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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Predicting brain structural network using functional connectivity.

Lu Zhang1, Li Wang2, Dajiang Zhu1

  • 1Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA.

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|May 1, 2022
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Summary
This summary is machine-generated.

This study introduces a novel deep learning model, MGCN-GAN, to predict individual brain structural connectivity (SC) from functional connectivity (FC). The model accurately infers brain networks, revealing common regulatory patterns between structure and function.

Keywords:
Brain connectivityGenerative adversarial networkGraph convolution networksStructure-function relationship

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Understanding the brain's structure-function relationship is crucial for neuroscience.
  • Inferring individual brain structural connectivity (SC) from functional connectivity (FC) is complex due to heterogeneous patterns and individual variations.
  • Deep learning models like generative adversarial networks (GANs) and graph convolutional networks (GCNs) offer new avenues for analyzing complex brain networks.

Purpose of the Study:

  • To develop a novel deep learning framework, multi-GCN based GAN (MGCN-GAN), for inferring individual brain structural connectivity (SC) from functional connectivity (FC).
  • To automatically learn the intricate associations between individual brain structural and functional networks.
  • To improve the reliability and accuracy of predicting SC from FC.

Main Methods:

  • Proposed a multi-GCN based GAN (MGCN-GAN) model.
  • The generator utilizes multiple multi-layer GCNs to model complex indirect connections.
  • The discriminator employs a single multi-layer GCN to differentiate predicted from real SC.
  • Introduced a structure-preserving (SP) loss function to stabilize GAN training and enhance SC pattern learning.

Main Results:

  • The MGCN-GAN model successfully generated reliable individual SC from FC using the Human Connectome Project (HCP) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.
  • The model demonstrated the capability to automatically learn complex associations between structural and functional brain networks.
  • Validation on independent datasets confirmed the model's robustness and accuracy.

Conclusions:

  • The developed MGCN-GAN model provides a powerful tool for inferring individual brain structural connectivity from functional connectivity.
  • The findings suggest a potential common regulatory mechanism linking specific brain structural and functional architectures across individuals.
  • This work advances our understanding of brain organization and opens new possibilities for neuroimaging analysis.