Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course.

Introduction: Identifying older drivers at risk of critical decline in driving safety performance (DSP) is essential for traffic safety. Regional cerebral gray matter (GM) volume may serve as a biomarker for such decline, but its predictive value in real-world driving contexts remains unclear. Methods: We enrolled 94 cognitively healthy older drivers (45 males, 49 females; mean age 77.66 ± 3.67 years) who completed a standardized driving assessment using actual vehicles on a closed-circuit course. DSP was evaluated across six categories: visual search behavior, speeding, indicator signaling, vehicle stability, positioning, and steering. Scores were assigned by a certified driving instructor, with lower scores (<15th percentile) indicating critical DSP decline. Regional GM volumes were quantified using voxel-based morphometry of MRI scans. Feature selection and classification were performed using the Random Forest machine learning algorithm, optimized to identify the most predictive GM regions. Results: Out of 114 GM regions, eleven were selected as optimal predictors: left angular gyrus, frontal operculum, occipital fusiform gyrus, parietal operculum, postcentral gyrus, planum polare, superior temporal gyrus, and right hippocampus, orbital part of the inferior frontal gyrus, posterior cingulate gyrus, and posterior orbital gyrus. These regions are implicated in attention, spatial cognition, visual processing, and somatosensory integration-functions critical for safe driving. The Random Forest model demonstrated high accuracy and specificity, but moderate precision and recall, limiting immediate real-world application. Discussion: While regional GM volume shows promise for identifying older drivers at risk of critical DSP decline, predictive performance remains suboptimal for practical implementation. Additional factors, such as neuronal connectivity assessed by functional MRI, may improve predictive accuracy. Nonetheless, MRI-based assessment of brain structure can enhance our understanding of the neural mechanisms underlying driving safety and inform strategies to prevent traffic accidents among older adults.