Li Yueyang, Zeng Weiming, Dong Wenhao, Cai Luhui, Wang Lei, Chen Hongyu, Yan Hongjie, Bian Lingbin, Wang Nizhuan
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, 201306, China.
Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, 222002, China.
J Imaging Inform Med. 2025 Jan 28. doi: 10.1007/s10278-025-01399-5.
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. 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. 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. MHNet leverages multi-view feature learning from both Euclidean and non-Euclidean spaces, incorporating high-order information from BFNs to enhance NDD classification performance.
深度学习模型在诊断自闭症谱系障碍（ASD）和注意力缺陷多动障碍（ADHD）等神经发育障碍（NDD）方面已显示出前景。然而，许多模型要么使用图神经网络（GNN）来构建单级脑功能网络（BFN），要么采用空间卷积滤波从静息态功能磁共振成像（rs-fMRI）数据中提取局部信息，常常忽略了对NDD分类至关重要的高阶特征。我们引入了一种多视图高阶网络（MHNet），以从rs-fMRI数据导出的多视图BFN中捕获分层和高阶特征，用于NDD预测。MHNet有两个分支：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包括一个功能连接生成（FCG）模块和一个高阶卷积神经网络（HCNN）模块，用于从欧几里得空间中的BFN中提取局部和高阶特征。Non-ESFE包括一个类通用互联网脑分层网络生成（G-IBHN-G）模块和一个高阶图神经网络（HGNN）模块，用于捕获非欧几里得空间中的拓扑和高阶特征。在三个公共数据集上进行的实验表明，MHNet在使用AAL1和脑网络组图谱模板时均优于现有方法。广泛的消融研究证实了MHNet的优越性以及使用多视图功能磁共振成像信息和高阶特征的有效性。我们的研究还为构建更复杂的分层网络提供了图谱选项，并解释了关键脑区与NDD之间的关联。MHNet利用从欧几里得和非欧几里得空间进行的多视图特征学习，结合BFN的高阶信息来提高NDD分类性能。
