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With rapid advances in modern medicine and technology, human lifespan has increased dramatically, especially in the developed world. This increase has created new challenges to keep the older population mobile and safe. Older drivers (65 years and older) are at a higher risk for fatal crashes for every vehicle mile travelled in spite of the self-regulating measures they take (e.g., wearing safety belts, less likelihood of driving at night, or speeding)(1). The following facts reiterate the need for concerted efforts to understand the biological basis of unsafe driving behavior in the older population. Unsafe driving is defined "as any action or lack of action on the part of the driver that increases their risk of a collision"(2):

- In 2008, over 32 million older drivers were licensed in the U.S. (3) and this number is expected to exceed 40 million by 2020 (4).
- Over 183,000 older individuals were injured in traffic crashes (approximately 8% of all traffic injuries) in the U.S. in 2008.
- Older driver crash rate is second only to that of adolescent drivers, who have the highest crash rate.
- By 2020, 40% of fatal crashes are predicted to involve older drivers (5, 6).

Several efforts have been made to identify markers that characterize unsafe older drivers. These include extensive neuropsychological assessments involving perceptual-cognitive-psychomotor tests, physical tasks (7-14) and also performance metrics in a driving simulator (15-17). Several researchers have demonstrated the promise of a "Useful Field of View" test (UFOV) to predict driving performance (18). However, extant evidence suggests that the UFOV test which mainly evaluates "low-level" processing such as sensory and perceptual capacities and processing speed does not predict all aspects of driving behavior, "high-level" processing involving central cognitive processing abilities such as executive function, conflict management, attention, and inhibition which decreases in older adults is also a major factor contributing to poor performance and increased crash rates (19, 20).Yet, there is no consensus on a set of tests that can reliably and accurately identify unsafe older driver behavior. In addition, several studies have used driving simulator metrics to predict unsafe driving behavior, but the limited availability of full-scale high fidelity simulators due to prohibitive costs, and the drop-out rates of older adults from these studies due to issues of motion sickness/vertigo (21), have provided limited results which have not been replicated with large samples.

Studying neural correlates of age-related differences would provide a brain basis behind the correlations between neuropsychological tests and road driving performance. A number of neuroimaging studies are also providing evidence that neuroimaging measures alone or in combination with neuropsychological measures are a better predictor of changes in subject behavior (23, 24). By identifying the neuroimaging patterns that can predict safe versus unsafe driving, it may be possible to develop innovative models of older driving behavior that combine neurobiological and neuropsychological dimensions of driving. Current methods of predicting driving performance have mainly utilized neuropsychological tests which have shown predictive abilities in the 2-5 year range (15, 17, 25). Neuroimaging or neuroimaging combined with neuropsychological tests may extend the predictive abilities beyond this time period and moreover provide a longer time window to utilize novel methods to improve driver behavior.