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Generalized TensorBased Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors
"... Abstract—This paper investigates the performance of a new multivariate method for tensorbased morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical temp ..."
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Cited by 44 (10 self)
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Abstract—This paper investigates the performance of a new multivariate method for tensorbased morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling’s 2 test to them, in a LogEuclidean domain. In 2D and 3D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensorderived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulativevalue plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and largescale studies of disease that use tensorbased morphometry. Index Terms—Brain, image analysis, Lie groups, magnetic resonance imaging (MRI), statistics. I.
Atypical frontalposterior synchronization of Theory of Mind regions in autism during mental state attribution
"... This study used fMRI to investigate the functioning of the Theory of Mind (ToM) cortical network in autism during the viewing of animations that in some conditions entailed the attribution of a mental state to animated geometric figures. At the cortical level, mentalizing (attribution of metal state ..."
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Cited by 36 (10 self)
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This study used fMRI to investigate the functioning of the Theory of Mind (ToM) cortical network in autism during the viewing of animations that in some conditions entailed the attribution of a mental state to animated geometric figures. At the cortical level, mentalizing (attribution of metal states) is underpinned by the coordination and integration of the components of the ToM network, which include the medial frontal gyrus, the anterior paracingulate, and the right temporoparietal junction. The pivotal new finding was a functional underconnectivity (a lower degree of synchronization) in autism, especially in the connections between frontal and posterior areas during the attribution of mental states. In addition, the frontal ToM regions activated less in participants with autism relative to control participants. In the autism group, an independent psychometric assessment of ToM ability and the activation in the right temporoparietal junction were reliably correlated. The results together provide new evidence for the biological basis of atypical processing of ToM in autism, implicating the underconnectivity between frontal regions and more posterior areas.
Smoothing and cluster thresholding for cortical surfacebased group analysis of fMRI data.
 Neuroimage
, 2006
"... Cortical surfacebased analysis of fMRI data has proven to be a useful method with several advantages over 3dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surfacebased analyses. Operating within the framework of the FreeSurfer soft ..."
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Cited by 30 (0 self)
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Cortical surfacebased analysis of fMRI data has proven to be a useful method with several advantages over 3dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surfacebased analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surfacebased version of the cluster size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise data sets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surfacebased smoothing simulations and found a large disparity between the expected and actual kernel widths.
Spherical Demons: Fast Diffeomorphic LandmarkFree Surface Registration
 IEEE TRANSACTIONS ON MEDICAL IMAGING. 29(3):650–668, 2010
, 2010
"... We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. B ..."
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Cited by 25 (5 self)
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We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160k nodes requires less than 5 minutes when warping the atlas and less than 3 minutes when warping the subject on a Xeon 3.2GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmarkfree surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image: (1) parcellation of invivo cortical surfaces and (2) Brodmann area localization in exvivo cortical surfaces.
Tensorbased cortical surface morphometry via weighted spherical harmonic representation
 IEEE Transactions on Medical Imaging
, 2008
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Weighted Fourier series representation and its application to quantifying the amount of gray matter
 Special Issue of IEEE Transactions on Medical Imaging, on Computational Neuroanatomy
, 2007
"... representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a ..."
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Cited by 14 (4 self)
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representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a selfadjoint partial differential equation and the traditional spherical harmonic (SPHARM) representation. To reduce steep computational requirements, a new iterative residual fitting (IRF) algorithm is developed. Its computational and numerical implementation issues are discussed in detail. The computer codes are also available at
Encoding Cortical Surface by Spherical Harmonics
"... Abstract: There is a lack of unified statistical modeling framework for cerebral shape asymmetry analysis in literature. Most previous approaches start with flipping the 3D magnetic resonance images (MRI). The anatomical correspondence across the hemispheres is then established by registering the o ..."
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Cited by 13 (7 self)
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Abstract: There is a lack of unified statistical modeling framework for cerebral shape asymmetry analysis in literature. Most previous approaches start with flipping the 3D magnetic resonance images (MRI). The anatomical correspondence across the hemispheres is then established by registering the original image to the flipped image. A difference of an anatomical index between these two images is used as a measure of cerebral asymmetry. We present a radically different asymmetry analysis that utilizes a novel weighted spherical harmonic representation of cortical surfaces. The weighted spherical harmonic representation is a surface smoothing technique given explicitly as a weighted linear combination of spherical harmonics. This new representation is used to parameterize cortical surfaces, establish the hemispheric correspondence, and normalize cortical surfaces in a unified mathematical framework. The methodology has been applied in characterizing the cortical asymmetry of a group of autistic subjects.
General multivariate linear modeling of surface shapes using SurfStat,” NeuroImage 53(2
, 2010
"... General multivariate linear modeling of surface ..."
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A comparison of volumebased and surfacebased multivoxel pattern analysis.
 Neuroimage.
, 2011
"... For functional magnetic resonance imaging (fMRI), multivoxel pattern analysis (MVPA) has been shown to be a sensitive method to detect areas that encode certain stimulus dimensions. By moving a searchlight through the volume of the brain, one can continuously map the information content about the ..."
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Cited by 12 (6 self)
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For functional magnetic resonance imaging (fMRI), multivoxel pattern analysis (MVPA) has been shown to be a sensitive method to detect areas that encode certain stimulus dimensions. By moving a searchlight through the volume of the brain, one can continuously map the information content about the experimental conditions of interest to the brain. Traditionally, the searchlight is defined as a volume sphere that does not take into account the anatomy of the cortical surface. Here we present a method that uses a cortical surface reconstruction to guide voxel selection for information mapping. This approach differs in two important aspects from a volumebased searchlight definition. First, it uses only voxels that are classified as grey matter based on an anatomical scan. Second, it uses a surfacebased geodesic distance metric to define neighbourhoods of voxels, and does not select voxels across a sulcus. We study here the influence of these two factors onto classification accuracy and onto the spatial specificity of the resulting information map. In our example data set, participants pressed one of four fingers while undergoing fMRI. We used MVPA to identify regions in which local fMRI patterns can successfully discriminate which finger was moved. We show that surfacebased information mapping is a more sensitive measure of local information content, and provides better spatial selectivity. This makes surfacebased information mapping a useful technique for a datadriven analysis of information representation in the cerebral cortex.