Impact of Left Hemisphere Stroke on Cognitive Functioning and Implications for Driving

中风后的慢性残疾是退伍军人的一个重要问题，可能会影响各种日常活动。 临床评估和治疗计划中的一个重要领域是中风对驾驶安全的影响。 不幸的是，对驾驶健康和相关认知缺陷的正式评估（例如，视觉空间变化） 通常不会在左半球中风患者中进行。目前的项目将通过评估 左半球中风患者在一个国家的最先进的驾驶模拟器，比较他们的表现， 右半球中风患者和健康对照。模拟器评估的驾驶变量包括 碰撞次数、不安全的车道交叉、速度偏差和停车反应时间。不同类型 的驾驶错误，然后将与性能的神经心理电池，其中包括标准化 以及视觉空间能力、执行功能和语言的实验测量。最后，我们将调查 使用基于体素的损伤症状映射的不同类型的驾驶错误的神经相关性， 结构性病变对行为表现的影响。 本研究的参与者包括40名左半球和40名右半球的退伍军人中风患者， 神经系统或严重精神病史。对照组为20名年龄和教育程度相匹配的退伍军人， 神经或精神病史将为数据的解释提供额外的背景。驾驶性能 将使用最先进的驾驶模拟器进行检查，该模拟器已被证明具有强大的生态 有效性和预测有效性方面的道路驾驶健身。据预测，60-70%的左半球 患者将表现出受损的驾驶性能（“失败”或“需要培训”评级）， 健康对照组，但与右半球患者无显著差异。然而，据估计， 半球患者将显示出与右半球患者不同的表现模式。例如它 预计LH患者在复杂的驾驶场景下将表现出不成比例的更多错误（例如，在 施工区），而两个中风组将表现出显着的视觉空间驾驶错误（不安全的车道 变化和碰撞）。然后使用偏最小二乘回归来确定 神经心理学的措施是最密切相关的驾驶性能变量测量中， 模拟器据预测，左半球患者的驾驶错误将与视觉空间 措施（例如，有用的视野/视觉搜索）和执行功能测量（例如，轨迹B）。我们也 建议将来自高分辨率3 T MRI扫描的结构性病变数据与不同类型的驾驶错误联系起来。 该项目的这一方面将利用我们小组帮助的基于体素的病变症状映射（VLSM）软件 开发.据预测，与视觉空间注意力不集中相关的驾驶错误将与病变相关， 背侧额顶叶网络，而复杂驾驶场景中的驾驶错误将额外相关 包括左下顶叶和前额叶皮层在内的更腹侧的网络受到损伤。 总之，我们的研究结果将为驾驶安全的重要性提供关键信息 卒中后的转诊和评估，特别是左半球卒中患者。这项研究将推动 通过识别神经心理学测试表现和病变位置， 对中风后驾驶安全的额外关注，以及识别左和右之间的差异 半球中风患者。我们希望这项研究的结果不仅能告知临床医生， 患者的潜在驾驶风险，但最终将提供所需的数据类型，以支持个性化 在模拟驾驶环境中为希望重新驾驶的退伍军人提供培训计划。

Chronic disability following stroke is a significant problem for Veterans that can affect a variety of daily activities. One area of importance in clinical assessment and treatment planning is the impact of stroke on driving safety. Unfortunately, formal assessments of driving fitness and related cognitive deficits (e.g., visuospatial changes) are often not conducted in left hemisphere stroke patients. The current project will address this gap by evaluating left hemisphere stroke patients on a state-of-the-art driving simulator, comparing their performance to that of both right hemisphere stroke patients and healthy controls. Driving variables assessed by the simulator include the number of collisions, unsafe lane crossings, speed exceedances, and reaction time to stop. Different types of driving errors will then be related to performance on a neuropsychological battery, which includes standardized and experimental measures of visuospatial ability, executive functioning, and language. Last, we will investigate the neural correlates of distinct types of driving errors using voxel-based lesion symptom mapping, which relates structural lesions to behavioral performance on a voxel-by-voxel basis. Participants for the current study include 40 left and 40 right hemisphere Veteran stroke patients with no prior neurologic or severe psychiatric history. A control group of 20 age- and education-matched Veterans with no neurologic or psychiatric history will provide additional context for interpretation of the data. Driving performance will be examined with a state-of-the-art driving simulator that has been demonstrated to have strong ecological validity and predictive validity with respect to on-road driving fitness. It is predicted that 60-70% of left hemisphere patients will exhibit impaired driving performance (“failed” or “needs training” rating), significantly higher than healthy controls but not significantly different from right hemisphere patients. It is expected, however, that left hemisphere patients will show a distinct pattern of performance from right hemisphere patients. For example, it is expected that LH patients will exhibit disproportionately more errors under complex driving scenarios (e.g., in a construction zone), whereas both stroke groups will exhibit significant visuospatial driving errors (unsafe lane changes and collisions) relative to controls. Partial least squares regression will then be used to identify which neuropsychological measures are most closely related to driving performance variables measured in the simulator. It is predicted that driving errors in left hemisphere patients will correlate with both visuospatial measures (e.g., Useful Field of View/Visual Search) and executive functioning measures (e.g., Trails B). We also propose to relate structural lesion data from high-resolution 3T MRI scans to different types of driving errors. This aspect of the project will utilize voxel-based lesion symptom mapping (VLSM) software that our group helped develop. It is predicted that driving errors related to visuospatial inattention will be associated with lesions to a dorsal fronto-parietal network, whereas driving errors in complex driving scenarios will be additionally associated with lesions to a more ventral network that includes left inferior parietal and prefrontal cortex. In summary, the findings from our study will provide critical information as to the importance of driving safety referrals and evaluations following stroke, particularly in left hemisphere stroke patients. This study will advance the field by identifying neuropsychological test performance and lesion locations that should be flags for additional concern about driving safety following stroke, as well as identifying differences among left and right hemisphere stroke patients. It is our hope that the findings from this study will not only inform clinicians and patients of potential driving risks, but will ultimately provide the type of data needed to support individualized training programs in simulated driving environments for Veterans who wish to return to driving.