Impact Objectives• Clarify the neural basis of unsafe driving in healthyolder people• Analyse the links between how a brain ages and highrisk driving actions

Driver behaviour linkedwith brain ageing
Associate Professor Bun Yamagata discusses his innovative medicine-engineering collaboration with
Associate Professor Motoki Shino, Assistant Professor Jinichi Hirano and Dr Yasuharu Yamamoto
combining brain image analysis technology with driving evaluation technology

Associate Professor
Bun Yamagata

Associate Professor
Motoki Shino

Dr YasuharuYamamoto

Firstly, what inspired you to become
involved in researching brain diseases?
I studied abroad at the Center forInterdisciplinary Brain Sciences Research(CIBSR) at Stanford University after
obtaining my degree, and I was engaged inMRI research targeting genetic disorders
such as Turner syndrome and Williams
syndrome. After returning to Japan, Ichanged my research direction to highly
heterogeneous psychiatric disorders suchas depression and developmental disorders(autism and Attention Deficit Hyperactivity
Disorder [ADHD]). I continued research toextract more disease specific brain function
and structural abnormalities by combining
brain imaging techniques such as MRI andNIRS with neuropsychological evaluation
and using Artificial Intelligence (AI)technology (machine learning).
How did the inspiration for your latest
project come about?
This project is a slight departure from mybackground. It began with an encounter
with Professor Motoki Shino, an associate
professor at The University of Tokyo’s

Faculty of Engineering. He specialises inergonomics and mechanical engineering
and was collecting and extracting dataon highly unsafe driver behaviour anddeveloping preventative safety systems
based on his findings. In Japan, traffic
accidents caused by elderly drivers havebecome a social problem, thus I thoughtthat by combining my brain image analysis
technology and Shino’s driving evaluation
technology, we will make a social impact.
What gaps in knowledge are you hoping toaddress?
In Japan, if you are diagnosed withdementia, you are obliged to return yourdriver’s licence and you can no longer
drive. It can also serve as a social message
that healthy older people who have notbeen diagnosed with dementia can drive
safely. Even in healthy older people without
dementia and who are not obviously
forgetful, the risk of unsafe driving increases
as they age. We believe this does not mean
all healthy older people can drive safely, but
it is rather a social message that objective
evaluation and intervention of driving skills
are important.
How have you built on your previous
learnings?
Age-related changes in the brain may beinvolved in the risk of unsafe driving in
healthy older people. Brain volume shrinks

with age, so even if cognitive function isage-appropriate, there must be healthypeople whose brain atrophy is significant.
This may contribute to unsafe driving.
Our second research study investigated
how brain structural abnormalities areinvolved in unsafe driving. We usedneuropsychological tests and informationon the volume of grey matter in the brain as
an objective index. It became clear that age
and the grey matter volume changes in thedorsolateral prefrontal cortex-parietal lobe
region may help predict the risk of unsafe
driving in healthy older people. Our results
suggest that MRI images of the head can bea scientific index to predict the risk of unsafe
driving and that abnormalities in the dorsal
attention network may contribute to unsafe
driving in healthy older people.
Can you talk a little about your collaboration
with Asociate Professor Motoki Shino?
Shino collects and extracts data on highly
unsafe behaviours based on actual driving
behaviour. This study is an innovative
medicine-engineering collaboration projectin which his research team, who specialisein the quantification research on driving
skills of the elderly and my own team, which
focuses on prediction models using brain
imaging research and AI technology, work
together. This project would not be possible
without this collaborative partnership
between us.
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Using machine learning, we established a model thatpredicts the risk of unsafe driving in healthy older people
with high accuracy from neuropsychological tests andpractical visual acuity

Screening for safer roads
machine (SVM) to create the prediction
model used in the study as well as utilising
Optuna in the development of the linear
SVM algorithm. Data was validated using
the LOOCV analysis, but he notes that thedifficulty in validating the data without an
independent data set is a limitation of thisstudy. ‘Strictly speaking, we believe thatit cannot be put into practical use unless
we verify whether the algorithm obtainedin this research can discriminate safe/
unsafe driving with high accuracy using acompletely independent data set,’ he points
out.

Some surprising results were garnered by
the team. They found that the results of
the immediate recall neuropsychologicaltest highlighted the risk of unsafe driving.
‘That is, the high-risk driving behaviours
were not linked with forgetfulness, which
is characteristic of dementia and earlyAlzheimer’s disease, but related to thechanges in attention that appeared to
impair driving behaviour in the elderly,’describes Yamagata. While data is notavailable on driving patterns in dementia
patients as it is not considered ethical toconduct such research, these findings raise
the possibility that early-stage dementia
does not necessarily equate to poor
driving. ‘In addition, results from the MRI
scans consistently showed that subjects
demonstrating unsafe driving behaviours
were more likely to also demonstratechanges in the brain structure within thedorsal attention network,’ he adds.

CHALLENGES AND RESULTS
From Yamagata’s perspective, one of
the important factors for the success of
research is acquiring high-quality data sets.
‘This has proven the most difficult area toimplement as the subjects were required totake part in many different experiments over
several days, which may have dissuaded
potential participants from taking part,’he comments. Each subject was required
to undergo a driving skill evaluation in areal vehicle at a driving school, followed
by a neuropsychological examination at
Yamagata’s facility. They were also given an
MRI scan.

TACKLING A SOCIAL PROBLEM
This is important research that can help
address a growing social problem, makeroads safer and allow at-risk elders toidentify how the way in which their brains
are ageing can affect their behaviours.
‘Using AI technology, we created a modelthat predicts unsafe driving behaviour atstop intersections with the highest accuracy
from various neuropsychological tests,’explains Yamagata. Their findings show therisk of unsafe driving strongly correlatedwith age and attention dysfunction as
determined by the score on the first
immediate recall in the Rey Auditory Verbal

Researchers from the Department of Neuropsychiatry, Keio University School of Medicine and The
University of Tokyo are combining engineering with neuropsychology techniques to link changes inageing brains with inattention and an increased risk of unsafe driving

Japan is known for the longevity of its
citizens, with an average life expectancy ofover 85 years. However, with increased life
expectancy comes an increase in social andhealth problems associated with ageing
populations. Where driving is concerned,for example, Japan has a significant social
problem from accidents caused by elderlydrivers. Drivers aged 75 and over are subject
to cognitive function tests when renewing
their driving licence. However, while thisscreens for dementia, it cannot account for
healthy elderly people who have a greaterrisk of unsafe driving due to the naturalageing process.
Associate Professor Bun Yamagata, fromthe Department of Neuropsychiatry at
Keio University School of Medicine, hasbeen researching brain function andstructural abnormalities within the brain
using imaging techniques such as MRI
and NIRS and combining these datawith neuropsychological evaluations andAI technology. He has partnered withergonomics and mechanical engineering
specialist Associate Professor Motoki Shino,from The University of Tokyo, to conduct
a new and innovative study combining
engineering with neuropsychology
techniques to gather data on driving
behaviours and brain atrophy patterns. They
seek to develop algorithms that predictthe risk of dangerous driving behaviours inelderly drivers.
Yamagata has conducted a series of studies
exploring the relationship between thestructural connectivity of white matter in thebrain and the driving ability of healthy olderpeople. He found that changes in the whitematter within the dorsal attention network
may contribute to a higher risk of unsafe
driving behaviours in healthy elderly people.
‘We found that the white matter structure of
the superior longitudinal fascicle connecting
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the dorsolateral prefrontal cortex and theparietal lobe was significantly impaired in
the unsafe driving group,’ he describes.
REAL-WORLD DATA COLLECTION
Gathering real information on the driving
behaviour of older drivers was a criticalaspect of this study. Using a car equippedwith a global positioning system (GPS),a video camera and a drive recorder,
the team collected the data required tobuild a new driving skill index. This work
enabled them to quantitatively analyse thedriving behaviours of their subjects. ‘Usingmachine learning, we established a modelthat predicts the risk of unsafe driving in
healthy older people with high accuracy
from neuropsychological tests and practicalvisual acuity,’ outlines Yamagata.
The researchers performed LASSO (least
absolute shrinkage and selection operator)and LOOCV (leave-one-out cross validation)
analyses on the data gathered based on theAkaike Information Criterion (AIC). This
enabled them to select specific features fortheir prediction model. Using LOOCV alsoenabled the team to optimise the training
sample and avoid a split bias situation that
could have arisen due to their small sample
size. Yamagata used a linear support vector

Voxels showing significant higher axial diffusivity (AD) values in unsafe drivers compared with safe drivers. An
AD increase in unsafe drivers relative to safe drivers is shown in red. The whole-brain skeleton is shown in green.

Learning Test. Drivers over 75 in Japan arescreened for dementia when renewing theirlicences. However, Yamagata has shownthat healthy elderly people who also have ahigher risk of road accidents could also be
screened using MRI screening to highlightatrophy in parts of the brain. ‘We clarified
that attentional dysfunction in the elderly isinvolved in unsafe driving risks,’ he states.
Yamagata believes that his work may
help in reducing the risk of serious traffic
accidents by identifying healthy older drivers
who are more likely to demonstrate poor
driving behaviours. By using brain imaging
techniques in conjunction with AI, he isconfident that this group of drivers can bereadily identified and interventions such asadditional driving lessons can be offered.
This would not only keep costs low, by
targeting these resources only to thosemost at risk, but also benefit individuals andsociety in general by reducing the likelihoodof serious road accidents.
One of his plans for future research aims
to target implementation of large-scale
screening of elderly drivers. However, theseresults must be verified on a large scale, in a
multi-centre trial, before being carried out. ‘I
am also seeking to conduct similar research
focused on ADHD, which is linked with ahigh risk of unsafe driving,’ Yamagata says.
This disorder is characterised by impulsive
behaviour, hyperactivity and inattention, andmay be a useful model to investigate theimpact of inattention on unsafe driving.

Road accidents involving elderly drivers havebecome a widespread global problem, and
it is important steps are taken to tackle thisissue. However, a balance between budget
and impact must be struck to achieve aviable solution for all. This ground-breaking
work by Yamagata and Shino could resultin a cost-effective method for identifying
high-risk drivers, enabling them to receive
targeted assistance in improving theirdriving behaviours, making the roads safer
for all. l

Project Insights
FUNDINGThe General Insurance Associationof Japan and JSPS, KAKENHI Grant
Numbers 20H03607 and JP16H03130
COLLABORATORS
Associate Professor Motoki Shino,
Department of Human and Engineered
Environmental Studies, The University ofTokyo, JapanTEAM MEMBERS
Assistant Professor Jinichi Hirano andDr Yasuharu Yamamoto, Department ofNeuropsychiatry, Keio University Schoolof Medicine
CONTACT
Associate Professor Bun Yamagata
Project Coordinator
T: +81 3 5363 3971
E: yamagata@a6.keio.jp
BIO
Associate Professor Bun Yamagata
is based at the Department of
Neuropsychiatry, Keio University
School of Medicine. His research
focuses on elucidating disease-specificneurobiological evidence in psychiatricdisorders such as depression anddevelopmental disorders.
Associate Professor Motoki Shino isbased at The University of Tokyo. His
research has covered Wellbeing Scienceand Assistive Technology that leads
to the improvement of QOL. Shino
is looking to the establishment oftechnology and study to provide a safe
and comfortable life through a mixtureof human adaptability, interface androbotics for the persons with disabilities
and the elderly.

The four cortical regions identified as consistent classification inputs were located within the cortical regions
involved in cognitive functions essential for driving, such as voluntary orienting of attention, decision-making andworking memory.
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