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MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI.

Yueyang Li1, Weiming Zeng2, Wenhao Dong1

  • 1Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, 201306, China.

Journal of Imaging Informatics in Medicine
|January 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-view High-order Network (MHNet) for improved diagnosis of neurodevelopmental disorders (NDD) by capturing complex brain network features. MHNet significantly enhances NDD classification accuracy using multi-view functional connectivity data.

Keywords:
Convolution neural networkEuclidean spaceGraph neural networkHigh-orderMulti-viewNeurodevelopmental disorderNon-Euclidean spacers-fMRI

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning models show potential for diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD.
  • Existing models often overlook high-order features in brain functional networks (BFNs) derived from rs-fMRI data, limiting diagnostic accuracy.
  • Graph neural networks (GNNs) and spatial convolution are commonly used but have limitations in capturing complex network hierarchies.

Purpose of the Study:

  • To introduce a novel Multi-view High-order Network (MHNet) for enhanced prediction of neurodevelopmental disorders (NDD).
  • To capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data.
  • To improve NDD classification by integrating features from both Euclidean and non-Euclidean spaces.

Main Methods:

  • Developed MHNet with two branches: Euclidean Space Features Extraction (ESFE) and Non-Euclidean Space Features Extraction (Non-ESFE).
  • ESFE utilizes Functional Connectivity Generation (FCG) and High-order Convolutional Neural Network (HCNN) modules.
  • Non-ESFE employs Generic Internet-like Brain Hierarchical Network Generation (G-IBHN-G) and High-order Graph Neural Network (HGNN) modules, followed by Feature Fusion-based Classification (FFC).

Main Results:

  • MHNet demonstrated superior performance compared to state-of-the-art methods on three public datasets using AAL1 and Brainnetome Atlas templates.
  • Ablation studies confirmed the effectiveness of multi-view fMRI information and high-order features in MHNet.
  • The study identified key brain regions associated with NDD and provided atlas options for network construction.

Conclusions:

  • MHNet effectively leverages multi-view feature learning from both Euclidean and non-Euclidean spaces.
  • Incorporating high-order information from BFNs significantly enhances NDD classification performance.
  • The proposed method offers a promising approach for improving the diagnosis of neurodevelopmental disorders.