Shape Analysis with Overcomplete Spherical Wavelets

Yeo, B.T. Thomas; Yu, Peng; Grant, P. Ellen; Fischl, Bruce; Golland, Polina

doi:10.1007/978-3-540-85988-8_56

Cited by 21 publications

(36 citation statements)

References 17 publications

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“…After the transformation, each vertex has a set of wavelet coefficients representing its position in lower resolutions. The total number of vertices and the indices of vertices do not change during this decomposition process [7]. Thus, the correspondence of a vertex remains in all resolutions, which is illustrated by the same ROI colors in Fig.…”

Section: Multi-scale Surface Decompositionmentioning

confidence: 91%

“…Hence, we extract shape features for each ROI base on both folding patterns of multi-resolution surfaces and MNI atlas labels, and then assess the regularity and variability of these features. The cortical surface reconstructed from DTI data [10] is decomposed into multiresolutions by the spherical wavelet algorithm approach [7]. The volumetric working memory ROIs were obtained from task-based fMRI, and then are mapped onto the reconstructed surface, as well as other decomposed multi-resolution surfaces.…”

Section: Overviewmentioning

confidence: 99%

“…In this paper, the over-complete spherical wavelets algorithm [7] is applied to decompose the original cortical surface reconstructed from DTI data into a cascade of 7 lower resolutions (Fig. 2).…”

Section: Multi-scale Surface Decompositionmentioning

confidence: 99%

“…Another is to present the meso-scale folding pattern by using the recently published polynomial models [4]. Compared with other folding pattern descriptors [5,6,7], the advantages of the polynomial model not only lie in its meso-scale view which is more suitable for ROIs, but also its compactness for describing the symmetric shape patterns and its soundness for classifying the cortical shapes into eight primitive folding patterns [8]. To effectively extract folding pattern features for the ROIs, the cortical surface is decomposed into multiple resolutions [7] and attributes are generated on the multi-resolution surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with other folding pattern descriptors [5,6,7], the advantages of the polynomial model not only lie in its meso-scale view which is more suitable for ROIs, but also its compactness for describing the symmetric shape patterns and its soundness for classifying the cortical shapes into eight primitive folding patterns [8]. To effectively extract folding pattern features for the ROIs, the cortical surface is decomposed into multiple resolutions [7] and attributes are generated on the multi-resolution surfaces. In order to assess the regularity and variability of the shape attributes for each ROI in the working memory network, we performed a set of statistical and computational analysis for ROIs within a population.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessing Regularity and Variability of Cortical Folding Patterns of Working Memory ROIs

Chen

Zhang

et al. 2011

Lecture Notes in Computer Science

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Abstract. Cortical folding patterns are believed to be good predictors of brain cytoarchitecture and function. For instance, neuroscientists frequently apply their domain knowledge to identify brain Regions of Interests (ROIs) based on cortical folding patterns. However, quantitative mapping of cortical folding pattern and brain function has not been established yet in the literature. This paper presents our initial effort in quantification of the regularity and variability of cortical folding pattern features for working memory ROIs identified by taskbased fMRI, which is widely accepted as a standard approach to localize functionally-specialized brain regions. Specifically, we used a set of shape attributes for each ROI base on multiple resolution decomposition of cortical surfaces, and described the meso-scale folding pattern via a polynomial-based approach. We also applied brain atlas label distribution as a global-scale description of ROI folding pattern. Our studies suggest that there is deep-rooted regularity of cortical folding patterns for certain working memory ROIs across subjects, and folding pattern attributes could be useful for the characterization, recognition and prediction of ROIs, if extracted and applied in a proper way.

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Section: Multi-scale Surface Decompositionmentioning

confidence: 91%

Section: Overviewmentioning

confidence: 99%

Section: Multi-scale Surface Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessing Regularity and Variability of Cortical Folding Patterns of Working Memory ROIs

Chen

Zhang

et al. 2011

Lecture Notes in Computer Science

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Riemannian Statistical Analysis of Cortical Geometry with Robustness to Partial Homology and Misalignment

Awate

Leahy

Joshi

2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Typical studies of the geometry of the cerebral cortical structure focus on either cortical folding or thickness. They rely on spatial normalization, but use cortical descriptors that are sensitive to misregistration arising from the well-known problems of partial homologies between subject brains and local optima in nonlinear registration. In contrast to these approaches, we propose a novel framework for studying the geometry of the entire cortical sheet, subsuming its folding and thickness characteristics. We propose a novel descriptor of local cortical geometry to increase robustness to partial homology and misregistration. The proposed descriptor lies on a Riemannian manifold, and we describe a method for hypothesis testing on manifolds for cross-sectional studies. Results on simulated and clinical data show the benefits of the proposed approach for detecting between-group differences with greater accuracy and consistency.

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Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex

Awate

Leahy

Joshi

2017

Lecture Notes in Computer Science

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Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based descriptor lies on a Riemannian manifold. We propose new regularized nonlinear methods for (i) detecting group differences, using a Mercer kernel with an implicit lifting map to a reproducing kernel Hilbert space, and (ii) regression against clinical variables, using kernel density estimation. For both methods, we employ kernels that exploit the Riemannian structure. Results on simulated and clinical data shows the improved accuracy and stability of our approach in cortical-sheet analysis.

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Shape Analysis with Overcomplete Spherical Wavelets

Cited by 21 publications

References 17 publications

Assessing Regularity and Variability of Cortical Folding Patterns of Working Memory ROIs

Assessing Regularity and Variability of Cortical Folding Patterns of Working Memory ROIs

Riemannian Statistical Analysis of Cortical Geometry with Robustness to Partial Homology and Misalignment

Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex

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