Normative brain mapping of 3-dimensional morphometry imaging data using skewed functional data analysis

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, our voxelwise analysis shows b...

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Bibliographic Details
Published inarXiv.org
Main Authors Palma, Marco, Tavakoli, Shahin, Brettschneider, Julia, Ana-Maria Staicu, Nichols, Thomas E
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.07.2024
Subjects
Age factors
Alzheimer's disease
Brain
Data analysis
Degeneration
Functionals
Gaussian process
Medical imaging
Skewed distributions
Skewness
Tensors
Variance
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Summary: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, our voxelwise analysis shows both a mean-variance relationship in these areas and evidence of spatially dependent skewness. We propose a model for 3-dimensional functional data where mean, variance, and skewness 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 three parameter functions allow to transform 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 to assess the individual risk of pathological degeneration.
ISSN:2331-8422