标题： 基于多通道变换器的脑部疾病诊断中的有符号高阶交互 显示英文标题

标题： Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers

摘要： 准确表征脑区的高阶交互作用并从功能性磁共振成像数据中提取可解释的组织模式对于脑疾病诊断至关重要。当前基于图的深度学习模型主要关注成对或三元模式，而忽略了有符号的高阶交互作用，限制了对全脑通信的全面理解。我们提出了HOI-Brain，一种新的计算框架，利用功能性磁共振成像数据中的有符号高阶交互作用和组织模式进行脑疾病诊断。首先，我们引入了一种基于时间导数乘法的共波动度量，以检测具有时间分辨率的高阶交互作用。然后，我们区分正负协同交互作用，并将它们编码在有符号加权单纯复形中，以揭示脑通信的见解。使用持久同调理论，我们对这些复形应用两种过滤过程，以时空方式提取有符号的高维神经组织。最后，我们提出一个多通道脑Transformer来整合异构拓扑特征。在阿尔茨海默病、帕金森综合征和自闭症谱系障碍数据集上的实验表明了我们框架的优势、有效性和可解释性。识别出的关键脑区和高阶模式与神经科学文献一致，提供了有意义的生物学见解。 显示英文摘要

摘要： Accurately characterizing higher-order interactions of brain regions and extracting interpretable organizational patterns from Functional Magnetic Resonance Imaging data is crucial for brain disease diagnosis. Current graph-based deep learning models primarily focus on pairwise or triadic patterns while neglecting signed higher-order interactions, limiting comprehensive understanding of brain-wide communication. We propose HOI-Brain, a novel computational framework leveraging signed higher-order interactions and organizational patterns in fMRI data for brain disease diagnosis. First, we introduce a co-fluctuation measure based on Multiplication of Temporal Derivatives to detect higher-order interactions with temporal resolution. We then distinguish positive and negative synergistic interactions, encoding them in signed weighted simplicial complexes to reveal brain communication insights. Using Persistent Homology theory, we apply two filtration processes to these complexes to extract signed higher-dimensional neural organizations spatiotemporally. Finally, we propose a multi-channel brain Transformer to integrate heterogeneous topological features. Experiments on Alzheimer' s disease, Parkinson' s syndrome, and autism spectrum disorder datasets demonstrate our framework' s superiority, effectiveness, and interpretability. The identified key brain regions and higher-order patterns align with neuroscience literature, providing meaningful biological insights.