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. Metho...
| Published in: | Frontiers in Aging Neuroscience pp. 1 - 14 |
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| Main Authors: | , , |
| Format: | diagnostic images pictorial research tables/charts Journal Article |
| Published: |
Frontiers Media S.A.
2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185837100&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185837100 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2025 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 185837100 185837100 185837100 10.3389/fnagi.2025.1462951 185837100 ppf: 1 ppct: 13 formats: tig: atl: Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course. aug: au: Putra, Handityo Aulia Park, Kaechang Yamashita, Fumio affil: Department of Electrical, Electronics, and Information Engineering, Nagaoka University of Technology, Nagaoka, Japan sug: subj: Gray Matter Anatomy and Histology Cerebral Cortex Anatomy and Histology Automobile Driving In Old Age Safety In Old Age Psychomotor Performance Evaluation Biological Markers Human Male Female Aged Aged, 80 and Over Funding Source Predictive Value of Tests Cognition In Old Age Magnetic Resonance Imaging Random Forest Prefrontal Cortex Physiology Frontal Lobe Physiology Occipital Lobe Physiology Parietal Lobe Physiology Hippocampus Physiology Spatial Perception Evaluation Evoked Potentials, Somatosensory Evaluation Accidents, Traffic Prevention and Control Japan Psychological Tests Interviews Neuropsychological Tests Descriptive Statistics Data Analysis Software Machine Learning Validity Logistic Regression Neural Networks (Computer) ROC Curve Analysis of Variance Post Hoc Analysis Cerebrospinal Fluid Metabolism Aging White Matter Physiology Sex Factors Sensitivity and Specificity Attention Evaluation Memory Evaluation Decision Making T-Tests Aged: 65+ years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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