Neurocognitive Correlates of Driving Capacity
Along with other civil capacities, evaluation of driving capacity is an important role of neuropsychologists. Even when not part of the referral question or presenting complaint, the capacity for safe motor vehicle operation remains relevant when evaluating neurologically-impaired populations. Questions of driving capacity are anticipated to increase with rising numbers of older licensed drivers, and recommendations must strike a balance between protecting individual rights and public safety. Although impaired performance on tests of attention, processing speed, visuo-spatial perception, and executive functioning does not equate to driving incompetence, results of such testing should factor into driving recommendations. A variety of neuropsychological tests, including the NAB Driving Scenes, Useful Field of View Test, Complex Figure Test-Copy, Block Design, Grooved Pegboard Task, and Trailmaking A and B have demonstrated ecological validity by corresponding to performance on standardized road tests (Anderson et al., 2012; Brown et al., 2005; Classen et al., 2013; Dawson et al., 2010). Notably, use of demographically-adjusted norms may diminish accuracy in predicting driving ability with neuropsychological tests (Barrash et al., 2010). Brief screening measures of cognitive impairment demonstrate greater variability in predicting driving capacity, although a recent study indicates that the MoCA outperforms the MMSE in detection of on-road driving performance in cognitively impaired populations, with scores of 18 or lower suggesting the greatest risk of unsafe driving (Hollis et al., 2015). Neurobiological correlates have also demonstrated significant relationships with functional driving ability, including hippocampal volume and third ventricle size (Dehning et al., 2014; Griffith et al., 2013).
ABSTRACT: Neuropsychological Performance, Brain Imaging, and Driving Violations in Multiple Sclerosis. Dehning, M., Kim, J., Nguyen, C. M., Shivapour, E., & Denburg, N. L. (2014). Archives of Physical Medicine and Rehabilitation, 95(10), 1818-1823. http://www.ncbi.nlm.nih.gov/pubmed/24929025
Objective: To examine the relationship between third ventricular width, a measure of thalamic brain atrophy, and motor vehicle violation type and frequency in a cohort of patients with multiple sclerosis (MS). Design: Retrospective cohort study. Setting: Tertiary care university hospital. Participants: Thirty-five individuals with clinically confirmed relapsing-remitting multiple sclerosis and 35 age-, sex-, and education-matched community-dwelling healthy comparisons (NZ70). Participants were aged between 25 and 65 years. Interventions: Not applicable. Main Outcome Measures: Data on motor vehicle violations were obtained from an online database (Iowa Courts Online). The violations were categorized as follows: (1) speeding, (2) nonmoving safety, (3) administrative, (4) alcohol-related offense, (5) moving safety, and (6) total violations. Neuropsychological performance in all major cognitive domains was obtained. Thalamic atrophy for the patients with MS was determined via third ventricular width measurement. Results: The MS group had a greater number of overall violations, administrative violations, and nonmoving safety violations. The groups differed on neuropsychological tasks measuring visuospatial skills, speeded language, learning, and executive functioning, after controlling for affective symptoms. Third ventricular width was associated with total violations as well as moving safety violations. Finally, third ventricular width accounted for a significant variance in driving violation frequency above and beyond demographic variables and neuropsychological factors. Conclusions: There is an increased frequency of motor vehicle violations among patients with multiple sclerosis, and the number of violations can be predicted by thalamic brain atrophy.
PODCAST: Neurology Journal’s Segment on Evaluating Driving Risk in Dementia
Don Iverson, M.D., is interviewed in a 7-minute segment (Driving Risk in Dementia: 1:25-8:25) on guidelines for the evaluation and management of driving risk in dementia. https://www.aan.com/rss/search/home/episodedetail/?item=2061
1. Anderson, S. W., Aksan, N., Dawson, J. D., Uc, E. Y., Johnson, A. M., & Rizzo, M. (2012). Neuropsychological assessment of driving safety risk in older adults with and without neurologic disease. Journal of Clinical and Experimental Neuropsychology, 34(9), 895-905. http://www.ncbi.nlm.nih.gov/pubmed/22943767
2. Barrash, J., Stillman, A., Anderson, S. W., Uc, E. Y., Dawson, J. D., & Rizzo, M. (2010). Prediction of driving ability with neuropsychological tests: Demographic adjustments diminish accuracy. Journal of the International Neuropsychological Society, 16(04), 679-686. http://www.ncbi.nlm.nih.gov/pubmed/20441682
3. Brown, L. B., Stern, R. A., Cahn-Weiner, D. A., Rogers, B., Messer, M. A., Lannon, M. C., et al. (2005). Driving Scenes test of the Neuropsychological Assessment Battery (NAB) and on-road driving performance in aging and very mild dementia. Archives of Clinical Neuropsychology, 20(2), 209–215. http://www.ncbi.nlm.nih.gov/pubmed/15708731
4. Classen, S., Wang, Y., Crizzle, A. M., Winter, S. M., & Lanford, D. N. (2013). Predicting older driver on-road performance by means of the Useful Field of View and Trail Making Test Part B. American Journal of Occupational Therapy, 67(5), 574–582. http://0-www.ncbi.nlm.nih.gov.library.unl.edu/pmc/articles/PMC3750125/
5. Dawson, J. D., Uc, E. Y., Anderson, S. W., Johnson, A. M., & Rizzo, M. (2010). Neuropsychological predictors of driving errors in older adults. Journal of the American Geriatrics Society, 58(6), 1090-1096. http://www.ncbi.nlm.nih.gov/pubmed/20487082
6. Dehning, M., Kim, J., Nguyen, C. M., Shivapour, E., & Denburg, N. L. (2014). Neuropsychological performance, brain imaging, and driving violations in multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 95(10), 1818-1823. http://www.ncbi.nlm.nih.gov/pubmed/24929025
7. Griffith, H. R., Okonkwo, O. C., Stewart, C. C., Stoeckel, L. E., den Hollander, J. A., Elgin, J. M., ... & Wadley, V. G. (2013). Lower hippocampal volume predicts decrements in lane control among drivers with amnestic mild cognitive impairment. Journal of Geriatric Psychiatry and Neurology, 26(4), 259-266. http://www.ncbi.nlm.nih.gov/pubmed/24212246
8. Hollis, A. M., Duncanson, H., Kapust, L. R., Xi, P. M., & O'Connor, M. G. (2015). Validity of the Mini‐Mental State Examination and the Montreal Cognitive Assessment in the prediction of driving test outcome. Journal of the American Geriatrics Society, 63(5), 988-992. http://www.ncbi.nlm.nih.gov/pubmed/25940275
9. Iverson, D. J., Gronseth, G. S., Reger, M. A., Classen, S., Dubinsky, R. M., & Rizzo, M. (2010). Practice Parameter update: Evaluation and management of driving risk in dementia. Neurology, 74(16), 1316-1324. http://www.ncbi.nlm.nih.gov/pubmed/20385882
10. Perumparaichallai, R. K., Husk, K. L., Myles, S. M., & Klonoff, P. S. (2014). The relationship of neuropsychological variables to driving status following holistic neurorehabilitation. Frontiers in Neurology, 5(56), 1-7. http://www.ncbi.nlm.nih.gov/pubmed/24795693