driving in older adults, including those at risk of dementia. Following PRISMA-ScR guidelines, we searched PubMed, Scopus, and CINAHL for studies examining driving-related neurophysiological measures in older adults aged ≥60 years. Twelve studies were included. Findings converge on load-dependent neural compensation failure: older adults maintain driving performance under low-to-moderate demands, but compensatory mechanisms break down under high cognitive load. Electroencephalography (EEG) studies revealed blunted midfrontal theta upregulation during high-load conditions, associated with reduced steering precision and delayed responses. Event-related potential studies demonstrated that reduced P3b amplitude was associated with missed braking responses and that abnormal visual evoked potentials in Alzheimer’s disease predicted unfit-to-drive classifications. fNIRS studies during driving-related tasks and an fMRI study using a laboratory-based visual task consistently showed prefrontal hyperactivation in older adults. Although some older adults maintained comparable performance to younger adults, the brain–behavior associations observed in younger adults were absent, suggesting that this hyperactivation does not necessarily serve a functional compensatory role. Combined with EEG evidence of impaired oscillatory modulation, these findings suggest that prefrontal hyperactivation does not necessarily compensate for diminished neural synchronization under high-load conditions. Neurophysiological markers hold promise for fitness-to-drive assessments. Future research should employ high-load scenarios and multimodal neuroimaging to verify prefrontal compensatory mechanisms.

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