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Task-radMBNet: An Improved Task-Driven Dynamic Graph Sparsity Pattern Radiomics-Based Morphological Brain Network for

Limei Song1, Zhiwei Song2, Pengzhi Nan2

  • 1School of Medical Imaging, Shandong Second Medical University, Weifang, China.

Brain Connectivity
|April 8, 2025
PubMed
Summary

This study introduces Task-radMBNet, a novel model for Alzheimer's disease (AD) diagnosis using dynamic adaptive graph sparsity. The model enhances diagnostic accuracy by focusing on key brain regions and connections, showing significant promise for neurological disorder detection.

Keywords:
Alzheimer’s diseasegraph sparsityradiomics featuretask-driven

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) analysis benefits from dynamic adaptive graph sparsity for improved focus and sensitivity.
  • Radiomics-based morphological brain networks (radMBN) offer a framework for analyzing brain structure.

Purpose of the Study:

  • To introduce a task-driven dynamic adaptive graph sparsity model (Task-radMBNet) for enhanced AD diagnosis.
  • To integrate connectivity and radiomics features within a dual-channel graph convolutional network (GCN) framework.
  • To improve the accuracy of early AD detection using advanced neuroimaging analysis.

Main Methods:

  • Developed Task-radMBNet, incorporating a connectivity-GCN channel and a radiomics-GCN channel sharing a dynamic sparse brain network.
  • The connectivity-GCN channel dynamically learns optimal sparse topology for the task.
  • The radiomics-GCN channel integrates radiomics node features with dynamic topology for diagnostic enhancement.

Main Results:

  • Task-radMBNet achieved superior classification accuracy: 87.8% and 86.0% for early AD diagnosis.
  • Evaluated on 1273 subjects across AD Neuroimaging Initiative and European DTI Study on Dementia databases.
  • Visualizations demonstrated topology heatmaps and important connectivity under varying sparsity settings.

Conclusions:

  • Task-radMBNet shows significant promise for diagnosing neurological disorders, particularly Alzheimer's disease.
  • The integration of Task-radMBNet with radMBN offers a powerful approach for neuroimaging analysis.
  • Dynamic adaptive graph sparsity is a key factor in improving diagnostic sensitivity and accuracy.