A generalization of voxel-wise procedures for highdimensional statistical inference using ridge regression

Karl Sjöstrand, Valerie A. Cardenas, Rasmus Larsen, Colin Studholme

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    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.
    Original languageEnglish
    Title of host publicationProceedings of the SPIE : Medical Imaging 2008: Image Processing
    Number of pages12
    Volume6914
    PublisherSPIE - International Society for Optical Engineering
    Publication date2008
    Pages69140A
    DOIs
    Publication statusPublished - 2008
    EventSPIE Medical Imaging 2008: Image Processing - Miami, Florida, US
    Duration: 1 Jan 2008 → …

    Conference

    ConferenceSPIE Medical Imaging 2008: Image Processing
    CityMiami, Florida, US
    Period01/01/2008 → …

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