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Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes.

Yanfu Zhang1, Liang Zhan1, Shandong Wu2

  • 1Department of Electrical and Computer Engineering, University of Pittsburgh,Pittsburgh, PA 15260, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 22, 2022
PubMed
Summary

This study introduces a novel method to create unified brain network representations from multiple tractography views. The approach ensures fair and disentangled information, improving downstream analysis in brain research.

Keywords:
Alzheimer’s DiseaseBrain ConnectomeMulti-viewPrediction

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Diffusion MRI is crucial for brain structural connectome analysis.
  • Tractography algorithms create brain networks, but results vary, complicating downstream analysis.
  • A unified representation is needed to overcome multi-view dependency.

Purpose of the Study:

  • To develop a method for learning a unified representation from multi-view brain networks.
  • To ensure learned representations are fair (proportional) and disentangled.
  • To improve the reliability and applicability of brain network analysis.

Main Methods:

  • Utilized unsupervised variational graph auto-encoders for disentangled representation learning.
  • Implemented an alternative training routine based on network flow analogy for view-wise fairness (proportionality).
  • Developed a network scheduling algorithm aware of proportionality for fair representation learning.

Main Results:

  • Learned representations effectively fit various downstream neuroimaging analysis tasks.
  • The proposed approach successfully preserves proportionality across different network views.
  • Demonstrated the utility of unified, fair, and disentangled brain network representations.

Conclusions:

  • The proposed method offers a robust way to integrate multi-view brain network data.
  • Fair and disentangled representations enhance the performance and consistency of downstream analyses.
  • This work advances the field of neuroimaging by providing a more reliable approach to brain network modeling.