Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

• Published in: Frontiers in aging neuroscience Vol. 12; p. 592979
• Main Authors: Yamamoto, Yasuharu, Yamagata, Bun, Hirano, Jinichi, Ueda, Ryo, Yoshitake, Hiroshi, Negishi, Kazuno, Yamagishi, Mika, Kimura, Mariko, Kamiya, Kei, Shino, Motoki, Mimura, Masaru
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 03.12.2020; Frontiers Media S.A
• Subjects: Accidents; Aging; Automation; Automobile driving; Behavior; Cameras; Decision making; Dementia; Driving ability; Frontal gyrus; gray matter volume; healthy older people; Investigations; Learning algorithms; Machine learning; Magnetic resonance imaging; Memory; Neuroscience; Older people; on-road driving; Questionnaires; Short term memory; Substantia grisea; support vector machine; unsafe driving; Japan; unsafe driving; gray matter volume; support vector machine; healthy older people; machine learning; on-road driving
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2020.592979

In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters (age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus [of Jensen], the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus), were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior.