Poisson Flow Model of Cortical Folding Pattern

A team of researchers led by Moo K. Chung has introduced a novel computational geometry method, the Poisson Flow Model, to analyze the complex folding patterns of the human cerebral cortex. Published in IEEE EMBC 2026, the model addresses a key challenge in neuroimaging: detecting subtle, spatially distributed structural abnormalities in neurological disorders. The method starts with the mean curvature field of the cortical surface and solves a Poisson equation to derive a smooth scalar field. The surface gradient of this field defines a 'flow' representation that captures the organized geometry of sulci (grooves) and gyri (ridges) in a mathematically principled way.

This new representation is specifically applied to study juvenile myoclonic epilepsy (JME), a condition where traditional morphometric measures like cortical thickness lack sensitivity. The Poisson Flow Model's strength lies in its ability to provide a spatially coherent characterization of folding patterns across the entire cortical surface. This allows researchers to move beyond localized measurements and model how neurodevelopmental processes coordinate to shape the brain's landscape. The framework offers a more sensitive tool for identifying the distributed cortical alterations associated with JME, potentially leading to better biomarkers for diagnosis and understanding of disease progression.