提出新框架BrainMAP，挖掘大脑功能网络中的多激活路径。
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
- 用序列模型捕捉脑区间的长程相关性
- 通过专家混合聚合模块学习多条激活路径
- 可解释关键脑区作用，适合神经科学与AI交叉研究者
功能性磁共振成像（fMRI）常用于研究人脑活动，揭示功能波动与行为之间的关联。为提升脑活动分析能力，图神经网络（GNN）被广泛应用于从fMRI数据中提取的功能连接（FC）分析，因其能捕捉脑区间的协同作用。然而，在执行复杂任务时，人脑通常会激活特定的路径，这些路径可表示为图上的路径。传统GNN因难以处理多路径的长距离依赖而表现受限。为此，我们提出新型框架BrainMAP，用于学习脑网络中的多激活路径。BrainMAP利用序列模型识别序列化脑区间的长程相关性，并引入基于混合专家（Mixture of Experts, MoE）的聚合模块，实现对多路径的学习。全面实验表明，BrainMAP性能显著优于现有方法。此外，该框架支持对任务相关关键脑区的可解释性分析。代码已开源：https://github.com/LzyFischer/Graph-Mamba。
原文摘要 · Abstract (English)
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and human behavior. To enhance analysis and comprehension of brain activity, Graph Neural Networks (GNNs) have been widely applied to the analysis of functional connectivities (FC) derived from fMRI data, due to their ability to capture the synergistic interactions among brain regions. However, in the human brain, performing complex tasks typically involves the activation of certain pathways, which could be represented as paths across graphs. As such, conventional GNNs struggle to learn from these pathways due to the long-range dependencies of multiple pathways. To address these challenges, we introduce a novel framework BrainMAP to learn Multiple Activation Pathways in Brain networks. BrainMAP leverages sequential models to identify long-range correlations among sequentialized brain regions and incorporates an aggregation module based on Mixture of Experts (MoE) to learn from multiple pathways. Our comprehensive experiments highlight BrainMAP's superior performance. Furthermore, our framework enables explanatory analyses of crucial brain regions involved in tasks. Our code is provided at https://github.com/LzyFischer/Graph-Mamba.
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