用几何流建模大脑沟回结构，揭示青少年肌阵挛癫痫的细微异常
Geometry-Driven Flow Analysis of Brain Sulcal Pattern
- 基于泊松方程构建曲面几何驱动的折叠流模型
- 在青少年肌阵挛癫痫中发现空间连贯的折叠异常模式
- 适合研究脑发育与神经疾病影像分析的学者
皮质折叠反映协同的神经发育过程，日益被视为神经疾病敏感标志。然而，现有分析多依赖间接的标量汇总，未显式建模折叠几何本身。在青少年肌阵挛癫痫（JME）中，皮质异常常表现细微、空间分散，传统形态测量难以检出。本文提出基于泊松方程的框架，将皮质折叠视为由皮质流形上平均曲率导出的几何驱动流。通过将折叠模式视为稳态源-汇结构，该方法生成平滑、全局平衡的势场，其表面梯度定义了具有物理可解释性的通量。该框架实现了沟回折叠组织的空间一致性分析，为JME中几何驱动的皮质结构提供了原则性表征。
原文摘要 · Abstract (English)
Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However, most existing analyses rely on indirect scalar summaries that do not explicitly model folding geometry itself. In juvenile myoclonic epilepsy (JME), a common genetic epilepsy, cortical abnormalities are often subtle, spatially distributed, and difficult to detect using conventional morphometric measures. We introduce a Poisson-equation-based framework that models cortical folding as a geometry-driven flow derived from mean curvature on the cortical manifold. By treating folding patterns as a stationary source-sink structure, the proposed approach yields a smooth, globally balanced potential field whose surface gradient defines a physically interpretable flux. This framework enables spatially coherent analysis of sulcal-gyral folding organization and provides a principled representation of geometry-driven cortical structure in JME.
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