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

Abstract

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.

White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people

“…Thus, based on our previous study, we hypothesized that structural abnormalities in brain regions involved in attentional functions might be associated with the risk of unsafe driving among healthy older drivers. Previously, we exploratively built a classification model that could dissociate unsafe drivers from safe drivers with an accuracy of 87.5% using gray matter volume data 12 . Our analysis of the features that contributed to this model showed that gray matter volume in the frontal eye field and inferior parietal lobule (IPL) were strongly associated with a high risk of unsafe driving among healthy older people 12 .…”

“…Previously, we exploratively built a classification model that could dissociate unsafe drivers from safe drivers with an accuracy of 87.5% using gray matter volume data 12 . Our analysis of the features that contributed to this model showed that gray matter volume in the frontal eye field and inferior parietal lobule (IPL) were strongly associated with a high risk of unsafe driving among healthy older people 12 . Since the frontal eye field and IPL constitute the dorsal attention network (DAN), this finding suggested that the DAN is more strongly involved in the risk of unsafe driving in healthy older people than other attention‐related networks, such as the ventral attention network (VAN) and the default mode network (DMN).…”

Aging Brains Degrade Driving Safety Performances of the Healthy Elderly

“…Toyota Central Research and Keio University teams investigated the relationship between brain and driving performances with 39 and 32 participants, respectively (Sakai et al, 2012 ; Yamamoto et al, 2020b ). The present study enrolled 90 participants, which may be not regarded small but relatively large for the analyses of driving performances using actual motor vehicles connected with MRI data.…”

“…Other cerebral sites were not investigated. Very recently, parts of DSPs were investigated using 32 participants and vehicle stability (acceleration and braking without knocking) at an intersection was reported to correlate to the volumes of several cortical regions including the superior frontal sulcus (Yamamoto et al, 2020b ). Elucidating the relationship between brain volume and DSP has yet to be determined.…”

Relationships between Personality Traits and Brain Gray Matter Are Different in Risky and Non-risky Drivers

“…On the other hand, Aydogan et al [ 19 ] have found that people who reported higher risk behavior related to alcohol, smoking, sex, or driving had a lower gray matter volume in areas such as the striatum, putamen, ventromedial, and dorsolateral prefrontal cortex, insula, and cerebellum. In another recent study, Yamamoto et al [ 117 ] have evaluated, using realistic driving situations, risky driving at intersections with stop signs in a group of older adults without cognitive impairment. The best predictors for classifying risky and non-risky drivers were age and the gray matter volume of areas related to executive functions, cognitive control, and incentive processing in the frontal and parietal cortices.…”