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.

翻译：准确刻画脑区的高阶交互并从功能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，限制了对全脑通信的全面理解。我们提出HOI-Brain，一种利用fMRI数据中有符号高阶交互与组织模式进行脑疾病诊断的新型计算框架。首先，我们引入基于时间导数乘积的共波动度量，以检测具有时间分辨率的高阶交互。随后，我们区分正负协同交互，将其编码为带符号加权单纯复形以揭示脑通信机制。运用持续同调理论，我们对这些复形实施两种过滤过程，从而在时空维度上提取有符号的高维神经组织。最后，我们提出一种多通道脑Transformer以整合异质拓扑特征。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证明了本框架的优越性、有效性及可解释性。所识别的关键脑区与高阶模式与神经科学文献相符，提供了有意义的生物学见解。