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Related Experiment Video

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A new method to predict anomaly in brain network based on graph deep learning.

Jalal Mirakhorli1, Hamidreza Amindavar1, Mojgan Mirakhorli2

  • 1Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.

Reviews in the Neurosciences
|July 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning framework using Variational Graph Autoencoders and Generative Adversarial Networks to analyze brain connectivity patterns in functional magnetic resonance imaging (fMRI) data, specifically for Alzheimer's disease research.

Keywords:
brain functiongenerative modelgraph theorymild cognitive impairment (MCI)neural plasticityposterior contraction

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Functional magnetic resonance imaging (fMRI) enables brain disorder studies by mapping brain connection topology (connectopic mapping).
  • Analyzing complex functional and structural brain networks is challenging due to subtle differences between healthy and unhealthy regions and increasing evaluation metrics.
  • Deep learning on irregular graphs offers a powerful approach for analyzing cognitive functions, gene expression, and spatial patterns in the brain.

Purpose of the Study:

  • To develop and apply a high-order graph analysis framework for identifying abnormal brain connections in functional imaging data.
  • To investigate the correlation between affected brain regions and their temporal co-occurrence, particularly in the context of Alzheimer's disease.
  • To explore the potential for diagnosing brain diseases and understanding brain plasticity through advanced network analysis.

Main Methods:

  • Utilized an individual generative model and high-order graph analysis for region of interest recognition.
  • Proposed a high-order framework of Variational Graph Autoencoder with a Gaussian distributer for analyzing functional brain imaging data.
  • Employed Generative Adversarial Network (GAN) to optimize the latent space for learning non-rigid graphs in large-scale datasets.

Main Results:

  • Successfully analyzed functional data in brain imaging studies, distinguishing modes of correlation in abnormal brain connections.
  • Identified abnormal brain connections during specific tasks and resting states.
  • Demonstrated the capability to decompose irregular observations and learn strong non-rigid graphs.

Conclusions:

  • The proposed Variational Graph Autoencoder framework with GAN optimization is effective for analyzing complex brain functional connectivity.
  • This approach can help diagnose brain diseases like Alzheimer's by identifying abnormal connectivity patterns.
  • The study highlights the potential for understanding brain plasticity and the nervous system's adaptability.