Yamamoto Yasuharu, Yamagata Bun, Hirano Jinichi, Ueda Ryo, Yoshitake Hiroshi, Negishi Kazuno, Yamagishi Mika, Kimura Mariko, Kamiya Kei, Shino Motoki, Mimura Masaru
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Office of Radiation Technology, Keio University Hospital, Tokyo, Japan.
Front Aging Neurosci. 2020 Dec 3;12:592979. doi: 10.3389/fnagi.2020.592979. eCollection 2020.
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.
在发达国家，老年驾驶员引发的交通事故数量正在增加。在导致致命事故的老年驾驶员中，约有一半认知功能正常。因此，识别认知功能正常但有导致致命交通事故高风险的老年驾驶员非常重要。然而，尚未建立评估老年驾驶员驾驶能力的标准化方法。我们旨在建立对驾驶能力的客观评估，并阐明健康老年人不安全驾驶的神经基础。我们招募了32名65岁以上的健康老年人，并通过道路驾驶测试对不安全驾驶员进行分类。然后，我们利用机器学习方法，根据临床特征和灰质体积数据，将不安全驾驶员与安全驾驶员区分开来。21名参与者被归类为安全驾驶员，11名参与者被归类为不安全驾驶员。线性支持向量机分类器成功地将不安全驾驶员与安全驾驶员区分开来，准确率为87.5%（敏感性为63.6%，特异性为100%）。五个参数（年龄以及四个皮质区域的灰质体积，包括中央前沟左上部、左第一中间沟[詹森氏]、额下回右眶部和右上额沟），被一致选为最终分类模型的特征。我们的研究结果表明，与注意力的自愿定向制定决策和工作记忆相关的皮质区域可能构成驾驶行为的基本神经基础。
