Bright spots of white matter damage on a brain MRI appear to forecast how well older adults will handle the everyday demands of driving. In a study set for presentation at the American Stroke Association's International Stroke Conference in New Orleans (Feb. 4-6), Chia-Ling Phuah, M.D., of the Barrow Neurological Institute in Phoenix, and colleagues followed 220 community-dwelling adults aged 65 and older who did not have dementia when the study began, tracking their real-world driving behavior over a mean of 5.6 years.
White matter hyperintensities, or WMH, are areas of damage to the brain's connective wiring commonly linked to small vessel disease and visible on routine MRI. The researchers measured how much WMH each participant had using deep learning-based image segmentation, then compared those measurements with driving patterns. Participants with a greater WMH burden were already driving less often, visiting fewer distinct destinations, and showing less adaptability at the study's start - and those measures fell faster over the following years. Greater burden also predicted more crashes and more hard braking events, with growth of WMH in the posterior regions of the brain carrying the highest crash risk.
Driving is one of the more revealing real-world tests of cognition, since it demands attention, spatial judgment, and rapid decisions all at once. That makes shrinking trip variety or increasing hard braking a potentially useful early behavioral signal, particularly given that 17% of participants with heavier WMH burden went on to develop cognitive impairment during follow-up. For clinicians and families, the findings suggest that a brain scan finding often described as incidental may carry practical information about a person's functional trajectory.
The work is being presented at a scientific meeting, which means the details will need to hold up through full peer review, and an association between WMH and driving decline does not by itself establish that the white matter damage is the direct cause. Still, the combination of automated MRI analysis and passively collected driving data points toward a way to monitor everyday function without relying on self-report or a clinic visit.