MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI

2024-07-03

arXiv.org

AI Summary

Existing methods for identifying neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) predominantly rely on local or single-view brain network features, neglecting joint modeling of higher-order functional dynamics and topological structure—thereby limiting diagnostic accuracy and clinical interpretability. To address this, we propose a multi-view hierarchical brain network generative framework (G-IBHN-G) coupled with a higher-order graph neural network (HGNN), which jointly models Euclidean-space features (local and higher-order functional connectivity) and non-Euclidean-space features (topological and higher-order structural organization). Integrated via feature-fusion classification (FFC), the framework enables end-to-end diagnosis. Evaluated on ABIDE, ADHD-200, and Peking datasets, our method significantly outperforms state-of-the-art approaches under both AAL1 and Brainnetome parcellation templates, demonstrating robustness across protocols. It precisely identifies NDD-associated critical brain regions, achieving substantial performance gains while preserving clinical interpretability.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Brain-Sensing and Analysis
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Brain Development Issues
• Deep Learning
• Diagnostic Accuracy

Innovation

• MHNet
• multidimensional brain network analysis
• improved accuracy for neurodevelopmental disorders