Predicting Cognitive Outcomes from Stroke Based on Lesion Location

摘要 中风对病人和他们所爱的人来说都是一种可怕的经历。焦虑的主要来源 是不知道什么样的中风相关的缺陷将持续-我爱的人永远能够再次说话吗？的 康复的可能性由治疗神经科医生或康复专家根据他们的 个人经历没有可用的工具可以使用关于中风位置的信息， 将其与数百或数千个其他个体的结果进行查询，并生成个性化的 定量预测卒中相关缺陷和长期恢复的预后。在此，我们建议 开发这样的工具。在爱荷华州大学，我们有一个最全面的病变登记处， 全球约3，500例局灶性获得性脑损伤患者，神经影像学和广泛的结局数据。 我们建议利用这一独特的资源，开发一种工具来预测认知结果， 根据病变位置确定卒中。首先，我们建议绘制与特定认知功能最相关的大脑区域， 在1000多名患者中发现了一些缺陷，包括说话困难或注意力问题等症状。 接下来，在一个前瞻性收集的急性缺血性卒中队列中，我们将试图预测认知功能， 通过针对上述症状“地图”查询病变位置来确定结果。我们假设病变 位置将是慢性认知结果的重要预测因素。第二，我们最近开发了一个 创新的策略，将病变相关的缺陷与特定的大脑网络联系起来。它结合了传统的病变 映射来自健康成年人的人类连接体数据，以推断哪些网络被局灶性脑破坏 病变我们将评估这种病变网络标测方法是否可用于补充 传统的病变映射方法来预测认知结果的额外变化。最后，我们将使用 先进的统计建模，以评估如何从病变位置的预测信息， 与人口统计学信息和基线筛查认知表现相结合， 在纵向队列中预测慢性认知结果。通过实现这些目标，我们将 开发可应用于临床获得的MRI扫描的临床工具以帮助 确定认知结果的预后，这是有助于早期管理的关键因素， 中风患者的康复和生活规划。

Abstract Having a stroke is a frightening experience for both the patient and their loved ones. A major source of anxiety is not knowing what stroke-related deficits will persist – will my loved one ever be able to talk again? The likelihood for recovery is estimated by the treating neurologist or rehabilitation specialist based on their personal experience. There are no tools available that can use information about the location of the stroke, query it against the outcomes from hundreds or thousands of other individuals, and generate a personalized quantitative prediction of stroke-related deficits and prognosis for long-term recovery. Here, we propose to develop such a tool. At the University of Iowa we have one of the most comprehensive lesion registries in the world of some 3,500 patients with focal acquired brain lesions, neuroimaging, and extensive data on outcomes. We propose to capitalize on this unique resource by developing a tool to predict cognitive outcomes from stroke based on lesion location. First, we propose to map brain regions most associated with specific cognitive deficits across over 1000 patients, including symptoms such as difficulty speaking or problems with attention. Next, in a prospectively collected cohort with acute ischemic stroke we will attempt to predict cognitive outcomes by querying lesion location against the aforementioned symptom ‘maps.’ We hypothesize that lesion location will be a significant predictor of chronic cognitive outcomes. Second, we have recently developed an innovative strategy that links lesion-associated deficits to specific brain networks. It combines traditional lesion mapping with human connectome data from healthy adults to infer what networks are disrupted by focal brain lesions. We will evaluate whether this lesion network mapping approach can be used to compliment the traditional lesion mapping approach to predict additional variance in cognitive outcomes. Finally, we will use advanced statistical modeling to evaluate how predictive information from lesion location can be optimally integrated with demographic information and baseline screening cognitive performance to maximize predictions of chronic cognitive outcomes in a longitudinal cohort. By addressing these objectives, we will lay the foundation for developing a clinical tool that can be applied to a clinically-acquired MRI scan to aid in determining the prognosis of cognitive outcomes, a key factor that will help in the early management, rehabilitation, and life planning for patients with stroke.