ArticleMedical image analysis2018
A cortical shape-adaptive approach to local gyrification index.
Article in Medical image analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- Biophysical modeling of anatomically realistic prenatal cortical folding development.Research square · 2026Article
- Long-term effects of preterm birth on cortical folding trajectories in early childhood.Brain communications · 2026Article
- Local gyrification index and sulcal depth as imaging markers of cognitive decline in Alzheimer's disease.Frontiers in aging neuroscience · 2025Article
- Longitudinal Assessment of Abnormal Cortical Folding in Fetuses and Neonates With Isolated Non-Severe Ventriculomegaly.Brain and behavior · 2025Article
- Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.PloS one · 2025Article
- Curvature analysis of perisylvian epilepsy.Acta neurologica Belgica · 2023Article
- Mechanical hierarchy in the formation and modulation of cortical folding patterns.Scientific reports · 2023Article
- Comprehensive shape analysis of the cortex in Huntington's disease.Human brain mapping · 2023Article
- Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection.Computers in biology and medicine · 2023Article
- A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping.Scientific data · 2022Article
- Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Morphometric Analysis of Brain in Newborn with Congenital Diaphragmatic Hernia.Brain sciences · 2021Article
- Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training.NeuroImage · 2021Article
- A gyrification analysis approach based on Laplace Beltrami eigenfunction level sets.NeuroImage · 2021Article
- Validation of Group-wise Registration for Surface-based Functional MRI Analysis.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder.Frontiers in neuroscience · 2021Article
- Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Joint cortical surface and structural connectivity analysis of Alzheimer's Disease.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Article
- Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis.IEEE transactions on medical imaging · 2020Article
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5 authors.
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Abstract
The amount of cortical folding, or gyrification, is typically measured within local cortical regions covered by an equidistant geodesic or nearest neighborhood-ring kernel. However, without careful design, such a kernel can easily cover multiple sulcal and gyral regions that may not be functionally related. Furthermore, this can result in smoothing out details of cortical folding, which consequently blurs local gyrification measurements. In this paper, we propose a novel kernel shape to locally quantify cortical gyrification within sulcal and gyral regions. We adapt wavefront propagation to generate a spatially varying kernel shape that encodes cortical folding patterns: neighboring gyral crowns, sulcal fundi, and sulcal banks. For this purpose, we perform anisotropic wavefront propagation that runs fast along gyral crowns and sulcal fundi by solving a static Hamilton-Jacobi partial differential equation. The resulting kernel adaptively elongates along gyral crowns and sulcal fundi, while keeping a uniform shape over flat regions like sulcal banks. We then measure local gyrification within the proposed spatially varying kernel. The experimental results show that the proposed kernel-based gyrification measure achieves a higher reproducibility than the conventional method in a multi-scan dataset. We further apply the proposed kernel to a brain development study in the early postnatal phase from neonate to 2 years of age. In this study we find that our kernel yields both positive and negative associations of gyrification with age, whereas the conventional method only captures positive associations. In general, our method yields sharper and more detailed statistical maps that associate cortical folding with sex and gestational age.
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