Parametric Representation of Cortical Surface Folding Based on Polynomials

Zhang, Tuo; Li, Gang; Nie, Jingxin; Liu, Tianming

doi:10.1007/978-3-642-04271-3_23

Cited by 13 publications

(20 citation statements)

References 15 publications

(15 reference statements)

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“…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. Then, the folding patterns of the ROIs are computed by the polynomial models [4], and the feature vectors of each ROI is then obtained and analyzed via the methods illustrated in Fig. 1 separately.…”

Section: Overviewmentioning

confidence: 99%

“…To compute the folding pattern of a surface patch, a parametric folding descriptor using polynomials [4] is applied:…”

Section: Meso-scale Attributes Based On Polynomial Folding Descriptorsmentioning

confidence: 99%

“…where, a and b describe the mirror symmetric components of the patch along x axis and y axis respectively, while c and d represent the rotational symmetric components [4]. Then, a model-driven method similar to that in [4] is applied to classify the combination of the symmetric components into eight primitive patterns of peak, pit, ridge, valley, saddle ridge, saddle valley, flat, and inflection [8].…”

Section: Meso-scale Attributes Based On Polynomial Folding Descriptorsmentioning

confidence: 99%

“…Then, a model-driven method similar to that in [4] is applied to classify the combination of the symmetric components into eight primitive patterns of peak, pit, ridge, valley, saddle ridge, saddle valley, flat, and inflection [8]. The attribute vector of ROI on each surface resolution is defined as the normalized histogram of eight patterns within ROI.…”

Section: Meso-scale Attributes Based On Polynomial Folding Descriptorsmentioning

confidence: 99%

“…One is to present the global-scale folding pattern by using the widely used MNI (Montreal Neurological Institute) atlas labels. 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].…”

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: Overviewmentioning

confidence: 99%

“…To compute the folding pattern of a surface patch, a parametric folding descriptor using polynomials [4] is applied:…”

Section: Meso-scale Attributes Based On Polynomial Folding Descriptorsmentioning

confidence: 99%

Section: Meso-scale Attributes Based On Polynomial Folding Descriptorsmentioning

confidence: 99%

Section: Meso-scale Attributes Based On Polynomial Folding Descriptorsmentioning

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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A Dynamic Skull Model for Simulation of Cerebral Cortex Folding

Chen

Nie

Zhang

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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The mechanisms of human cerebral cortex folding and their interactions during brain development are largely unknown, partly due to the difficulties in biological experiments and data acquisition for the developing fetus brain. Computational modeling and simulation provide a novel approach to the understanding of cortex folding processes in normal or aberrant neurodevelopment. Based on our recently developed computational model of the cerebral cortex folding using neuronal growth model and mechanical skull constraint, this paper presents a computational dynamic model of the brain skull that regulates the cortical folding simulation. Our simulation results show that the dynamic skull model is more biologically realistic and significantly improves our cortical folding simulation results. This work provides further computational support to the hypothesis that skull is an important regulator of cortical folding.

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A few thoughts on brain ROIs

Liu

2011

Brain Imaging and Behavior

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Quantitative mapping of structural and functional connectivities in the human brain via non-invasive neuroimaging offers an exciting and unique opportunity to understand brain architecture. Because connectivity alterations are widely reported in a variety of brain diseases, assessment of structural and functional connectivities has emerged as a fundamental research area in clinical neuroscience. A fundamental question arises when attempting to map structural and functional connectivities: how to define and localize the best possible Regions of Interests (ROIs) for brain connectivity mapping? Essentially, when mapping brain connectivities, ROIs provide the structural substrates for measuring connectivities within individual brains and for pooling data across populations. Thus, identification of reliable, reproducible and accurate ROIs is critically important for the success of brain connectivity mapping. This paper discusses several major challenges in defining optimal brain ROIs from our perspective and presents a few thoughts on how to deal with those challenges based on recent research work done in our group.

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Parametric Representation of Cortical Surface Folding Based on Polynomials

Cited by 13 publications

References 15 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

A Dynamic Skull Model for Simulation of Cerebral Cortex Folding

A few thoughts on brain ROIs

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