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.