Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional sub-networks to construct sub-network associations. However, we identified many cross-network interaction patterns with high Pearson correlations that this strict, prior-based organization fails to capture. To overcome this limitation, we propose the Brain Hierarchical Organization Learning (BrainHO) to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels. Specifically, we design a hierarchical attention mechanism that allows the model to aggregate nodes into a hierarchical organization, effectively capturing intricate connectivity patterns at the subgraph level. To ensure diverse, complementary, and stable organizations, we incorporate an orthogonality constraint loss, alongside a hierarchical consistency constraint strategy, to refine node-level features using high-level graph semantics. Extensive experiments on the publicly available ABIDE and REST-meta-MDD datasets demonstrate that BrainHO not only achieves state-of-the-art classification performance but also uncovers interpretable, clinically significant biomarkers by precisely localizing disease-related sub-networks.

翻译：基于功能性磁共振成像（fMRI）的脑网络分析对于诊断脑部疾病至关重要。现有方法通常依赖于预定义的功能子网络来构建子网络关联。然而，我们发现了许多具有高皮尔逊相关性的跨网络交互模式，而这种严格的、基于先验知识的组织结构无法捕捉到这些模式。为了克服这一局限，我们提出了脑层次组织学习（BrainHO）方法，以基于脑网络的内在特征而非预定义的子网络标签，学习其固有的层次化脑网络依赖关系。具体而言，我们设计了一种层次化注意力机制，使模型能够将节点聚合成一个层次化组织结构，从而在子图级别有效捕捉复杂的连接模式。为了确保组织结构的多样性、互补性和稳定性，我们引入了一个正交性约束损失，并结合层次一致性约束策略，利用高层图语义来细化节点级特征。在公开可用的ABIDE和REST-meta-MDD数据集上进行的大量实验表明，BrainHO不仅实现了最先进的分类性能，而且通过精确定位与疾病相关的子网络，发现了可解释的、具有临床意义的生物标志物。