Article

Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures

University of Utah, Salt Lake City, UT, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 09/2010; 13(Pt 3):529-37. DOI: 10.1007/978-3-642-15711-0_66
Source: DBLP

ABSTRACT

The purpose of this study is to characterize the neuroanatomical variations observed in neurological disorders such as dementia. We do a global statistical analysis of brain anatomy and identify the relevant shape deformation patterns that explain corresponding variations in clinical neuropsychological measures. The motivation is to model the inherent relation between anatomical shape and clinical measures and evaluate its statistical significance. We use Partial Least Squares for the multivariate statistical analysis of the deformation momenta under the Large Deformation Diffeomorphic framework. The statistical methodology extracts pertinent directions in the momenta space and the clinical response space in terms of latent variables. We report the results of this analysis on 313 subjects from the Mild Cognitive Impairment group in the Alzheimer's Disease Neuroimaging Initiative (ADNI).

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Available from: Sarang C Joshi, Jan 06, 2014
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    • "This measure is similar to the logarithms of the Jacobian determinants of the deformations . The second measure was the " scalar momentum " (Singh et al, 2010), which was spatially smoothed by convolving with a Gaussian of 10 mm full width at half maximum . These particular features were chosen because we had previously examined the effectiveness of a number of features (including Jacobian determinants, rigidly aligned GM, spatially normalised GM and Jacobian scaled spatially normalised GM) derived from the same IXI dataset (unpublished work). "
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    ABSTRACT: In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The "Pattern Recognition for Neuroimaging Toolbox" (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.
    Full-text · Article · Feb 2013 · Neuroinformatics
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    • "This measure is similar to the logarithms of the Jacobian determinants of the deformations . The second measure was the " scalar momentum " (Singh et al, 2010), which was spatially smoothed by convolving with a Gaussian of 10 mm full width at half maximum . These particular features were chosen because we had previously examined the effectiveness of a number of features (including Jacobian determinants, rigidly aligned GM, spatially normalised GM and Jacobian scaled spatially normalised GM) derived from the same IXI dataset (unpublished work). "

    Full-text · Article · Feb 2013 · Neuroinformatics
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    • "This work is supported in part by NSF awards OCI-0906379 and OCI-0904631 and DOE awards DOE/NEUP 120341, DOE/MAPD DE-SC000192, DOE/LLNL B597476, DOE/Codesign P01180734, and DOE/SciDAC DE-SC0007446. J. Samuel Preston and Sarang Joshi provided access to data [14]. LLNL-PROC-576992. "

    Full-text · Conference Paper · Jan 2012
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