UCSF Pediatric Stroke Research Center

          In Project 2 we will assess the utility of advanced functional and structural imaging to predict outcomes after hemorrhagic stroke in pediatric patients. Currently our ability to predict recovery after hemorrhagic stroke is limited to interpreting standard anatomic imaging in the context of expected functional localization derived from population level averages of neurotypical individuals. This has significant implications when treating patients on an individual basis, as opposed to population level average of typically developed adults. Therefore, we will take a novel approach known as lesion network mapping to analyze patient specific connectomics to test our ability to predict outcomes in pediatric hemorrhagic stroke.

          We will use resting state functional MRI (rs-fMRI) to study the organization of resting state networks (RSNs) as well as diffusion tensor imaging (DTI) to perform tractography studies of white matter connections between brain regions. RSNs will be the basis of a functional connectome and tractography will be the basis of a structural connectome. These imaging modalities have the unique advantage of being easily obtained alongside routine clinical care resulting in absolute minimal burden to the patient. We will then repeat these measures at serial timepoints throughout recovery, beginning with initial presentation and following along standard clinical follow up. The patient specific connectome will provide a metric with which we will correlate neurologic deficits. We anticipate this will provide greater precision and more personalized care beyond the current standard using anatomic studies alone and inferences based on population level averages. We then will evaluate changes in the functional and structural connectome by analyzing trends in the strengths of these connections over time and in correlation with changes in standardized outcomes metrics. The imaging timepoints will be synergistic with the those in Project 1 and the outcomes metrics synergistic with those in Project 3. The strength of personalized connectomes and within subject comparisons over time will provide a tremendous opportunity to improve the quality of care to this unique patient population. We anticipate these data will provide a strong basis to improve our ability to predict recovery, anticipate sustained deficits, and guide specific therapy based on the needs as predicted by personalized functional and structural connectomes.