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The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve a better prediction, we not only delineate spatial association between neuroimaging data and(More)
Longitudinal neuroimaging data plays an important role in mapping the neural developmental profile of major neuropsychiatric and neurodegenerative disorders and normal brain. The development of such developmental maps is critical for the prevention, diagnosis, and treatment of many brain-related diseases. The aim of this paper is to develop a(More)
Background. Treatment of pediatric medulloblastoma is associated with known neurocognitive deficits that we hypothesize are caused by microstructural damage to frontal white matter (WM). Methods. Longitudinal MRI examinations were collected from baseline (after surgery but before therapy) to 36 months in 146 patients and at 3 time points in 72 controls.(More)
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