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15
Framework for the statistical shape analysis of brain structures using spharmpdm
 In Insight Journal, Special Edition on the Open Science Workshop at MICCAI
, 2006
"... Abstract — Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical ..."
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Cited by 59 (7 self)
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Abstract — Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARMPDM). After alignment, differences between groups of surfaces are computed using the Hotelling T 2 two sample metric. Statistical pvalues, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the familywise error rate (FWER) or falsepositives via the extrema histogram of nonparametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the falsenegatives. I.
A comparison of random field theory and permutation methods for the statistical analysis of MEG data. NeuroImage
 Neuroimage
, 2005
"... We describe the use of random field and permutation methods to detect activation in cortically constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps eventrelated MEG to a coregistered cortical surface. These approaches also ex ..."
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Cited by 37 (7 self)
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We describe the use of random field and permutation methods to detect activation in cortically constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps eventrelated MEG to a coregistered cortical surface. These approaches also extend directly to images computed from eventrelated EEG data. We determine statistical thresholds that control the familywise error rate (FWER) across space or across both space and time. Both random field and permutation methods use the distribution of the maximum statistic under the null hypothesis to find FWER thresholds. The former methods make assumptions on the distribution and smoothness of the data and use approximate analytical solutions, the latter resample the data and rely on empirical distributions. Both methods account for spatial and temporal correlation in the cortical maps. Unlike previous nonparametric work in neuroimaging, we address the problem of nonuniform specificity that can arise without a Gaussianity assumption. We compare and evaluate the methods on simulated data and experimental data from a somatosensoryevoked response study. We find that the random field methods are conservative with or without smoothing, though with a 5 vertex FWHM smoothness, they are close to exact. Our permutation methods demonstrated exact specificity in simulation studies. In real data, the permutation method was not as sensitive as the RF method, although this could be due to violations of the random field theory assumptions.
Multistructure network shape analysis via normal Surface Momentum Maps
 NEUROIMAGE 42 (2008) 1430–1438
, 2008
"... ..."
Statistical SurfaceBased Morphometry Using A NonParametric Approach
 In: Int. Symposium on Biomedical Imaging(ISBI). In
, 2004
"... We present a novel method of statistical surfacebased morphometry based on the use of nonparametric permutation tests. In order to evaluate morphological differences of brain structures, we compare anatomical structures acquired at different times and/or from different subjects. Registration to a ..."
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Cited by 17 (6 self)
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We present a novel method of statistical surfacebased morphometry based on the use of nonparametric permutation tests. In order to evaluate morphological differences of brain structures, we compare anatomical structures acquired at different times and/or from different subjects. Registration to a common coordinate system establishes corresponding locations and the differences between such locations are modeled as a displacement vector field (DVF). The analysis of DVFs involves testing thousands of hypothesis for signs of statistically significant effects. We randomly permute the surface data among two groups to determine thresholds that control the familywise (type 1) error rate. These thresholds are based on the maximum distribution of the amplitude of the vector fields, which implicitly accounts for spatial correlation of the fields. We propose two normalization schemes for achieving uniform spatial sensitivity. We demonstrate their application in a shape similarity study of the lateral ventricles of monozygotic twins and nonrelated subjects.
Time sequence diffeomorphic metric mapping and parallel transport track . . .
 NEUROIMAGE 45 (2009) S51–S60
, 2009
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A statistical analysis of brain morphology using wild bootstrapping
 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53705, U.S.A. Email: mkchung@wisc.edu Department of Systems Engineering, Australian National University
, 2007
"... Methods for the analysis of brain morphology, including voxelbased morphology and surfacebased morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphom ..."
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Cited by 9 (4 self)
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Methods for the analysis of brain morphology, including voxelbased morphology and surfacebased morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild
Heat Kernel Smoothing of Anatomical Manifolds via
, 2010
"... We present a novel surface smoothing framework using the LaplaceBeltrami eigenfunctions. The Green’s function of an isotropic diffusion equation on a manifold is analytically represented using the eigenfunctions of the LaplaceBeltraimi operator. The Green’s function is then used in explicitly cons ..."
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Cited by 3 (3 self)
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We present a novel surface smoothing framework using the LaplaceBeltrami eigenfunctions. The Green’s function of an isotropic diffusion equation on a manifold is analytically represented using the eigenfunctions of the LaplaceBeltraimi operator. The Green’s function is then used in explicitly constructing heat kernel smoothing as a series expansion of the eigenfunctions. Unlike many previous surface diffusion approaches, diffusion is analytically represented using 1 the eigenfunctions reducing numerical inaccuracy. Our numerical implementation is validated against the spherical harmonic representation of heat kernel smoothing on a unit sphere. The proposed framework is illustrated with mandible surfaces, and is compared to a widely used iterative kernel smoothing method in computational anatomy. The MATLAB source code is freely available at
Statistical Group Differences in Anatomical Shape Analysis using Hotelling T2 metric
"... Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape anal ..."
Abstract
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Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARMPDM). After alignment, differences between groups of surfaces are computed using the Hotelling T 2 two sample metric. Statistical pvalues, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the familywise error rate (FWER) or falsepositives via the
1 2 ARTICLE IN PRESS
, 2004
"... 3 Mapping cortical change in Alzheimer’s disease, brain development, ..."