Poisson Flow Model of Cortical Folding Pattern
Moo K. Chung, Luigi Maccotta, Aaron Struck
TLDR
Introduces a Poisson flow model to characterize cortical folding patterns, offering a new way to study subtle brain abnormalities in JME.
Key contributions
- Introduces a Poisson flow model for analyzing cortical folding patterns.
- Derives a smooth scalar field from mean curvature gradients via a Poisson equation.
- Defines a flow representation for spatially coherent sulcal-gyral organization.
- Offers a principled geometric framework for studying brain alterations in JME.
Why it matters
Conventional morphometric measures struggle with subtle, distributed brain abnormalities in conditions like JME. This model provides a more sensitive and spatially coherent method to characterize cortical folding, offering a new tool for understanding neurological diseases.
Original Abstract
Cortical folding reflects coordinated neurodevelopmental processes and provides a sensitive marker of neurological disease. In juvenile myoclonic epilepsy (JME), structural abnormalities are subtle and spatially distributed, limiting the sensitivity of conventional morphometric measures such as cortical thickness. We introduce a Poisson flow model derived from gradients of the mean curvature field on the cortical surface. The method yields a smooth scalar field obtained from a Poisson equation, whose surface gradient defines a flow representation of folding organization. This representation enables spatially coherent characterization of sulcal--gyral patterns and provides a principled geometric framework for studying distributed cortical alterations in JME.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.