Multivariate High-Dimensional Cortical Folding Analysis, Combining Complexity and Shape, in Neonates with Congenital Heart Disea

The paper presents a novel statistical framework for cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial-normalization-based approaches stemming from the severe deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding complexity or shape by a single number, the proposed descriptor unifies complexity and shape of the surface in a high-dimensional space. In this way, the proposed framework couples the reliability of ROIbased analysis with the richness of the novel cortical folding pattern descriptor. Furthermore, the descriptor can easily incorporate additional variables, e.g. cortical thickness. The paper proposes a novel application of a nonparametric permutation-based approach for statistical hypothesis testing for any multivariate high-dimensional descriptor. While the proposed ...

• Type: Conference
• Year: 2009
• Where: IPMI
• Authors: Suyash P. Awate, Paul A. Yushkevich, Zhuang Song, Daniel J. Licht, James C. Gee
• Keywords: Cortical Folding Pattern, Descriptor Unifies Complexity, Folding Pattern Descriptor, IPMI 2009, Medical Imaging