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

TL;DR

This paper introduces a novel 3D functional data model using skew-normal distributions to analyze brain morphometry, capturing mean, variance, and skewness variations for improved normative mapping and disease risk assessment.

Contribution

It proposes a new skew-normal based model for 3D brain data that accounts for skewness and spatial dependence, enhancing normative mapping in neuroimaging.

Findings

Model effectively captures skewness in brain regions like ventricles.

Normative maps enable assessment of individual deviation from healthy brain structure.

Application to ADNI data demonstrates potential for early disease risk detection.

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, 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.