title: Quantifying anatomical shape variations in neurological disorders.
abstract: We develop a multivariate analysis of brain anatomy to identify the relevant shape deformation patterns and quantify the shape changes that explain corresponding variations in clinical neuropsychological measures. We use kernel Partial Least Squares (PLS) and formulate a regression model in the tangent space of the manifold of diffeomorphisms characterized by deformation momenta. The scalar deformation momenta completely encode the diffeomorphic changes in anatomical shape. In this model, the clinical measures are the response variables, while the anatomical variability is treated as the independent variable. To better understand the "shape-clinical response" relationship, we also control for demographic confounders, such as age, gender, and years of education in our regression model. We evaluate the proposed methodology on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline structural MR imaging data and neuropsychological evaluation test scores. We demonstrate the ability of our model to quantify the anatomical deformations in units of clinical response. Our results also demonstrate that the proposed method is generic and generates reliable shape deformations both in terms of the extracted patterns and the amount of shape changes. We found that while the hippocampus and amygdala emerge as mainly responsible for changes in test scores for global measures of dementia and memory function, they are not a determinant factor for executive function. Another critical finding was the appearance of thalamus and putamen as most important regions that relate to executive function. These resulting anatomical regions were consistent with very high confidence irrespective of the size of the population used in the study. This data-driven global analysis of brain anatomy was able to reach similar conclusions as other studies in Alzheimer's disease based on predefined ROIs, together with the identification of other new patterns of deformation. The proposed methodology thus holds promise for discovering new patterns of shape changes in the human brain that could add to our understanding of disease progression in neurological disorders.
authors.affiliation: University of Utah, UT, United States. Electronic address: nikhil@cs.utah.edu.
authors.forename: Nikhil
authors.initials: N
authors.lastname: Singh
authors.affiliation: University of Utah, UT, United States.
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher
authors.affiliation: University of Utah, UT, United States.
authors.forename: J Samuel
authors.initials: JS
authors.lastname: Preston
authors.affiliation: University of Utah, UT, United States.
authors.forename: Richard D
authors.initials: RD
authors.lastname: King
authors.affiliation: University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
authors.forename: J S
authors.initials: JS
authors.lastname: Marron
authors.affiliation: University of California, San Francisco, CA, United States.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.affiliation: University of Utah, UT, United States.
authors.forename: Sarang
authors.initials: S
authors.lastname: Joshi
mesh_terms: D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Alzheimer’s disease; Computational anatomy; Deformation momenta; Kernel Partial Least Squares (PLS); Prediction