Structural Brain Biomarker on Driving Safety in Healthy Older Adults: An MRI-Based Review

Abstract This review synthesizes current evidence on the relationship between structural brain changes and driving safety in cognitively healthy older adults. Using magnetic resonance imaging (MRI) structural biomarkers—such as white matter hyperintensities (WMH), brain atrophy (BA), and regional gray matter volumes (rGMVs)—recent studies have demonstrated that even mild or asymptomatic alterations are associated with impaired driving behavior and increased crash risk. Emerging machine learning models that incorporate rGMVs have achieved high accuracy and specificity in identifying high-risk drivers, though sensitivity and precision remain limited when relying solely on structural MRI data. Japan’s Brain Dock system, an MRI-based brain healthcare screening program developed uniquely in Japan, provides a valuable infrastructure for large-scale neuroimaging-based risk assessment. Importantly, structural MRI biomarkers can be influenced by lifestyle improvements, such as quitting smoking and reducing alcohol consumption, as well as by the treatment of lifestyle-related diseases, including diabetes and hypertension. Overall, this review highlights the potential of neuroimaging-informed approaches to identify at-risk drivers and to support targeted preventive strategies, thereby contributing to both brain healthcare program and traffic safety in aging societies.

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Publication Details

Journal
International Journal of Intelligent Transportation Systems Research
Published
2025-12-22
DOI
https://doi.org/10.1007/s13177-025-00601-w
Primary Topic
Older Adults Driving Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Structural Brain Biomarker on Driving Safety in Healthy Older Adults: An MRI-Based Review

Handityo Aulia Putra, Kaechang Park
International Journal of Intelligent Transportation Systems Research
Older Adults Driving Studies
article

Structural Brain Biomarker on Driving Safety in Healthy Older Adults: An MRI-Based Review

Handityo Aulia Putra, Kaechang Park
article en

Abstract

Abstract This review synthesizes current evidence on the relationship between structural brain changes and driving safety in cognitively healthy older adults. Using magnetic resonance imaging (MRI) structural biomarkers—such as white matter hyperintensities (WMH), brain atrophy (BA), and regional gray matter volumes (rGMVs)—recent studies have demonstrated that even mild or asymptomatic alterations are associated with impaired driving behavior and increased crash risk. Emerging machine learning models that incorporate rGMVs have achieved high accuracy and specificity in identifying high-risk drivers, though sensitivity and precision remain limited when relying solely on structural MRI data. Japan’s Brain Dock system, an MRI-based brain healthcare screening program developed uniquely in Japan, provides a valuable infrastructure for large-scale neuroimaging-based risk assessment. Importantly, structural MRI biomarkers can be influenced by lifestyle improvements, such as quitting smoking and reducing alcohol consumption, as well as by the treatment of lifestyle-related diseases, including diabetes and hypertension. Overall, this review highlights the potential of neuroimaging-informed approaches to identify at-risk drivers and to support targeted preventive strategies, thereby contributing to both brain healthcare program and traffic safety in aging societies.

International Journal of Intelligent Transportation Systems Research
Nagaoka University (JP), Kochi University of Technology (JP), Advanced Telecommunications Research Institute International (JP), Nagaoka University of Technology (JP)
General Insurance Association of Japan, Japan Society for the Promotion of Science
Industry, innovation and infrastructure
Openalex Percentile: Top 25%
Older Adults Driving Studies
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Structural Brain Biomarker on Driving Safety in Healthy Older Adults: An MRI-Based Review — Handityo Aulia Putra, Kaechang Park · International Journal of Intelligent Transportation Systems Research (2025) | TGRS Research Map | TGRS