While survival rates for premature infants have improved steadily over the last decade, the incidence of adverse neurodevelopmental outcomes has remained essentially unchanged. Approximately 50% of the half million very low birth-weight infants born each year in the United States will face motor, cognitive, and/or behavioral challenges. The principal neuropathology associated with prematurity occurs in the cerebral white matter (WM), with secondary impact on the developing cerebral cortex. The mortality rate in this cohort is very low, limiting the amount of pathological material available for study. Thus, methods for quantitative evaluation of cerebral WM in preterm infants are urgently required. Such methods could be used to define normal WM development, which would allow the monitoring of neonatal interventions aimed at optimizing cerebral development as well as identifying infants at risk for later cognitive impairment. MR diffusion measurements can provide information on WM microstructure and on neuronal fiber tracts. At present, it is not clear which parameters are the best indicators of white matter integrity or quality. Similarly, there is no consensus on the best means by which to identify or follow WM tracts. Currently, the diffusion tensor model is the most commonly used and is usually separately applied to individual voxels. White matter fiber bundles in the brain extend over many voxels and could be better modeled with extension of the diffusion tensor model to include local connectivity with neighboring voxels. Bayesian probability theory provides us with the tools for optimal model selection and parameter estimation that can better evaluate WM connectivity and provide a consistent probability theory basis for neuronal fiber tracts and their evaluation. The candidate's long-term goal is to develop diffusion MR imaging methods to provide an accurate evaluation of WM development and maturation using Bayesian probability theory. The central hypothesis is that Bayesian probability theory will provide a means for optimal parameter estimation that will provide accurate information on the status of WM connectivity. The objective in this application is to develop the software tools needed for Bayesian based analysis and to apply it initially to simulated data, followed by application to normal ex vivo baboon brain, followed by a study of normal human infants. The study will conclude with an evaluation of WM injury in ex vivo baboon brains with histological correlates. Bayesian probability theory has not been applied to the evaluation of WM development, and the candidate is able to compare human neonate results with normal and abnormal ex vivo baboon brains, which is a well-established model for human brain maturation.
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