Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI), yet early identification remains challenging due to the complex structural and functional alterations it induces in the brain. To address this, we propose a dynamic multimodal Mixture-of-Experts (MoE) framework that integrates functional and structural MRI through time-aware functional-structural encoding and class-conditioned expert routing. Within this framework, modality-specific and cross-modal experts learn complementary representations, while a Modality-Class MoE (MCoE) module dynamically dispatches expert weights according to each classification objective. Experimental results across three binary classification tasks demonstrate that the framework consistently outperforms static fusion baselines, and high-interpretability analyses further reveal meaningful region-of-interest (ROI) interactions. This dynamic multimodal expert framework effectively captures class-dependent brain interaction patterns and provides an interpretable approach for PTE diagnosis and risk stratification.

翻译：创伤后癫痫（PTE）是创伤性脑损伤（TBI）的一种严重并发症，然而由于TBI在大脑中诱发的复杂结构与功能改变，早期诊断仍面临挑战。为此，本文提出一种动态多模态混合专家（MoE）框架，通过时间感知的功能-结构编码与类别条件专家路由机制，整合功能与结构MRI数据。在该框架中，模态特定专家与跨模态专家学习互补表征，同时一个模态-类别MoE（MCoE）模块根据每个分类目标动态分配专家权重。在三个二分类任务上的实验结果表明，该框架始终优于静态融合基线方法，且高可解释性分析进一步揭示了有意义的感兴趣区（ROI）交互作用。该动态多模态专家框架有效捕捉了类别依赖的脑交互模式，为PTE诊断与风险分层提供了可解释的解决方案。