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Title: Normative brain mapping of 3-dimensional morphometry imaging data using skewed functional data analysis Authors:  Marco Palma - University of Cambridge (United Kingdom) [presenting]
Shahin Tavakoli - University of Geneva (Switzerland)
Julia Brettschneider - University of Warwick (United Kingdom)
Thomas Nichols - University of Oxford (United Kingdom)
Ana-Maria Staicu - North Carolina State University (United States)
Abstract: Tensor-based morphometry (TBM) aims at showing local differences in brain volumes with respect to a common template. TBM images are smooth, but they exhibit (especially in diseased groups) higher values in some brain regions called lateral ventricles. More specifically, a voxelwise analysis shows a mean-variance relationship in these areas and evidence of spatially dependent skewness, which can be missed in the standard functional data analysis (FDA) settings, which focus only on the first two functional moments. We propose a model for 3-dimensional functional data where mean, variance, and shape functions vary smoothly across brain locations. We model the voxelwise distributions as skew-normal. The smooth effects of age and sex are estimated on a reference population of cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and mapped across the whole brain. The parameter functions allow the transformation of each TBM image (in the reference population as well as in a test set) into a Gaussian process. These subject-specific normative maps are used to derive indices of deviation from a healthy condition which could help to assess the individual risk of pathological degeneration or to cluster different disease groups.