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Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational

Carlo Amodeo1, Igor Fortel1, Olusola Ajilore2

  • 1Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|July 15, 2024
PubMed
Summary

This study introduces a novel method to analyze brain connectivity by integrating structural and functional connectomics data. This approach improves the understanding of brain networks and patient sub-populations, like those with Alzheimer's disease (AD).

Keywords:
Brain networksDeep learningNeuroimaging

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Graph theory is crucial for modeling brain connectivity using structural connectome (DTI tractography) and functional connectome (resting-state fMRI).
  • Existing studies often analyze structural or functional connectomes separately, potentially missing complementary insights.
  • Integrating both connectome types can enhance our understanding of brain function and structure.

Purpose of the Study:

  • To develop a method that jointly analyzes structural and functional connectome data.
  • To create a unified low-dimensional embedding for cross-subject comparisons.
  • To improve the characterization of patient sub-populations using integrated connectome information.

Main Methods:

  • Proposed a function-constrained structural graph variational autoencoder (FCS-GVAE).
  • Employed an unsupervised learning approach to integrate functional and structural connectome data.
  • Utilized the OASIS-3 dataset, including Alzheimer's disease (AD) patients, for evaluation.

Main Results:

  • The FCS-GVAE successfully generated a joint low-dimensional embedding of brain connectivity.
  • A variational formulation was shown to be optimal for encoding functional brain dynamics.
  • The joint embedding approach demonstrated superior accuracy in distinguishing patient sub-populations compared to single-modality methods.

Conclusions:

  • Integrating functional and structural connectome data offers a more comprehensive understanding of brain networks.
  • The proposed FCS-GVAE provides a powerful tool for analyzing multimodal brain connectome data.
  • This method has significant potential for improving diagnostic and prognostic capabilities in neurological disorders like Alzheimer's disease.