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MM-TransUNet: Multi-modal and multi-graph feature learning for neurodevelopmental disorder diagnosis.

Weilun Wu1, Huan Wang1, Shen Wu1

  • 1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 17, 2026
PubMed
Summary

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This study introduces MM-TransUNet, a novel framework for diagnosing neurodevelopmental disorders using multi-modal brain network data. It enhances diagnostic accuracy and generalizability by effectively handling feature heterogeneity and complex graph structures.

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Graph learning enhances brain network analysis for neurodevelopmental disorder diagnosis.
  • Existing methods face challenges with multi-modal feature heterogeneity and complex graph structures, limiting performance.

Purpose of the Study:

  • To propose MM-TransUNet, a multi-modal and multi-graph feature learning framework.
  • To improve representation capability and generalizability in neurodevelopmental disorder diagnosis.

Main Methods:

  • Developed the Graph Token Statistics Transformer (GTST) for global structural features and topology encoding.
  • Designed a TransUNet encoder integrating GTST with Graph UNet for multi-scale feature learning.
  • Implemented a distribution-aware dynamic fusion (DADF) module for adaptive multi-modal feature fusion.
Keywords:
Feature learningGraph learningMulti-graphMulti-modalNeurodevelopmental disorders

Related Experiment Videos

Main Results:

  • MM-TransUNet demonstrated superior diagnostic accuracy on ABIDE and ADHD-200 datasets.
  • The framework achieved strong generalization across different subject cohorts.
  • Outperformed existing state-of-the-art methods in neurodevelopmental disorder diagnosis.

Conclusions:

  • MM-TransUNet effectively addresses multi-modal feature heterogeneity and complex graph structures.
  • The proposed framework offers a robust and generalizable solution for neurodevelopmental disorder diagnosis.
  • Code is available at https://github.com/Roninddd/MM-TransUNet.