用先验知识指导脑网络子区域交互学习，提升精神疾病诊断效果
Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
- 引入语义先验控制注意力机制，明确子网络间功能交互路径
- 通过病理一致性约束，使学习结果符合临床已知规律
- 在多种精神疾病诊断任务中表现领先，且识别出可解释生物标志物
建模功能子网络间的复杂交互对精神障碍诊断和功能通路识别至关重要。然而，现有基于Transformer的方法因训练样本有限，难以有效学习潜在子网络的交互关系。为此，我们提出KD-Brain——一种融合先验知识的图学习框架，显式引导学习过程。具体而言，设计语义条件化交互机制，将语义先验注入注意力查询，基于子网络的功能身份显式导航交互路径；同时引入病理一致性约束，通过与临床先验对齐学习到的交互分布来正则化模型优化。KD-Brain在多种精神障碍诊断任务中达到当前最优性能，并识别出与精神病理生理一致的可解释生物标志物。代码已公开于https://anonymous.4open.science/r/KDBrain。
原文摘要 · Abstract (English)
Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the interactions of the underlying subnetworks remains a significant challenge for existing Transformer-based methods due to the limited number of training samples. To address these challenges, we propose KD-Brain, a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process. Specifically, we design a Semantic-Conditioned Interaction mechanism that injects semantic priors into the attention query, explicitly navigating the subnetwork interactions based on their functional identities. Furthermore, we introduce a Pathology-Consistent Constraint, which regularizes the model optimization by aligning the learned interaction distributions with clinical priors. Additionally, KD-Brain leads to state-of-the-art performance on a wide range of disorder diagnosis tasks and identifies interpretable biomarkers consistent with psychiatric pathophysiology. Our code is available at https://anonymous.4open.science/r/KDBrain.
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