Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Analysis with Overcomplete Spherical Wavelets
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Parametric Representation of Cortical Surface Folding Based on Polynomials
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
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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.