Longitudinal Analysis for Early Diagnosis of Alzheimer’s Disease
Dr. Guorong Wu
This proposal aims for the development of novel neuroimaging analysis methods for early diagnosis of Alzheimer’s disease using longitudinal image data. In particular, a set of automated algorithms will be created to aid the difficult task of analyzing subtle and complex morphological change patterns of Alzheimer’s disease during the disease progression. The successful completion of this project will represent an import milestone in neuroimaging research by bringing forth the fruition of a powerful and practical system for early diagnosis of Alzheimer’s disease at individual level, for potential effective treatment.
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Dr. Gang Li was awarded a K01 grant on August 2015 by the National Institute of Health to receive training in neurodevelopment and create infant-specific neuroimaging analysis tools for accurate characterization of early brain development in both typically developing infants and infants at high-risk.
Schizophrenia is a debilitating mental disorder with early neurodevelopmental origins. Genetic high-risk infants born to schizophrenic mothers are ideal candidates for improving our understanding of developmental origins and abnormal trajectories in schizophrenia. The University of North Carolina at Chapel Hill has collected a unique cohort of longitudinal MRI dataset of typically developing infants and also infants at high-risk for schizophrenia in their first two years of life, which allows us to track dynamic developmental trajectories of the cortex in both typical and high-risk infants during this critical stage. Cortical surface-based analysis of neuroimaging data is playing an increasingly critical role in adult schizophrenia studies, and has revealed widespread structural and functional abnormalities. However, existing cortical surface-based analysis tools developed for adult brains are ill-suited for infant studies, due to their dramatic differences in image contrast, cortical size, shape, and folding degree. Moreover, independent processing of image for each time-point in the longitudinal infant studies leads to temporally inconsistent and inaccurate measurements. To become an independent investigator on pediatric neuroimaging research, the candidate proposes in this K01 application to receive training in developmental neurobiology and neurodevelopmental disorders, advanced biostatistics, and infant MR imaging techniques. These training activities will greatly augment the candidate's background in infant neuroimaging mapping and establish a solid foundation for his long-term goal of being a leading researcher on early brain development study. In the research plan, the candidate will create a unique suite of infant-specific, 4D cortical surface based neuroimaging analysis tools that enable accurate characterization of early brain development in both typically developing infants and infants at high-risk for schizophrenia. These computational tools will lead to the significantly improved understanding of the dynamic cortex development in typical infants, and also abnormal developmental trajectories in infants at high-risk for schizophrenia and other neurodevelopmental disorders.