MHNet：用于利用静息态fMRI诊断神经发育障碍的多视图高阶网络
MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI
AI总结：
提出MHNet双分支网络，从rs-fMRI多视图脑功能网络的欧几里得与非欧几里得空间提取高阶特征，提升ASD、ADHD等NDD诊断性能。
摘要：
背景：深度学习模型在诊断ASD和ADHD等神经发育障碍（NDD）方面已展现出前景。然而，许多模型要么使用图神经网络（GNN）构建单层级脑功能网络（BFN），要么采用空间卷积滤波从rs-fMRI数据中提取局部信息，往往忽视了对NDD分类至关重要的高阶特征。方法：我们提出一种多视图高阶网络（MHNet），以从由rs-fMRI数据得到的多视图BFN中捕获层级和高阶特征，用于NDD预测。MHNet包含两个分支：欧几里得空间特征提取（ESFE）模块和非欧几里得空间特征提取（Non-ESFE）模块，随后是用于NDD识别的基于特征融合的分类（FFC）模块。ESFE包括功能连接生成（FCG）模块和高阶卷积神经网络（HCNN）模块，用于在欧几里得空间中从BFN提取局部和高阶特征。Non-ESFE由通用类互联网脑层级网络生成（G-IBHN-G）模块和高阶图神经网络（HGNN）模块组成，用于在非欧几里得空间中捕获拓扑和高阶特征。结果：在三个公开数据集上的实验表明，使用AAL1和Brainnetome Atlas模板时，MHNet均优于最先进方法。大量消融研究证实了MHNet的优越性，以及使用多视图fMRI信息和高阶特征的有效性。我们的研究还为构建更复杂的层级网络提供了图谱选择，并解释了关键脑区与NDD之间的关联。结论：MHNet利用来自欧几里得空间和非欧几里得空间的多视图特征学习，融合来自BFN的高阶信息，以提升NDD分类性能。
英文摘要：
Background: Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. Methods: We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction. MHNet has two branches: the Euclidean Space Features Extraction (ESFE) module and the Non-Euclidean Space Features Extraction (Non-ESFE) module, followed by a Feature Fusion-based Classification (FFC) module for NDD identification. ESFE includes a Functional Connectivity Generation (FCG) module and a High-order Convolutional Neural Network (HCNN) module to extract local and high-order features from BFNs in Euclidean space. Non-ESFE comprises a Generic Internet-like Brain Hierarchical Network Generation (G-IBHN-G) module and a High-order Graph Neural Network (HGNN) module to capture topological and high-order features in non-Euclidean space. Results: Experiments on three public datasets show that MHNet outperforms state-of-the-art methods using both AAL1 and Brainnetome Atlas templates. Extensive ablation studies confirm the superiority of MHNet and the effectiveness of using multi-view fMRI information and high-order features. Our study also offers atlas options for constructing more sophisticated hierarchical networks and explains the association between key brain regions and NDD. Conclusion: MHNet leverages multi-view feature learning from both Euclidean and non-Euclidean spaces, incorporating high-order information from BFNs to enhance NDD classification performance.
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