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26 March 2008 A generalization of voxel-wise procedures for high-dimensional statistical inference using ridge regression
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Abstract
Whole-brain morphometry denotes a group of methods with the aim of relating clinical and cognitive measurements to regions of the brain. Typically, such methods require the statistical analysis of a data set with many variables (voxels and exogenous variables) paired with few observations (subjects). A common approach to this ill-posed problem is to analyze each spatial variable separately, dividing the analysis into manageable subproblems. A disadvantage of this method is that the correlation structure of the spatial variables is not taken into account. This paper investigates the use of ridge regression to address this issue, allowing for a gradual introduction of correlation information into the model. We make the connections between ridge regression and voxel-wise procedures explicit and discuss relations to other statistical methods. Results are given on an in-vivo data set of deformation based morphometry from a study of cognitive decline in an elderly population.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Karl Sjöstrand, Valerie A. Cardenas, Rasmus Larsen, and Colin Studholme "A generalization of voxel-wise procedures for high-dimensional statistical inference using ridge regression", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140A (26 March 2008); https://doi.org/10.1117/12.770728
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